Application of Digital Innovation

#### **Chapter 8**

## Perspective Chapter: Data Monetization Model for Sustaining Smart City Initiatives

*Mercy Samuel and Siddharth Gupta*

#### **Abstract**

This chapter aims to create a G2B business model framework for data monetization under Smart City Mission (SCM) in India. It sets a premise to understand the urgency of a data monetizing model as a revenue option for sustaining the smart city initiatives. The sustainability of smart city makes it inevitable to look for a separate revenue stream for cities to fund the operations & maintenance of smart city infrastructure created under SCM. The chapter explores few attempts made by other ministries in India towards data monetization and the role of data privacy and sharing policy for protection from data exploitation. The first approach to the research includes analysis of cases of other smart cities and data monetization initiatives around the world. Further the paper also explores the kind of data generated under Smart City Mission India to understand the possibility of monetization and its value for different stakeholders. The limitation of this study is that data monetization has not yet been rigorously tested in practice in the government sector. Hence the current study attempts to explore the potential of the same as a financing option for sustaining smart city initiatives in India.

**Keywords:** data monetization, data exchange, smart cities, internet of things, self-financing cities, business model, data marketplace, government-to-business, urban data exchange, business model canvas

#### **1. Introduction**

Smart Cities Mission is the Government of India's urban regeneration and retrofitting project launched in 2015 to create livable and sustainable smart cities in India [1]. The Indian government through a competitive process selected 100 cities to be developed as smart cities in multiple rounds. The cities were required to submit their proposals in two categories viz. pan city and area-specific proposals. Different cities chose different services to plan and prepare their proposals with the help of consultants and participated in the competitive process. Finally, 100 cities were selected to develop themselves based on the proposal submitted. The government of India dedicated a whopping US\$ 6.3 billion for smart city development. Most of the smart city projects revolved around applying smart solutions to the infrastructure, mixed land use in area-based growth, improving housing opportunities for everyone, defining

walkable areas, maintaining and enhancing open spaces, supporting a range of transit choices, etc. There was extensive tech deployment in the smart city project and technology was identified as an enabler for city management. Tech deployment generated data that could be converted to information for better planning and strategizing.

A total of 5151 ventures worth some 2,05,018 crore is to be completed in 5 years from the respective selection date of the cities under the scheme under the Smart Cities Programme, which was initiated in 2015 [2].

As of March 2019, under the Smart Cities Mission, integrated command and control centers (ICCCs) estimated at Rs 27.7 billion were made operational in 15 towns [3]. The ICCC identified as one of the projects in many cities were designed as the "nerve center" for operations management, day-to-day exception handling and disaster management. In addition, Rs 22.6 billion schemes are under development in 31 towns. The government has also initiated tenders in over 18 cities for projects worth Rs 25.5 billion [4].

The idea for a Smart City must provide funding strategy for the Project's full development cycle. The financial strategy should define internal (taxes, leases, permits, and usage fees) and external (grants, delegated sales, loans, and borrowings) streams of capital expenditure allocation and activity and maintenance over the project's life cycle. This financial strategy would include outlets for recovery of project expenses for a span of 8–10 years or more. O&M expenses which would also provide ULB's financial planning, capital management action plan [5].

When communities are planning to update their facilities with new technology, the large-scale deployment of advanced technologies raises a huge obstacle to compensate for such ventures. Cities are limited by small budgets and need to define market models that can help draw private investment to render and sustain the transition financially.

It is imperative for Smart Cities to identify a revenue generation model to specifically take care of all the tech devices engaged in data capture among other expenses Smart cities will have to continually search for ways to exploit the development infrastructure and network to create new revenue sources for smart cities. Throughout growing layer of the company model, suppliers and collaborators will consider innovative ways to produce income from the products and services they offer. Such opportunities could mimic the business models used in the digital economy as a whole.

It is particularly essential for India, as cities are trying to automate their delivery of public infrastructure by introducing interconnected Pan City Smart Technologies under the Smart Cities Project by producing huge volumes of data. Meeting digital infrastructure's operating and maintenance costs, measured at 15–20 percent of capital annually, would be a challenge unless the cities think of innovative mechanisms to generate revenue monetize city information [6].

The Economic Survey (2018–2019) proposed that the government intends to monetize the data of residents as part of a wider strategy to use data as a public good. There is no justification to prohibit the private usage of this data for benefit, in keeping with the notion of data as a public good. Although the social gains will well outweigh the government's expense, at least some of the data produced would be monetized to alleviate the burden on government finances. Datasets may be provided to analytics firms that analyze the results, produce insights and offer insights back to the private sector, who in effect will use these insights to forecast demand, find untapped opportunities or develop new goods [7].

Today data is called the "new oil" as it could prove to be a valuable resource. These days companies are using data analytics to grow and optimize business, by understanding customer patterns, which in long run adds value and helps to foresee the modification necessary to be made that can easily outrun their competitors, therefore

#### *Perspective Chapter: Data Monetization Model for Sustaining Smart City Initiatives DOI: http://dx.doi.org/10.5772/intechopen.108345*

data becomes the game changer in competition. This has led to the monetization of data by companies from B2B (business-to-business) that could be used to optimize self-processes, design strategy, and could be sold externally. Comparing to this monetization in private sector, data from G2B (government to business) is still in nascent scale across globe. This gives an opportunity to tap underlying potential of tremendous amount of data bank with the government which could be monetized and can generate revenue streams by G2B (Government to Business) model.

Selling the data or analysis derived from it, have been studied scarcely in business and academic literature. In academic literature, the phenomenon of big data and the utilization of it have been popular topics over the last few years. The interest in the subject of data monetization has also increased due to the rise of digitization and big data. The principle of monetization, providing new value and data earnings, is not new and yet the practice has not been thoroughly studied.

Changing the market landscape, on the other side, provides different data use opportunities, data monetization being one possibility for broader data use. This has created a research void, as businesses are increasingly using the data in new ways, while the academic literature does not cover offers extracted from such new data. The result of this research provides a probable business model framework of this new emerging data marketplace. Due to limited availability of data and to get a head start, the business model canvas by Alexander Osterwalder, a business theorist with works on business model is used.

#### **2. Background**

Data no longer acts as a secondary asset in the current world used for decision making or processing but is now taken the front stage where it in itself to could be productized to sell and use. This new role is referred to as "Data Monetization". But at the same time, various factors and risks are involved when a new product is ever launched in the market. First is overcoming the reluctance over its transparency when related to data sharing on different platforms and second is a platform where it could be safely stored to be shred, processed, or moderated with no harm to data as well data source owner [8]. At the same time, it is important to understand that data is not only about incorporating information technology or providing business intelligence.

Data in itself has formed an economy in which the extraction of data from IoT devices has given thrust. With the advancement in IoT, data is also exponentially growing. With the growth in data, its monetization would become more real. But there has to be certain standardization when it comes to monetization. For example, devices such as Wi-Fi, GPS, beacon, would produce data related to location or region and say sold to a cab service [9].

For example, the healthcare industry is a sector that has immense potential to generate revenue streams by monetizing their health care data. As a seller, pharmaceutical organizations could be seen as a strong customer base to buy the data from medical labs where the disease insights will be gained. Data on chronic diseases could be shared with healthcare partners or retail players producing health equipment or health food product. As a buyer, health insurers could provide data on the claims to the healthcare providers to improve their services and minimize their financial risk. Thus, data needs to be exploited at its fullest not only for revenue but to improve service delivery [10]. In developing countries, the responsibility to collect Health data regarding population, prevalent diseases, lies with common health workers (CHW). But the lack of incentives for these CHWs leads to challenges in the collection and aggregation of data. The most common hurdle is the technological challenges, i.e., difficulty in usage of computer or internet facility. Secondly, maintaining a similar pattern while collecting data, especially over larger nations where the accumulation of data in itself would be a challenge. Thus, overcoming these hurdles would be the first step towards making data monetization in healthcare possible [11].

It is not as simple a job to extract data and supply, but involves a team of participants who would create an entire data ecosystem through understanding both dynamics as well as structure. These participants include data manager, suppliers, custodian, aggregator, developer of the application, and a service provider. Setting the pricing, IP protection, and privacy concerns are the most highlighted concerns in data monetization [12].

A revenue model is what comes next after the formulation of the business model where data-driven service is sold by the start-ups to the consumer through four distinct business models. Subscription model, Usage fee, and gain sharing are the most common type that is generally taken up by individuals, small enterprise, and private office through limited access. The multi-sided model incorporates any previous three models with beneficial addition of extra data or helps to create revenue from one model to another [13].

Data not only serve as a revenue stream but the open data concept has created a lot of transparency in the system of people and government. Government data available on the public domain helps to define services that are offered. The datasets released by the government highlights the progress of the projects, census data, etc. which are all the core of any ecosystem or industry [14]. A suitable example is the monetization of the vehicle data ways by the government of India from the Ministry of Road Transport and Highways (MoRTH). The ministry has sold vehicle data and registration records to the various private and government entities worth more than 50 crores till date in duration 2014–2019. The buyers comprise of 30 public and private sector banks, 20 logistics solution providers finance organizations 18 insurance organizations, and 5 automobile manufacturers. Only the non-personal data of the vehicles for share and a total of more than 20 indicators of 50 crore registrations have been sold. Source.

The third case from the Indian urban data exchange with established under Smart City mission. The first urban data exchange of an Indian city is established by Pune Smart City also known as Pune Urban Data Exchange (PUDX) which has 850 data sets available on the detection website.

This is the initiative from the Ministry of Housing and Urban Affairs (MoHUA) and Indian Urban Data Exchange (IUDX), and Pune Smart City. This is to help citizens, academic institutions, entrepreneurs, government, industry, and cities. In their first stage, they have connected the Smart City administration, police, cab operators, cellular service provider, safety data aggregator, safety application provider, citizen mobile apps. In their recent initiative, they have launched the women safety application and this platform has integrated the data from the street lights, police, traffic, geographical location to serve better to the citizens in need.

Also, there are few bigger players in the industry in such as Amazon Web services (AWS), Dawex, Quandl, and Centre for Monitoring Indian Economy (CMIE). These companies are big players in the data exchange business for quite a long time and each has its own a diverse database but each operates on a distinct model.

Among these few companies have been compared and used as a benchmark to formulate the business model further in research.

#### **2.1 Smart City data**

Smart cities are constructed by connecting the city's public infrastructure with city application systems and passing collected data through numerous layers. City application systems then use data to make better decisions when controlling different

#### *Perspective Chapter: Data Monetization Model for Sustaining Smart City Initiatives DOI: http://dx.doi.org/10.5772/intechopen.108345*

city infrastructures [15, 16]. These application systems allow additional ICCC components to aggregate, consume, and process the information for deriving insights. Data collection and processing consists of modules for collecting and converting data from multiple structures, data repositories and diverse data formats [4].

The ICCC is the city's brain, making it smart, sustainable and ready for the future by monitoring all of the city's activities. Such centers are built to integrate knowledge through various applications and sensors deployed across the city, and then provide actionable intelligence with accurate analysis for decision-makers with the aid of sensors installed throughout the region [3]. The data collection under ICCC collects real-time data from sensor systems, data sources, static and real-time data streams for air and water quality control, light sensors for street light monitoring, metering tools, telematics and location-based apps, proximity sensors, surveillance and security cameras, sensors for disaster detection [2]. Hence, the data is distinct and non-perishable in nature making it a unique value proposition.

Smart cities had already started using data for good governance and taking strategic decisions. For example, the city of Ahmedabad in the state of Gujarat deployed Automated Fare Collection Service (AFCS), Automatic Vehicle Location System (AVLS), Passenger Information System (PIS), Vehicle Planning Schedule and Dispatch System (VPSD), Depot Management System (DMS) in their Bus Rapid Transit System. These are few of the IoT systems deployed in traffic system in smart cities which will later form a basis of study for targeted customers. All of these involved immense tech interventions and hence could generate real-time data for city transit service. Around 69 cities of the 100 cities have created Integrated Command Control Centers [17]. Data from each service is collected and analyzed uniformly in a command control center against key performance indicators to create more efficient and dynamic bus service operations, and a smarter, safer travel experience for commuters, across the ticketing, in-station, and in-journey stages [18].

The Bhubaneswar Smart City of the state of Orissa had deployed environmental sensors for, a sensor-based monitoring dashboard. The main environmental challenges that Bhubaneswar faces include rapid unplanned development especially construction, increasing pollution from vehicles and commercial establishments, road dust and other fugitive emissions, and significantly higher noise levels. The instruments transmit sensor data to the cloud platform through Ethernet / General Packet Radio Service connection, where it is stored and real-time analysis is done to make it meaningful. It is then visualized using a pollution-monitoring dashboard, where data is presented using interactive graphics and statistics for easy interpretation by citizens and administrators [19].

This data gives insights to the cities to take strategic decisions on several dimensions for better service performance. This creates an enabling ecosystem to makes the cities liveable. They are also used for monitoring citizen activities in public spaces making them also participate and be responsible for their conduct which is a crucial factor given the population and diversity in India.

Internet of Things (IoT) is revolutionizing the functioning of smart cities, especially in areas such as Efficient Water System, Smart Traffic Control, Accessible Public Transit, Energy-Saving Houses, Smart Parking, Productive Parking, Smart Street Lighting, Safe Environment, and Waste Management [20].

Indian government has succeeded in creating an Open Government Data (OGD) portal to promote transparent data. On this portal data is already being pooled to a data lake and refined for public use from a data warehouse. This website helps policy agencies to distribute their data sets for free public access, in a transparent format. Around 538,330 resources have been submitted to the website so far, and has received more than 31.64 million views. Many of those types of data can be personalized and optimized [21].

Thus far, from 100 Smart Cities the accessible data network has 12,547 data catalogs. Despite of having huge number of data catalogs the level and refinement of these data is not enough to get rich inferences for the cities. The data is being collated at macro level but still the analytics are weak and lead to suboptimal utilization of such real time data to take strategic decisions for the city. Wherein it can be observed that the private data platforms are able to sell their product due to the depth, richness and extent of information the data is able to generate for specific user segments. Many private entities like Google, Facebook and various e commerce companies are able to utilize the information obtained from their platform to sustain and grow their businesses or even arrive at data-based business models.

In contrast to that, being one of the complex and high maintenance systems involved in smart city missions, the data portal initiative needs to be taken to a level where it is capable enough to cater needs of data consumers and could support a part of expenses by revenues generated from it.

#### **2.2 Smart City finance**

The Smart City Mission being a Centrally Sponsored Scheme (CSS) the Central Government of India provides budgetary assistance to the Smart City Project to the amount of Rs. 48,000 crores over 5 years, i.e., an average Rs. 100 crore per region annually. The State / Urban Local Bodies (ULB) would have to spend an equivalent sum on a reciprocal basis; thus, almost Rupees one lakh crore of government / ULB funds would be required for the creation, operation and sustenance of Smart Cities.

The investment plan of each Smart City is different, based on the degree of commitment, model, implementing and repaying ability. Substantial funds are likely to be needed to execute the Smart City plan and to this end both the Center and the State must use policy grants to raise financing from internal and external sources.

The Government of India (GOI) grants and the States / ULB funding allocation would only cover a fraction of the expense of the initiative. Balance funds for operations are required to be collected through novel funding frameworks. Some of the smart cities are able to tap potential of municipal bonds, land monetization and other methods. The GOI encourages smart cities to come up with innovative financing methods to make Smart City systems more sustainable. With immense capital deployment under smart city mission the cities in India are in a dilemma to sustain the initiative as the operations and maintenance of smart city assets need to be fetched by the urban local body itself [7].

Although smart cities have been provided with some seed funding from the Centre and the state, with some to be created by their own budget, the financial necessity remains enormous as smart city strategy needs to support the whole city and not just the region specified for growth in the times to come.

#### **2.3 Data as a source of revenue for cities**

For example, "Terbine" is an enormous system of IoT data feeds to organizations involved with smart city research and pilot projects. This exchange offers sensor data sources that include electricity, water, wastewater, air quality, vehicular counts and movements from land/air/sea. Data researchers use deep web searching and customized Terbine tools to seek out publicly available machine-generated/sensor data from many sources. These include towns, cities, counties, and states, whole governments around the world, plus universities, research institutions, and more. Searchers then create highly descriptive metadata and submit them for entry into the Terbine system. Thus, bringing the data generated from the actual infrastructural elements found

#### *Perspective Chapter: Data Monetization Model for Sustaining Smart City Initiatives DOI: http://dx.doi.org/10.5772/intechopen.108345*

within and around municipalities into a single cohesive system, makes it discoverable and usable to researchers and project implementers alike [22].

With virtually every industry sector beginning to utilize Artificial Intelligence for internal processes, systems operation, supply chain and logistics, plus customer interactions, the requirement for AIs to discover, access and process data coming from machines is increasing rapidly. Smart cities in particular are key areas for implementation of AI-based functionality [23].

The technology revolution which is envisaged here would cost capital. Given that the private sector has the ability to harvest huge dividends from this information, charging them for its usage is common but there are limited cases where cities are monetizing data as a revenue model [7]. The financial position of cities in India is not encouraging enough to fund for the operations and maintenance of smart city projects. It is imperative for them to find a viable source for continuing with the initiatives taken under smart city mission reasonable.

The Ministry of Road Transport and Highways (MoRTH) experimented with monetizing vehicle data. The ministry came with a bulk data sharing policy to share data related to driving license and vehicle registration to various private and government entities. The buyers comprise of public and private sector banks, logistics solution providers finance organizations, insurance organizations, and automobile manufacturers. Only the non-personal data of the vehicles was shared and a total of more than 20 indicators of 50 crore registrations have been sold [24]. Though policy have been scrapped now, the agency monetized data to a considerable extent. However, in 2020 the policy was scrapped due to privacy issues arising out of triangulation [25]. This initiative if applied appropriately with better business models and backed by data privacy settings in accordance with the data privacy act (still in draft stages in India) can not only help government agencies generate revenue but also would be provide a sound base for researchers, private businesses, lending agencies to better strategies their offerings.

#### **2.4 Data from G2B as a source of strategic competitive advantage**

Information is a critical source to attain competitive advantage. Both established competitors and new entrants in most industries leverage with data-driven strategies to innovate, compete and capture value.

Vast data sets are being compiled and evaluated to identify trends for decision making and developing intelligent strategies to enhance the value proposition of the offerings. According to research carried out by the McKinsey Global Institute and the Market Technology Office of McKinsey & Company, the sheer volume of data produced, processed and extracted for insights has become economically important to companies, governments, and consumers [26].

Companies in current times could effectively monetize from data through enhancing their data storage and offer the data to other customers or companies. Data could be raw or purified based on the inventory and needs of the buyer. Vodafone sells mobile network data to TomTom to enhance their real-time navigating services. Similarly, Barclay's bank provides a platform for SME companies to compare their financial (key performance indicators) KPI's to others. But at the same time, the major setback would be the ability of the partner or purchaser to have the same system that would support data apart from the sensitivity of it [27].

Since the data in G2B is unique in nature, it can help generate potential development possibilities and completely different market areas, such as those aggregating and reviewing data from the sector. Many of these will be companies that are sitting in the

middle of large flows of information where data can be captured and analyzed about products and services, buyers and suppliers, consumer preferences and intent [26].

As soon as businesses and policymakers realize Big Data's ability to produce higher efficiency, greater value for customers, and the next phase of innovation in the world economy, they will be granted a good enough motivation to move robustly to address the obstacles to its usage [26]. Like the targeted advertising which Facebook and other tech giants practice is a big business for them.

This is the kind of potential government can unlock out of their collected data. In doing so, they will open opportunities for new market competition, higher publicsector productivity that will make for improved infrastructure, and balance fiscal deficit to some extent.

#### **2.5 Policy and regulatory framework**

As of now regulatory framework Personal Data Protection Bill ("PDP Bill") and Information Technology Act, 2000 ("IT Act") empowers the government to manage and organize the data within government and non-personal data to various stakeholders. But the PDP Bill introduced various contentious concepts such as data localization and data mirroring, which caused much consternation among corporate stakeholders who would have had to restructure significant parts of their data flow architectures to comply with such requirements. The existing IT Act is a relic of its time and does not adequately cater to modern data protection requirements. Therefore, a comprehensive overhaul of all data laws in India is a positive step towards solving India's data woes in a holistic manner. Recently media reports citing that government sources have indicated that the Government of India will shortly commence work on a new law to replace India's IT Act. As part of this process, it appears that the Government may introduce policies on data governance and cybersecurity, a "Digital India Act" to replace the IT Act and new regulations to replace the PDP Bill [28].

In support to this the Ministry of Electronics and Information Technology, GOI released draft National Data Governance Framework Policy (NDRFP) empowering data to be harnessed for more effective Digital Government, Public good, and innovation by maximizing data led governance and catalyzing data-based innovation that can transform government services and their delivery to citizens, especially in areas of social importance that include agriculture, healthcare, law and justice, education, among others. This policy also launches a non-personal data-based India Datasets program and addresses the methods and rules to ensure that non-personal data and anonymized data from both Government and Private entities are safely accessible by Research and Innovation eco-system [2]. But considering the risks like triangulation and revealing the identity of individuals and assets it has to be handled carefully so that data sets from open sources and received under NDRFP combined should not lead to privacy issues like what happened with vehicular data from Bulk Data Sharing Policy under MoRTH.

#### **3. Methods**

Selling the data or analysis derived from it, have been studied scarcely in business and academic literature. In academic literature, the phenomenon of big data and the utilization of it have been popular topics over the last few years.

*Perspective Chapter: Data Monetization Model for Sustaining Smart City Initiatives DOI: http://dx.doi.org/10.5772/intechopen.108345*

The changing business environment creates new possibilities for data utilization, data monetization as being one option for broader data usage. This has created a research gap, as companies are increasingly using the data in new ways, while the academic literature does not cover these new data derived offerings.

Therefore, the concept of data exchange is a nascent Indian ecosystem and is more practiced on B2B with high confidentiality on backend thus this is an under-researched theoretical topic in the Indian context, therefore, the qualitative methodology with support of case studies, documents and journals from various sources for data is used. Further, due to limited availability of data wherever suitable, the case studies, and practical practices would be used for the genesis of this exploratory research.

#### **4. New perspective**

#### **4.1 Leveraging Smart City data**

Smart Cities are deploying emerging technologies to capture real time data from cities in different sectors. Barcelona, Copenhagen and Singapore, as indicators of Smart Cities, are frequently cited being the front runners. There are examples of Big Data and its insights for enhancing the transport industry tremendously in a number of ways. It may be used to ensure that at any given moment, consumers are constantly made informed of the most appropriate / efficient mode of transport. Train operating companies now use Big Data to process live seat allocation data to say which carriages have the most seats available to passengers waiting on station platforms. Public transit is one of the main problems confronting today's communities. For example, in the transport sector big Data includes such data on bus and rail vehicle occupancy, real-time car parking data, local weather and air quality data, road speed and traffic count data, and real-time infrastructure status. Customer identification may serve to optimize customer satisfaction in addition to the improved customer experience resulting from improved awareness. The collection and successful review of the same customer's repeat grievances may result in a single, more efficient response. Smart Cities and Big Data will also enhance customer satisfaction by providing creative technologies that substitute both system and ticket utilizing mobile apps [5].

The more integrated and open our rail networks are, the greater the profits for travel companies and transporters. Improved information for consumers would contribute to a more effective usage of transport networks- time and resources optimization for passengers. Previously, the amount of time saved arising from the opening of Transport for London's accessible data was calculated to be around £58 m a year, with an average investment of less than £1 million. Additionally, the Big Data buying / licensing industry itself, and other associated businesses, would broaden and thereby boost economic development as a whole [5].

Smart City data also helps digital marketers to help target consumers with more tailored advertising they will more definitely like to see. Google, and now Facebook — the main digital ads players — have proven really good at developing and providing more non-intrusive advertisements. The explosion of mobile apps, especially smartphones, has provided a significant incentive for digital marketers to offer mobile unique advertising at the right time — in context — to the right people. It has been seen that hyper-localized ads improves consumer interaction and sales volume. This will also put forth actionable perspectives that guide strategic decisions and strategies [29].

#### **4.2 Business model canvas**

These data exchange work on specialized service delivery business models. For creating a simplistic picture, Business Model Canvas by Alexander Osterwalder is used. A Business Model Canvas is a strategic management tool to quickly and easily define and communicate a business idea or concept. It is a method that works through the fundamental elements of a business or product, coherently structuring an idea. In these, the customer segments which the entity is going to target base on the segment, value propositions based on the amount of value and USP we can provide to the customer, channels for delivering the service, type of customer relationships to be maintained with the customer, revenue streams that would generate income for the business, key resources required to run the whole business, key activities that need to happen to sustain business and operations, key partnerships that are required for business, the cost structure of all the expenses of the smooth functioning of the business [30]. These are identified to formulate in-depth insights and design a business layout as shown below in the **Table 1**.


#### **Table 1.**

*Business model canvas.*

First to identify the right data sets few of the smart City IoT platforms have been listed such as intelligent traffic management, smart lighting, smart health, which collection, smart environment, smart water supply, smart meter, and smart parking. These platforms have been thoroughly evaluated and out of these data sets, the data collected under these platforms have been narrowed down.

#### **5. Business model**

The approach that has been followed to identify the right data sets for the right customers to provide the right value through the right channel and that makeup to the whole business model.

Based on the segmentation the mass market it turns out to the retailers, consulting firms, application developers, telecom services, advertising companies, research companies, hospitals, and the niche market belongs to car insurance company, payment wallet companies, credit card companies, transporters, shopping malls, real estate developer.

To carry on further study with an example out of all these companies two of the companies from the mass market in two of the companies from the niche market have been identified. For the chapter the case of advertisement agencies has been considered as a targeted buyer.

After identifying the companies and the target segment, further, the right value proposition for each of the companies has been evaluated.

#### **5.1 Customer segment and value proposition: Value proposition canvas**

Since the study aims to establish a G2B system, the value proposition for each of the businesses will vary as per their requirement since every business is unidentical *Perspective Chapter: Data Monetization Model for Sustaining Smart City Initiatives DOI: http://dx.doi.org/10.5772/intechopen.108345*

and every business will have a unique set of requirements for their service to their clients. The value proposition will differ for each of the customer segments [30].

#### *5.1.1 Value proposition: Advertising companies*

The customer jobs of the advertisement companies are to look for an increase in awareness, wanted to have innovation in their products, to have the most profitable spaces, and retain customers also they wanted to stay up-to-date to stay ahead of their competitors and to capture potential target markets. The gains for the advertisement company would be to keep customer retention, innovation, to acquire new customers, and to have a strong network. The pains to achieve all this would be if the advertisement company acquire loss-making locations or if they had a hard time finding new markets. Also, extreme competition would be a pain and new entrants, wrong surveys, and time have taken processes to collect data is a pain.

The products and services that will be provided are area-based insights based on the data sets with an analysis tool through an interactive platform is to be provided for the assessment of areas by the company. This is assisted by backend services if opted for it. Gain creators would be analytics and tips to use analytics that for advertisement companies, the real-time data, the coverage area, and the pre-feed calculation modules will lead to gain and save time. On the other hand, pain relievers would be the product that would minimize the risk of failure, it is practical and visual and easy to use prefilled with applicable content accurate data and with real-time data collection.

There are also few unavoidable and addressable gains, customer jobs, and pains for advertisement companies like customer retention that is based on the competencies of the advertisement companies. Also is an extreme competition that they have to face on their own then the issue with a strong network and acquiring new customers also lies with the competency and the service provided by the advertisement companies to their customers.

Concluding the business model canvas, we can say value proposition is information as a service that is accurate, trusted, descriptive, and convenient for a customer segment that is advertisement companies that fall under mass segment as shown in **Table 2** below.

**Table 2.** *Business model canvas: Value proposition for advertising agencies.*

Before going it must be realized that out of the data sets that will be provided to these companies, not all the indicators or attributes would be useful for the advertisement companies. To evaluate what are the data sets and indicators are for specific use for the advertisement companies, a few data sets have been shortlisted on how to weight the most useful indicators that would be e evaluated for the advertisement companies. Now there are a different number of indicators under each of the data set this like one IoT sensor collects a total of 12 indicators, Citywide Wi-Fi has a total of 38 indicators, electronic ticketing has a total of six indicators, smart poles have 38 indicators, multi-service digital caves have a total of 12 indicators. Based on this there are only a few indicators that would be packaged for the advertisement companies and the rest are of no use for the advertisement companies. The useful indicators make some portion of the total indicators in each data sets on evaluating that we would get the amount of leveraging the company or sell the advertisement company is getting over is data set. For example, under the first dataset, only 5 out of 12 data indicators are required, under city Wi-Fi only 3 out of 38 data sets are required, for electronic ticketing to out of 6, for smartphones 538, and for multi-service, digital gives 5 out of 12. This will help o.

Under this exercise we have determined the value proposition for the specific company and how your product can help this company to achieve its customer jobs also how much leverage that company is getting on our product.

#### **5.2 Channels**

Further evaluation of the business model, two types of channels is owned or it could be a partner channel or a combination of both. The own channel would be direct and on the other hand, the partner channel would be indirect. Under direct and we can have Salesforce, web sales on stores, and under indirect services, we can have partner stores or wholesales stores.

Show by comparing benchmarks of the services from AWS, Dawex, Quandl, and CMIE, all of these have their direct sales force and the product is sold through web sales since it is an intangible asset. It is not necessary to have physical resources concluding out the sales force would be there for the proposed business model with the help of web sales.

#### **5.3 Customer relationships**

There are distinct kinds of relationships that we could avail to our customers like personal assistance, customized dedicated personal assistance, self-service, automatic services like backend support from AI and ML or there could be blogs communities to help the customers. In AWS there is no dedicated personal assistance in the vertical of data exchange but they do provide rest of the services. In Dawex the only provision of personal assistance dedicated personal assistance to the large enterprises and selfservices option for the most basic one and the rest are not provided. For Quandl there is an availability of automated service rest of the services are available based on the package type the customers are opting for. For CMIE only personal assistants and dedicated personal assistance are available for the customers. Concluding all the options since this data exchange would be in the critical and small stages it is recommended to have a personal assistance type of customer relationship also dedicated personal assistance to the large enterprise rest could be dropped. But in the future when scaling this data exchange, the rest of the type of customer relationships could be included in this data exchange.

*Perspective Chapter: Data Monetization Model for Sustaining Smart City Initiatives DOI: http://dx.doi.org/10.5772/intechopen.108345*

#### **5.4 Revenue streams**

Revenue streams could be categorized in the sale of the assets, uses fees, subscription fees, renting for leasing of the platform, license to use the product, brokerage fees on the sales, or it could be through advertisement. Under the selected vertical in AWS, only the revenue is made on asset sale and the brokerage fee is also charged from the vendor that has to list its product on the platform. For Dawex do there is only use it brokerage fee for the big enterprises a subscription fee for the small and base packages to use the products. Also, there is a licensing fee to use the platform. In Quandl only subscription fees charged for the product used also the brokerage fees are collected from the vendors that are listing their products on this platform. In CMIE subscription fees are charged based on the type of user that could be categorized in companies or institutional packages.

Based on the above benchmarking it would be appropriate to charge only a subscription fee type of model in the earlier stages and later on added to the licensing brokerage fees when scaling up.

#### **5.5 Key resources**

Key resources could be physical, intellectual, human, and financial resources. In all the four cases all these aforementioned resources are required in all the cases, therefore, concluded that each type of resource is essential to make this business model viable.

#### **5.6 Key activities**

There are three types of activities that are production, problem-solving, or providing a platform or a network. Buy understanding of AWS it was found that all three key activities are there, i.e., production, problem-solving, and provision of the platform is there. In Dawex the only production of the data and platform network is present. Problem-solving is a key activity is not there since it is operating to provide data not prescriptive or predictive analysis. Quandl has all three key activities just like AWS and CMIE does not have a problem-solving feature. It only has the production and provision of a platform network to provide the service. Inference to that these business models, concludes the production. That would be provided over a network hence the activity would also include the provision and also the packaging is based on the prescriptive and predictive analytics therefore problem-solving component would also be included in this business model.

#### **5.7 Key partners**

This has a breakdown in 4 types of partnerships. The first strategic alliance between the non-competitors, and corporation that would be a strategic partnership between competitors. The third is joint ventures to develop new businesses and products. Fourth is the buyer–supplier relationship to sure reliable supplies of the product.

In AWS we have only a strategic alliance between competitors and buyer–supplier relationships. In Dawex to the buyer–supplier relationship does not exist, the rest of the options are present. In Quandl there is no corporation or strategic partnership between competitors rest of the options of key partnership types are present. In CMIE there is no sort of key partnership is involved. Inference in this there could be a strategic alliance between non-competitive sort of partnership in the early stages of the business model. Also, the buyer–supplier relationship to have the supply of raw data into the system. The rest of the options are not recommended based on the risk involved.

#### **5.8 Cost structure**

Aforementioned shortlisted the key resources that are physical, intellectual, human, and financial will account for a cost. For physical this could be the infrastructure required to run these operations on intellectual sides we need licenses permissions brand and the raw product. For human resources, there would be salaries and insurances. Also, there would be a maintenance cost of the digital platform, background services, and sales marketing.

Collating all the exercises that we have mentioned above we have concluded a business model for advertising consulting firms based on the concluded business model components as below in **Table 3**.

**Table 3.**

*Showing derived business model for advertising consulting firms.*

#### **6. Conclusion**

The data exchange businesses shall always be value-driven since data is an intangible asset the information shall hold the value for the customer. If data is not valuable and distinct to the customer then there is no unique selling proposition in the product. Data exchange is an expensive operation so most of the services can be outsourced as there is a cheaper option available outside to outsource the businesses rather than having again in house setup.

Creating industry specific data insights by leveraging the data capturing ability of the smart cities and further monetizing the same will be a win – win proposition for government and private entities. But caution needs to be exercised with respect to privacy breach. It is particularly essential for India, as cities are trying to automate their delivery of public infrastructure by introducing interconnected Pan City Smart Technologies under the Smart Cities Project by producing huge volumes of data. Meeting digital infrastructure's operating and maintenance costs, measured at 15–20 per cent of capital annually, would be a challenging task unless we identify a revenue model [16].

#### *Perspective Chapter: Data Monetization Model for Sustaining Smart City Initiatives DOI: http://dx.doi.org/10.5772/intechopen.108345*

This system will not only help government to increase its efficiency for public operations and ease on financial support but also will help business to optimize, generate and expand new services while staying ahead of competitors. The transformation of the new open-data model into a data monetizing system creates a tremendous opportunity for all the stakeholders and a motivation for city officials to collect and feed appropriate data into the system.

### **Author details**

Mercy Samuel\* and Siddharth Gupta CEPT University, Ahmedabad, India

\*Address all correspondence to: mercy@cept.ac.in

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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### **Chapter 9**

## Understanding the Artificial Intelligence Implementation for Allocating an Order to a Seller among Multiple Sellers Who Sell the Same Product

*Md Imtiaz Ahmed*

#### **Abstract**

E-commerce sectors are growing rapidly worldwide and it adopts the new technological innovation drastically, such as embracing artificial intelligence in e-commerce sectors. Machine learning adaptation in e-commerce sectors is the most and much news already published by giant e-commerce companies, such as Amazon and eBay. The aim of this paper is to find out how artificial intelligence helps the e-commerce platform to choose a seller from multiple sellers when the same products or listings are sold by multiple sellers. When a customer will place the order, then who will get the order of the customer as multiple sellers sell the item within the same product listings. In the research, it is figured out that machine learning techniques are normally used for the selection of the seller where the prior points used for finding the appropriate seller are feedback or ratings, seller products location or distance from the customer, advertising or PPC or campaign, discounts, etc.

**Keywords:** e-commerce, artificial intelligence, machine learning, order management, multiple sellers

#### **1. Introduction**

The e-commerce sector is growing drastically every day due to the customer's trust and easiness of the products ordering process. It can be said that e-commerce is one of the most identical evolutions of the twenty-first century and in COVID-19 situations worldwide [1]; its value or worth cannot be written in a sentence, which means the e-commerce sector's priority goes up dramatically. Many small businesses and large businesses use e-commerce platforms to operate their business and get sales from the platform. However, most of the people or companies who run or operate their business on large platforms, such as Amazon, eBay, and Google shopping, actually do not know how they actually get the sales from them.

How to get a sale from an e-commerce platform? Many people or companies have these questions normally in their minds. Then the methodologies that are popular nowadays are that one should use digital marketing or SEO or advertisements [2]. Many people or companies invest a lot of money into the marketing purpose to get sales on an e-commerce platform. If one uses their own e-commerce platform, then it is okay to invest a lot of money on sales and marketing purposes, as well as getting popularity of their own e-commerce platform; however, in the 1st world countries, most companies or people are moving to the giant e-commerce platform, such as the Amazon, eBay, Alibaba, Rakuten, and Google shopping, because they know that they can get orders from their platform if normally they list or create their stores on these giant platforms. How can one seller or a company or person get sales from a popular platform? This is one of the big questions nowadays.

One should say that these giant platforms invested a lot of money for building their popularity or getting attractions to the customers. Not only did they invest money in advertisements but also in the adaption of new technologies and methods for implementation in their platform. Artificial intelligence is one of the prime technologies that are being adopted in e-commerce by which many tasks are very light and can be used for prediction, forecasting, and allocation purposes [3]. Customer behavior to the platform and trusted customers or defaulted customers can be sorted out through the implementation of artificial intelligence [4]. How the products can be cycled and how the product's assortment helped in selling an item in e-commerce can be pointed out through artificial intelligence [5].

Nevertheless, it should be stated that artificial intelligence applications, such as machine learning, data mining, deep learning, and recommendations algorithms, are vastly used by a lot of giant e-commerce platform owners and they keep updating themselves with the use of these techniques [6]. The recommendation algorithms are very popular and are used by a lot of sectors or industries, such as Netflix, Amazon, eBay, and Google [7]. Recent news comes from Amazon is that they reduced packaging waste by the use of machine learning [8], so it can be noted that giant platforms keep updating themselves with the use of AI. In this paper, the key technologies that surround how one should understand that they can get sales from an e-commerce platform where multiple sellers sell the same product. Though it cannot be stated that every platform uses the same key technologies that are stated in this paper, however, sellers or companies will gain vast knowledge after reading the paper (**Figure 1**).

*Understanding the Artificial Intelligence Implementation for Allocating an Order to a Seller… DOI: http://dx.doi.org/10.5772/intechopen.105560*

#### **2. Background**

E-commerce sectors are growing rapidly, and in most cases, people are most likely to build B2C e-commerce sites. However, there are few e-commerce sectors that provide multiple sellers opt for specific products or brands. Some of the e-commerce platforms both served as B2C, B2B, and in all e-commerce sectors that provide an option for companies or stores to list their items on their platform are normally called C2B but have the option of B2C and B2B as well. The seller who is selling the items on the e-commerce must need to know how to use their platform and how to add images or products. In many cases, sellers are worried about the sales and want to generate sales by using advertising or campaign techniques. Without implementing or investing in marketing for the products, some sellers can easily get sales as well. This paper focuses on how sales can be generated from e-commerce by the e-commerce built-in methods or implementation of AI or machine learning methods.

Research carried out shows that it depends on the customer's rating and feedback [9]; however, many researchers find out the products need to be attractive [10] or products' popularity can be an eminence that can play a good role in the sales of a product. Product choices for selling purposes on e-commerce are similar, like before starting any company, one should think and survey the market and how the people are willing to absorb the new products and the similar product's current market worth or value [11]. People or companies need to know a basic understanding of how the platforms normally work and how the technology helps run an e-commerce platform to be more identical so that marketing costs will be less.

It can be stated that without any investment in the advertisement, one seller can get sales from a platform such as Amazon, eBay, and Etsy. Actually, no one knows how it will work, however, the key technology behind it is the recommendation algorithm, whenever a similar type of product will be searched on the platform, if the seller item is cheap and have a good reputation, then it will show on the search [12]. However, there are some other criteria that are on the air for getting better search activities, such as elaborative descriptions, features, good zoom quality images, correct product type selection, and informative keywords. A group of researchers already worked on the features finding as it plays a vital role in the sales of products and comes up whenever a product search occurs [13]. It seems that for the betterment of the products, looks, and sales, the above criteria are necessary.

We are living in a Era where we cannot think of a single moment without the help of artificial intelligence, rather day by day more implementations are adopted to our regular life as AI shortens the tasks and helps us improve our tasks. Data science and machine learning are currently widely implemented in e-commerce sectors for sales and attracting customers [14]. The machine learning approach is used for fraud detection in e-commerce sectors as well [15]. The machine learning support vector machine used on the product reviews helps people's choices or attraction or sentiment analysis [16]; in some cases, machine learning is used to predict sales of a specific store's products [17]. Knowledge-based means using data mining techniques and machine learning the recommendation system actually work as well [18]. At last, it can be said that every aspect of the e-commerce sector is updating rapidly with the use of artificial intelligence, machine learning, recommended systems, data mining, etc.

#### **3. Observation**

In this research, the identical observation has made through the use of a renowned e-commerce platform and the main observation was about the multiple sellers who are selling the same item. Example: Let us say, a product is a sunglass, which is from the company Ray-Ban, and that item can be sold by different sellers as the manufacturer authorizes those sellers to sell the same product on their floor and online. So if all the sellers want to sell this item on online platforms, such as Amazon and eBay, then they have to use the manufacturer-provided specific UPC. By using the same UPC, different types of issues can arise and it is found in a research paper [19]. As the same product can be sold by multiple sellers, then questions can be arising to the seller who will get the order when a customer will place an order. The shortest path algorithm to find the nearest product location from the customer can be a good solution that can be stated for selling purposes [20]. However, the background of the technologies is still hidden as everyone knows that e-commerce sectors are adopting new technologies and artificial intelligence vastly, so how the order procedure beyond one product with multiple sellers works is still hidden. In this research, the basic ideas with the proper example will be given step by step to figure out the key technologies or the techniques that are involved in getting an order for a seller.

#### **4. Research gap**

The main research gap for this research, which is conducted, is finding how the large vendor manipulates the orders whenever a list of sellers sells the same item. It can be very good for the seller who wants to sell in large vendors, such as Amazon [21], Ebay, and Google shopping. Because if they can understand the technology behind how they can get the orders from the large vendor as they seem that it is very competitive. However, if they want to sell only their products, then they can judge whoever will buy their products, how much sales they can generate from the e-commerce platform, and they need to do research based on similar products, but it can be very useful for the retailers. If one seller sees that a product is sold by 40 different sellers, they normally will lose hope to sell that item and that can be a negative effect on the e-commerce sector. However, when they will find out that the main technology behind the selling procedures is artificial intelligence where each of the sellers will get a minimum order, then they will definitely move to the large platform for selling their products.

#### **5. Experimental procedure**

After years of observation of the e-commerce sectors, it is very identical that a product can be sold by different sellers or companies. Like a shoe of the UGG brand can be sold by multiple sellers on an e-commerce platform like Amazon, where each seller is different as they listed the item with the same UPC (Universal Product Code). So, let us say a seller sells a shoe whose size is 8, and the same shoe size has 6 different sellers, then sellers seem to be worried about from whom the customer will buy. It is true that one customer can choose from a list of sellers; however, in research, it is figured most cases customers do not choose sellers (**Figure 2**).

The experiment was done using the largest e-commerce platform Amazon [21] and multiple orders have been tried from different locations in the USA to figure out *Understanding the Artificial Intelligence Implementation for Allocating an Order to a Seller… DOI: http://dx.doi.org/10.5772/intechopen.105560*

#### **Figure 2.**

*Multiple sellers of the same product and seller's location.*


#### **Figure 3.**

*Twenty-seven sellers sell the same products and some of them are captured in the figure.*

which vendor is chosen whenever a customer from different states orders a product of UGG [22]. If one visits the product, then it will be found that around 20 plus seller sells the same product. In **Figure 3**, the images of the sellers are enlisted.

#### **6. Data collection**

#### **6.1 Consumers data**

In this research, feedback from 97 customers has been taken, where 75% of customers told that they do not choose the sellers, whereas 25% told that they sometimes but not always check the seller's name. A total of 25% of the customers also suggested that it is better to check the seller's overall scores, feedback, etc., so that they can normally think about the defect rate of the products or wrong shipment issues. Customers always look for prestigious sellers who are for a long time so that people have faith in them.

#### **6.2 Sellers data**

Around 40 sellers participated in this research, where they have been asked some questions, such as how they feel that they will get a chance to sell their products as multiple sellers sell the same item. Most of them replied that this is an uncertain thing, however, they always try to maintain their store's page attractively, and if they get any negative or bad reviews, they always satisfy the customer's issues. In many cases, they do refund and offer an exchange for the products for maintaining their good selling ratio.

In the questionnaire, it has been asked what they normally do for getting sales for their items. Twenty-nine of them replied that they normally do advertisements, campaigns, PPC (pay per click), discounts, etc. But in many cases, as the manufacturer bound them not to put discounts, then they cannot do it. Ten sellers told that they do nothing but they are getting orders. However, it is noted that those sellers who are doing advertisements, campaigns, PPC, etc., have better sales than the other sellers. So the question in mind is that how does the seller who does not do anything get sales as well?

#### **7. Understanding the process of order distribution**

It is figured out on the questionnaire that each seller can get sales from a platform whether they have good feedback (customer ratings) or no feedback (no customer ratings). However, if bad feedback (customer ratings) any seller has, then it will lower their chance of getting an order on an e-commerce platform.

#### **7.1 Multiple sellers with good feedback**

The key technologies of getting an order for the seller are normally evaluated by the system's hidden calculations that normally occur by artificial intelligence. Suppose that in machine learning we normally train the dataset that we have, then the algorithm predicts the results. So if we think of five sellers who have good ratings or feedback and sell the same item, then an algorithm can help allocate the customer's orders for them. If we draw with a diagram, then it can be easily understandable. Let us say, we have sellers 1, 2, 3, 4, and 5, where each of them has the ratings or feedback as follows:


So, if we train the dataset on a machine learning algorithm, then it can easily predict that seller 3 has the best rating, so the order should go to seller 3. However, a good system checks more basic points, such as the distance or location of the sellers as the

*Understanding the Artificial Intelligence Implementation for Allocating an Order to a Seller… DOI: http://dx.doi.org/10.5772/intechopen.105560*

buyer or consumers will always prefer the seller with less distance. So that the datasetbased distance algorithm can be an ideal technique for selecting a seller for the order [20]. Let us say that the five sellers have the distance from the customer as follows:


Now the system will calculate differently as the distance is another factor so that the system will allocate the nearest seller with average good ratings. So, in this case, the system will choose seller 4, though seller 1 has the lowest distance, however, seller 4 has a better rating than seller 1. So, the artificial intelligence applications help a seller for getting an order (**Figure 4**).

However, exceptions can be there if sellers use the system campaign or advertisements. Like seller 4 does not use the campaign, however, if seller 1 uses the campaign of the e-commerce platform, then the seller will be awarded for the order as the platform owner will get an amount of money for the order as the sellers are willing to pay for the pay per click (PPC) option (**Figure 5**).

#### **7.2 Multiple sellers with no feedback**

When an item has multiple sellers but none of the sellers owns any feedback or ratings, then the system will just check the distance from the customer to the seller for

**Figure 4.** *How machine learning and artificial intelligence allocate an order among the sellers.*

**Figure 5.**

*How machine learning and artificial intelligence allocate an order among the sellers when the seller uses.*

allocating an order to the seller. Let us say seller 1, seller 2, and seller 3 do not get any feedback yet, and then the system or application will check the distance as follows:


Then the system will automatically select seller 2 for getting the order as the customer address are the nearest distance from the seller. However, if any of the sellers use the advertising, then their possibilities of getting the order will be more appropriate.

#### **7.3 Multiple sellers with both feedback and no feedback**

Whenever a product has sellers with both feedback and no feedback, then the system chose from both depending upon the distance, ratings, and advertisement purposes. Let us think of a product with six different sellers in which two sellers do not have any feedback or ratings and four of them have feedback or ratings. Three of them use the advertisement for their items to be sold, which are given in the chart below:

From **Table 1**, it can be easily identified that seller 5 has the lowest distance for the customer, however, it has no feedback, and seller 5 does not use any advertisement. It can be also identified that sellers 4 and 6 have the 2nd lowest distance, which is 300 km, however, seller 6 does not have any feedback but seller 4 has good ratings or feedback. There is one noticeable thing, both seller 4 and seller 6 use the advertising of the products, so both can win the chance to get an order. Depending on this case, the algorithm or technique will easily select seller 4 as it has both feedback and uses advertisement. But it cannot be said that it will not choose seller 6 for any order. There can be conditions like if the same approach occurs multiple times, then select seller 6 one time if the same thing happened 10 times. Otherwise, if the seller with no rating

*Understanding the Artificial Intelligence Implementation for Allocating an Order to a Seller… DOI: http://dx.doi.org/10.5772/intechopen.105560*


#### **Table 1.**

*Dataset format when product sellers have both feedback and no feedback.*

doesn't get any orders on the platform, so it will lead the seller to lose hope to sell the item on the e-commerce platform.

#### **7.4 Exception cases**

There can be a lot of cases as an exception, which means the system described here cannot be potential for it because how the algorithm the developers will use in their policies. One of the identical exceptions is providing discounts on the products to get the attention of the customers, and customers always prefer to get discounts and willing to buy good products with discounts. The sellers who are giving discounts on their products will definitely get the priority of selling; however, in many cases, the manufacturer or the owner of the brand bounds the seller not to provide discounts. But in many cases, it is observed that many sellers do not follow the rules. So, this case can be an exception for getting an order. In some cases, it is observed that still in some orders, the seller does not get a discount because of their interruption and product availabilities. Like the discount exception, if a seller has items like 10pcs and another seller has 2pcs of the same product then, if the customer does not look properly at the discounted items then it will automatically be awarded to the seller with fewer quantities.

#### **8. Discussion**

The technology is beyond our knowledge of any e-commerce site where each platform owner implements their own technology. The popular technologies that are used to minimize the tasks of an e-commerce site are normally machine learning, data mining, data science, deep learning, and methods [23]. Research is already going on the theoretical understanding of products embedded in e-commerce by which researchers are trying to figure out how the machine learning techniques can be utilized properly in e-commerce [24]. This paper tries to figure out how an order will be distributed to a seller from a list of sellers of the same products.

The procedure and steps shown in this paper are normally investigated on an e-commerce site and there was a questionnaire section for both customers and sellers. So, both the seller's and buyer's perspectives on the e-commerce sector can be known. The research tried to figure out the hidden machine learning models that are occupied by the e-commerce sectors if e-commerce provides an option for selling the same product with the same UPC. It finds out the pattern by observing the platform and the questionnaires. The platform is observed for a year for the methodology finding and it can be stated that the e-commerce platform uses these techniques for selecting a seller whenever an order is placed by the customers.

It is true that there can be always exceptions; however, the e-commerce platform owners never open their ways of working into the air, but in this research, it was tried to figure out the technologies adaption and how the process actually is beyond a platform. In near future, more research will be carried out, and hope to work on the future with the platform owner so that real-time observation can be carried out by practically seeing the background system of any platform.

#### **9. Conclusion**

E-commerce uses are rising rapidly during the pandemic, and if a person thinks of business nowadays, then he/she first thinks of setting up an e-commerce platform. There are a lot of e-commerce platforms that already exist and give the option for the seller to list on their platform and in that case they take commissions from the sales. So that a lot of brands' products can have multiple sellers, and under one product page, a list of sellers can sell their inventory. So questions can arise about how the sellers will get an order as many sellers use the same platform. In that case, the research will help them find out how the sales will come or be appointed to the seller from an e-commerce platform. There can be exceptions that are elaborate in this research; however, the main summary has been sorted out that artificial intelligence is vastly used in e-commerce sectors, and choosing a seller from multiple sellers is one of the best examples of understanding the artificial intelligence uses. In the future, the research will focus on doing research physically with any of the renowned platform owners so that background codes and implementation can be understood more politely.

#### **Acknowledgements**

I hereby acknowledge that there was no funding or grants for this research. I should mention the e-commerce platform company Amazon, as in many cases for the research, I checked their procedures and systems.

#### **Author details**

Md Imtiaz Ahmed Computer Science and Engineering, Prime University, Bangladesh

\*Address all correspondence to: imtiaz.ahmed.ju@gmail.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Understanding the Artificial Intelligence Implementation for Allocating an Order to a Seller… DOI: http://dx.doi.org/10.5772/intechopen.105560*

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## UnIX-CARE: Universal Interface and Experience via Collaborative Archive Repository Express

*Sheldon Liang, Melanie Van Stry and Hong Liu*

#### **Abstract**

It is feasible to simplify interface design for better user experience without web developing skillfully. CARE or collaborative archive repository express holds the answer to universal interface & experience (UnIX) through algorithmic machine learning. CARE in collaboration with DATA and wiseCIO as a whole establishes a CMD triad for content management and delivery that harnesses rapid prototyping for user interface and propels user-centric experience by cohesive assembly of Anything as a Service (XaaS). Basically, user-centric experience makes a user centered without often webpage swapping while browsing in hierarchical depth via "In-&-Out" interactivity, and exploring in contextual breadth via self-paced spontaneity. Furthermore, CARE incorporates express tokens for information interchange (eTokin) into the CMD triad to prepare integral content management and informative delivery. In particular, by exploiting eTokin, CARE promotes seamless intercommunications inbetween and empowers users to be UNIX professionals cohesively, such as *ubiquitous* manager on content management and delivery, *novel* designer on universal interface, *intelligent* expert for business intelligence, and *extraordinary* liaison with XaaS without explicitly coding. More CARE uses algorithmic machine learning to coordinate instant online publishing, assemble efficient presentations via wiseCIO to end-users, and aggregate diligent intelligence over DATA for business, education and entertainment (iBEE) through robotic process automation.

**Keywords:** collaborative archive repository express, universal interface & experience, algorithmic machine learning, express token for info-interchange, algorithmic interactivity

#### **1. Introduction**

UnIX-CARE or Universal Interface & Experience has emerged from Collaborative Archive Repository Express through algorithmic machine learning that is involved in more and more aspects of everyday life through cloud-based content management and delivery (CMD) [1]. wiseCIO denotes web-based intelligent service engaging with cloud intelligence outlets [2], and DATA represents digital archiving via transformed analytics [3]. CARE is conceptualized as a "fastlane" that provides mathematical and

computational solutions to distributed and cloud-based problems to bridge the gap between integral content management over DATA and informative delivery on wiseCIO.

CARE is central to collaborating DATA with wiseCIO into a triad that best serves cloud-based content management and delivery (CMD) for UnIX that makes a user centered without often webpage swapping while browning via wiseCIO and exploring over DATA via algorithmic machine learning that enables users to browse information in depth with hierarchically "in-&-out" interactivity, and to explore intelligence in breadth with contextually self-paced spontaneity to aggregate intelligence for business, education and entertainment (iBEE) in support of decision-making [2–4].

#### **1.1 Collaborative triad for content management and delivery**

Collaborative triad is a model created to guide policies for information comprehension among and direct fulfillment of multiple cloud-based components that refer to *digital archiving* for content management over DATA, *intelligent service* for informative delivery via wiseCIO to support enterprise decision-making, and *archival repository* express for instant publication via UnIX-CARE. The model is also somewhat helpful in resolving controversial agendas among web personnel [5]. In general, cloud-based distributed intelligent services are currently presented via a website, or enterprise websites that are quite subject to the management and influence of personnel, such as a webmaster, web designers, service maintainers and end-users. Taking a large collaborative enterprise IT team as an example, "controversial web personnel" often have objectives for the websites that fail to consider the services being offered and could lead to controversial agendas: the webmaster oversees and ensures that the technical aspects of a website are met; the web designer is usually responsible for the site's creative aspects; and the end-user is pleasant to discover useful and usable information for enterprise decision-making.

As a collaborative effort made to turn *controversial* agendas into *cohesive* advancement to propel large teams united and working together effectively, Collaborative Archive Repository Express (CARE) incorporates DATA and wiseCIO into a CMD triad via universal interface with better user experience (UnIX) for content management and delivery. As a borrowing term for the sake of emphasis on critical briefness, "DNA-like" ingredients are introduced for transmissible UnIX to promote collaboration among three parties of the CMD triad. "DNA" stands for deoxyribonucleic acid that contains units of biological building blocks as a vitally important molecule containing something that makes individuals unique [6]. In addition to UnIX that makes users centered while browsing web content and exploring information, the CMD triad provides novel solutions to controversial agendas via eTokin-express tokens for information interchange in support of seamless intercommunications among three CMD parties and semantic enrichment from "DNA-like" ingredients to human-computer interfacing that is presentable and rederable through highly robotic process automation [7].

Algorithmically, intelligent services are developed with mathematical and practical methods for advanced solutions to integral content management over DATA and informative content delivery on wiseCIO. As a result, the CMD triad empowers endusers to be cohesively UNIX professionals, such as *ubiquitous* manager on content management and delivery, *novel* designer on universal interface, *intelligent* expert for business intelligence, and *extraordinary* liaison with Anything as a Service without

explicitly coding. In particular, DATA helps the end-user act like a webmaster to ensure that the technical aspects of web content management are met, CARE advances the end-user through web-based interface design without explicitly coding, and wiseCIO assists the end-user to be an intelligent expert to discover useful and usable information in support of decision-making.

#### **1.2 Chance and challenge**

By using "DNA-like" ingredients, both integral content under managed by DATA and informative delivery via wiseCIO are working cohesively without trivial information involved. It is the CMD triad through algorithmic machine learning [8, 9] that takes CARE in collaboration with DATA and wiseCIO to promote seamless intercommunications for interoperability via joint tasking of Anything as a Service among three parties of CMD triad. Express tokens for information interchange (eTokin) have been introduced in "DNA-like" notations to express (*via fast transmission*) digital archiving for content management and fulfill (*via online analytics*) intelligent services through universal interface & experience (UnIX). In a sense, UnIX simplifies instant content publishing in such a means that anybody could only need to input "DNA-like" eTokin in dictionary (Key-Value) pairs that are intelligently full of implicit syntactics and semantics in light of algorithmic machine learning; in a more significant sense, UnIX enables an ordinary user to be a webmaster, a web designer, and a database administrator with great ease. In light of "DNA-like" eTokin, In human computer interfacing, "DNA-like" eTokin's ability to innovate provides a key abstraction for UnIX differing abstractly from traditional web development in HTML/CSS/JS and/or PHP/Python.

The publishing express in dictionary eTokin pairs is capable of empowering universal interface & experience in the simplest means without explicit syntactics and semantics. This can shift the sophistication of interfacing design onto machine learning patterns without explicit coding required, but would result in highly brief description that is hard for a user to grasp, especially for a new-hand who might not be sure "what's going on" until the visual interface enabling algorithmic interactivity applied via operations. One of a "fake drawbacks" would become true that similar dictionary eTokin pairs may vary human computer interfacing when being associated with a variety of polymorphous and powerful patterns for machine learning. Web designers would have messed up web design with "wishy-sahy" agendas if they had lost understanding of original eTokin in depth.

Elastically, instant typing online publishing (iTOP) in dictionary eTokin pairs turns out immediately, the web designer can experience and enjoy visual renderability and actionable interactivity. Thanks to algorithmic machine learning, UnIX-CARE supports semantic enrichment transitioning "DNA-like" eTokin into analytical, interactive and responsive (AIR) across three parties of the CMD triad through elastic process automation.

#### **1.3 Major contribution**

UnIX-CARE in collaboration with wiseCIO and DATA utilizes "DNA-like" eTokin to achieve "cohesive UNIX" objectives as follows:

*Ubiquitous webmaster* across the CMD triad propels seamless intercommunication & interoperability among CMD parties to ensure technical aspects of web content management to be met (Section 2 algorithmic CMD).

*Novel designer* utilizes eTokin to perform informative delivery via universal interface design and automate user-centric experience without explicitly coding required (Section 3 innovative CARE).

*Innovative expert* aims to discover useful information and analytically harness intelligence for business, education and entertainment for enterprise decision-making (Section 4 analytical iBEE).

*Extraordinary liaison* with universal interface for rapid prototyping of user-centric experience and cohesive assembly from Anything orchestrated as a Service, which will be discussed (Section 5 Qinary XaaS).

#### **2. CMD triad via algorithmic interactivity**

Algorithmic interactivity represents information and operating technologies [10] applied to the CMD triad that is comprehended as UnIX-based Anything as a Service involving three correlated aspects: Collaborative Archive Repository Express promotes instant publishing to incorporate with integral content under managed over DATA, and informative delivery via wiseCIO to best serve cohesive personnel, illustrated in **Figure 1**.

With CMD triad diagrammatically illustrated in **Figure 1**, CARE to innovate uses iTOP for UnIX-universal interface & experience to bridge in between, DATA is evolved to cumulate "DNA-like" ingredients via digital transformation for integral content management (iCOM) through operating technologies, and wiseCIO is created to liaise with universal interface for informative content delivery (iCOD) through information technologies.

What is central to the CMD triad is computational thinking and machine learning [11]. The former empowers managers, decision makers and administrators to think laterally to generate a broader range of solutions, and the latter operationally automates a process of applying problem-solving from UnIX-CARE, through DATA, to wiseCIO. This chapter adopts the term of DNA as "DNA-like" ingredients that contain units of building blocks in the CMD triad for essential, vital, and sufficient information utilized for machine learning automata.

**Figure 1.** *UnIX-CARE collaborated with wiseCIO and DATA into CMD triad.*

*UnIX-CARE: Universal Interface and Experience via Collaborative Archive Repository Express DOI: http://dx.doi.org/10.5772/intechopen.107443*

#### **2.1 Integral content management via digital archives**

Online analytics and procedural automation highly relies on integrity of content under management where archives are digitized to ensure content to be *formattable* for computational processing, *verifiable* for analytical processing, and *cohesive* for algorithmic machine learning without unnecessary redundancies. How to format information and/or digitize content denotes a means by which a chosen pattern is selected to arrange and store text on a computer. The digital pattern promotes integral content management through digital transformation to which algorithmic machine learning can be applied.

As well known, DOM (document object model) dominates traditional web documents in HTML/CSS/JSON, and some data retrieved in XML/JSON from a database that may have some digitized features for computing and processing. A traditional web document primarily serves the sole purpose on how to render content as a web page on the client device via retrieving and/or downloading. With DATA, "DNA-like" eTokin is introduced to express digitally-archived content to serve one of multiple purposes: significant content of integrity ensured without being trivial information in HTML/CSS/JSON.

**Table 1** discloses what "DNA-like" notations look like in expressing a **profile folder** as a single folder for George Washingtong, the first President of the United States of America, and how profoundly they serve multiple purposes. The profile folder or folder, a digital archive that is "DNA-like", intelligent and applicable via algorithmic interactivity, performs UnIX design with multimedia to play and virtual containers through fold-out / fold-up.

UnIX-CARE is embodied by digitally integral archives of excellent novelty, characterized as *actionability*, *interactivity* and *manipulability* (AIM) for bridging between DATA (management) and wiseCIO (delivery) via algorithmic interactivity. For instance, the little image button to the left of foldHead enables to play the folderrelated multimedia, such as video, audio or other web service, and the arrow-button to the right fulfills fold-out (to open its body) and fold-up (to close).

As a result, all of the above mentioned activities will not cause webpage to swap but result in better user experience. Impressively, the "DNA-like" notation at least keeps integral content under management in utmost brief without redundancy, which will be in further discussions.

#### **2.2 Algorithmic online analytics via machine learning**

Online Analytical Processing or OLAP [12] is a core component of data warehousing implementations that enables fast, feasible, and flexible


**Table 1.**

*An illustrative "DNA-like" notation to serve rendering with semantics.*

multidimensional data analysis for business intelligence (BI) and decision support applications. iOLAP represents innovative OLAP that makes CMD triad actionable, interactive and manipulable for intelligent UnIX service through algorithmic online analytics and machine learning. iOLAP aims to computationally examine facts and information for decision-making. For instance, **Table 1** illustrates an intelligent pattern of @FLDr that the archival "foldHead" is associated with a playable "video", which makes sense on how to drive machine learning to commit AIM for actionability, interactivity and manipulability.

Machine learning is about using historical search probabilities in order to generate expected search objectives, solutions, and applications given the user's input action, query, subject, vocabulary choices, problem, or question. Given lack of context, the response may be generic in scope. Whereas, given repeated uses by an individual or group, the specialization may ensue in order to better fit an intended outcome or focus. Jargon may skew the result culturally or possibly even sub-culturally. This could lead to positive results: quicker utilization and responsiveness; negative results: stereotypical discrimination; irrelevant results: similar nomenclature, but unconnected material; bad results: silo dead ends. Ultimately, machine learning must not be in a vacuum. It must be done with context and in connection to these other features within the utilization of an archival system.

Consequently, iOLAP has applied machine learning on deep learning that fulfills online service with abilities to learn without being explicitly programmed, as illustrated in **Figure 1**. Feasibly, computational thinking can be applied to UnIX among CARE, DATA and wiseCIO for better user experience through algorithmic machine learning [13]. Most importantly, UnIX-CARE makes it possible that an end-user could be a webmaster, a web designer, or an ordinary user who enjoys and engages with web exploration.

**Table 2** as derived from the previous table, describes multiviews of @FLDr pattern with AIM at wiseCIO of actionability for informative delivery, DATA of manipulability on integral content management, and CARE of algorithmic interactivity for UnIX characteristics.

Cloud-based collaborative archive repository express takes good CARE for CMD between DATA and wiseCIO via machine learning whose AIM is clear to be actionable, interactive, and manipulatable for cloud intelligent service, as discussed below.

#### **2.3 Informative content delivery for decision-making**

Informative content delivery represents digitalization or digital transformation from integral content (under managed as DATA) to informative delivery (as intelligence on wiseCIO). Better user experience signifies information useful to and deliverable for end-users to act and interact with the remote service (decision-making) [14].

One of the significant values in a practical approach toward UnIX is fold-out / foldup of the detailed content (e.g. under the profile) as bodies. An end-user at his/her first glance at the profile is the foldHead the most attractive to explore, the secondary the image-related video (or some other multimedia) to play, and last (not least) folder body to open, all of which embodies user experience for readers' curiosity to be satisfied with spontaneity that enables individualization, interactivity, and independence.

User-centric experience aims at encouragement of exploring in contextual breadth (self-paced spontaneity) and browsing in hierarchical depth (in-&-out interactivity). It is easier to understand the interactive hierarchy without page-swapping while browsing the profile folder (**Table 2**).

*UnIX-CARE: Universal Interface and Experience via Collaborative Archive Repository Express DOI: http://dx.doi.org/10.5772/intechopen.107443*


#### **Table 2.**

*Further illustration for "DNA-like" notation to serve rendering with actions.*

On the other hand, contextually self-paced spontaneity encourages individualization while a user's exploring in breadth. A good example is a group of multi-news presented in collaboration with each other. Both universal interface (without explicitly coding) and user-centric experience are applicable through the following example in **Figure 2**.

**Figure 2** discloses that excellence of UnIX is applicable on how to group multiple profiles for contextually self-paced spontaneity in breadth while exploring without a fixed order. UnIX promotes user-centric experience that has been successful through both hierarchical interactivity and contextual spontaneity. In practice, UnIX has been successfully applied to advanced instructional delivery (AID) for the sake of hybrid learning engagement, and surely applicable Anything as a Service in business, education and entertainment.

As a matter of fact, traditional web content delivery could commit some unfriendliness, and it would be against psychological observations in terms of user interface that causes poor user experience: (a) too much information on a given web page that would be destructive to users' attention according to Dr. George A Miller [15]; (b) monotony in the mind causes boredom to mental fatigue by repetition and lack of interest in the details of presented works online that require continuous attention paid to. According to "Eight Reasons Why We Get Bored" [16], too much of the same thing

#### **Figure 2.**

*Contextually self-paced spontaneity for individual interests in breadth.*

and too little stimulation can cause in its victim an absence of desire and a feeling of entrapment.

wiseCIO has fulfilled informative delivery via universal interface for better user experience that is user-centric via hierarchical interactivity, and user-friendly via contextual spontaneity. Browsing in depth through hierarchical interactivity enables fold-out (to disclose hidden information) when desired to go into, and fold-up (to hide from too much information) from the first glance, which greatly assists the magical number of (7 2) applied to better user experience. The self-paced interests are satisfied by contextual spontaneity in breadth without a fixed order to explore, which wisely promotes avoidance of boredom in light of monotony in the mind [15, 16].

Further discussions will be conducted on how UnIX through Collaborative Archive Repository Express automates ideal informative delivery without explicit coding required.

#### **3. CARE for UnIX via archival repository express**

Cloud-based collaborative archive repository express aims for bridging DATA for integral content with wiseCIO for informative delivery where express tokens for information interchange (eTokin) are creatively introduced for universal interface & experience through elastic process automation. Similar to popularly used data formats such as XML and JSON [17] for C/S Architecture, eTokin is collaborative, text-based and more advanced (than XML/JSON) to support seamless intercommunications among the CMD triad.

It is "DNA-like" archival express (eTokin) that propels semantic enrichment during seamless intercommunication, as illustrated in **Figure 3**.

*UnIX-CARE: Universal Interface and Experience via Collaborative Archive Repository Express DOI: http://dx.doi.org/10.5772/intechopen.107443*

**Figure 3.** *Collaborative archive repository express among CMD triad via eTokin.*

**Figure 3**, from the perspective of a UnIX Designer, discloses CARE for eTokinoriented CMD triad through seamless intercommunications (arrows pointing toward) and semantic enrichments (away from eTokin) among three CMD parties: CARE, DATA and wiseCIO. Intercommunications toward eTokin perform implied elimination of trivial information without redundancy to guarantee integral content management, and semantics away from eTokin propels enriched activation of functional AIM for actionability, interactivity and manipulation without explicit coding required for informative delivery. Both trivial elimination and tactical enrichment are intelligently relying on deep learning experience, and automated elastic processes via machine learning (as denoted by dotted arrow lines).

#### **3.1 "DNA-like"archival express for universal interface**

"DNA-like" archival express enables universal interface & experience (UnIX) with which ordinary users can be made cohesive professionals to play multiple roles, such as UnIX Designer, Innovative Expert, and Webmaster via machine learning through elastic process automation, shown in **Figure 3**. The webmaster ensures technical aspects of a website over DATA, and the expert aggregates iBEE on wiseCIO, and the designer proposes CARE for universal interface & experience.

By using "DNA-like" archival express, cloud-aided DATA via machine learning acts like a webmaster for integral content management with high-speed accessibility to virtual containers, folders, and URL-related multimedia, so as to feed wiseCIO for informative delivery without unnecessary page swapping. Let us take a folder as an example: a profile folder (stated in **Tables 1** and **2**) usually represents a composite item with a caption and an arrow button that can be clicked to open with its body

extended beneath, or to close with it shrinked. This embodies user-centric experience browsing in hierarchical depth via "in-&-out" interactivity. E.g.,

**@FLDr(** foldHead ,) imgURL ,) videoID ,) emBody **)@**

The @FLDr(... )@ denotes a profile folder in "DNA-like" archival express that is well archived with a group of ingredients specified as a **foldHead**, an **image** button that enables to play the multimedia underneath, and an **arrow** button that controls extension / shrinkage of the folder body. How the profile folder is rendered remains unspecified until machine learning automata is triggered (dotted arrow lines in **Figure 3**) to enrich semantics for informative delivery.

Collaborative archive repository for information interchange among the CMD triad involves text-based content that is digitized and stored in "DNA-like" eTokin to represent folders, containers, URL-related images and multimedia, and semantic patterns. "DNA-like" archival express is capable of digital archive with integrity endured for the sake of *transmissible* retrieval with *minimal* bandwidth, and *elastic* process automation for online analytics. Transmissible retrieval means applicable, optional and operational fulfillment of cryptography depending on the level of enforced security; elastic process automation represents algorithmic interactivity to accommodate universal interface & experience (UnIX).

#### **3.2 Express tokens in collaboration within CMD tirad**

Collaborative archive repository express introduces "DNA-like" archival express, or express tokens for information interchange (eTokin) to provoke seamless intercommunications among the CMD triad in comprehensive collaboration so that three CMD parties, such as CARE, DATA, and wiseCIO, can feed to and/or retrieve from each other. By instant typing online publishing (iTOP) "DNA-like" eTokin is utilized to describe "what to do" for the sake of seamless intercommunications, but leave "how to do" unspecified in light of semantic enrichment that highly relies on algorithmic machine learning.

So CARE for universal interface & experience helps to make an ordinary user a webmaster capable of managing integral content, an innovative expert able to aggregate and deliver useful information, and a novel designer to create the universal interface for better user experience without explicitly coding required. "DNA-like" archival express (eTokin) for information interchange between CMD parties is context-neutral, text-based and cryptographic when describing *instant* publishing, *integral* management, and *informative* delivery until applied rules have been chosen for machine learning specifically at runtime. The applied eTokin "implants" feasibility and flexibility for UnIX to become reasonable and possible through elastic process automation.

One of the strategies of choosing "DNA-like" eTokin for information interchange is SnR: sufficiency and no redundancy—*sufficiency* means good enough to fulfill semantic enrichment for aggregating information on wiseCIO; *no redundancy* minimal as necessary to support online analytics over DATA with consistencies. For instance, a series of folders described in dictionary eTokin pairs:


;] 43rd President :> values for the profile

;] Others :> values for the profile

#### Where

**#>** triggering machine learning for semantic enrichment with AIM for actionability, interactivity and manipulability **;]** leading a profile folder in dictionary eTokin pairs iteratively **:>** split into a Key-Value pair, and "values" set in a list with more or less parameters applied to the profile.

The above dictionary eTokin pairs represent the "DNA-like" archival express with flexible sizes for rows (folders) and columns (more or less parameters). The dictionary pairs look like general data formats (XML/JSON), but are much more advanced than XML and/or JSON in light of machine learning automata implanted for UnIX through elastic process automation. The dictionary pairs are highly intelligent with AIM for actionability, interactivity and manipulability—under a specific context, the runtime situates machine learning rules in the context to fulfill semantic enrichment for informative delivery via wiseCIO, or integral content for online analytics over DATA. More importantly, text-based eTokin is the utmost core underneath in support of both seamless intercommunications and semantic enrichment among the CMD triad, as discussed afterward.

How a specific machine learning rule is situated under a given context will be thoroughly discussed next.

#### **3.3 Collaborative intercommunications among CMD triad**

Collaborative archive repository takes good CARE of Anything orchestrated as a Service via algorithmic machine learning, which establishes seamless intercommunications among three parties so that interoperability via joint tasking is made automated, interactive, and responsive (AIR).

Instant publishing takes initial CARE to prepare integral content under managed over DATA, and integral content enables wiseCIO to promote informative delivery. Furthermore, wiseCIO propels interoperability over joint tasks via innovative online analytical processing (iOLAP) for better user experience with DATA. As previously discussed, eTokin is text-based, and created as express tokens for information interchange to promote elastic process automation through Seamless intercommunications between distributed parties of the CMD triad incorporating data transmission with joint tasking .

The basic strategy applied to express tokens is to *suffice* with AIM at actionability, interactivity and manipulability, and *minimize* data storage without redundancy, *encrypt* networking transmission via cryptography. Text-based eTokin for seamless intercommunications has some similarities to, but is much more advanced than JSON, and/or XML [17]—intelligent ("DNA-like") ingredients are related to algorithmic machine learning without explicitly coding required. Consequently, CARE is expressed in text-based eTokin to incorporate AIM for actionability, interactivity and manipulability on UnIX.

One of the obvious examples is to situate a specific machine learning rule for a smartphone of a narrow screen, or a laptop of a wide screen, respectively. A smartphone may prefer a bulleted list (V-layout) to a multi-tab array (H-layout) for multiple profile folders in light of the width of the device screen.


#### **Table 3.**

*Contextual spontaneity in breadth for self-paced interest in browsing.*

**Table 3** shows ideas of how "DNA-like" eTokin (that is text-based across the CMD triad) turns out to be *context-sensitive* via semantic richments for informative delivery on wiseCIO, *context-innovative* via integral content management for online analytics over DATA, *context-neutral* via for seamless intercommunication for instant publishing by UnIX-CARE. The text-based eTokin and its equivalence UI Dictionary play a key role in human-computer interfacing for user-centric experience through elastic process automation without explicitly coding required. However, algorithmic machine learning is *context-specific* at runtime where iTOP embodies CARE in some ways by which instant typing online publishing supports: (a) text-based eTokin for content management in storage, online analytics, and machine learning rule-driven automation as well, (b) interactive editor in UI Dictionary without markups required so that everybody can perform UI design, and (c) bidirectional conversions between text-based eTokin and UI Dictionary.

#### **3.4 Machine learning-activated semantic enrichments**

UnIX-CARE, conceptualized as bridging between DATA and wiseCIO, has three "i" objectives in mind to advance *instant* publishing in "DNA-like" archival express, accumulate *integral* content, and aggregate *informative* delivery as a whole across the CMD triad. Machine learning helps to activate semantic enrichment from "DNA-like" archival express (eTokin) to wiseCIO for Anything orchestrated as a Service.

#### *UnIX-CARE: Universal Interface and Experience via Collaborative Archive Repository Express DOI: http://dx.doi.org/10.5772/intechopen.107443*

Superficially, "DNA-like" eTokin looks like traditional XML or JSON, but they would be in vain without intelligent service. "DNA-like" archival express in eTokin is not just utilized in data formats, but empowered by algorithmic machine learning where semantic enrichments orchestrate Anything as a Service with AIM for following characteristics:

*Actionability*—wiseCIO is created for informative delivery and actionability represents an ability to turn web document into a live website from deafness (no or less action) to dedication to servicing the users to act—an end-user to browse, a webmaster to administrate and/or a web designer to create cloud-based content under managed over DATA.

*Interactivity*—universal interface is automated on wiseCIO to enable users to communicate with the remote server to request, and/or to be prompted to react—the interactivity here is more than just to swap the current page to a new page via anchored tags, such as buttons, hyperlinks, etc. Algorithmic interactivity is made for active collaboration, friendly incorporation and rapid assembly or integration of Anything as a Service.

*Manipulability*—DATA is built up by accumulating various data in "DNA-like" archival express and manipulability aims for back-end operating, processing and, for instance, control over joint tasks for interoperability that composes smaller servicing parts into a larger service. Manipulability utilizes operating technologies (OT) on the remote server to support online analytics, and supply computing resources, and synthesize "Anything" as a Service—applied information technologies to intelligence for business, education and entertainment (iBEE).

Semantic enrichments through algorithmic machine learning provide AIM for automated UnIX that is to be actionable, interactive and manipulatable. Let us recap the profile folder for the 1st President of the USA: George Wahshington, then evolved from the simplest @FLDr( … )@ that represents a well archived folder to multiple folders (**Tables 1** and **2**). Each one of the profile folders is extensible and shrinkable by clicking the arrow button, which embodies successful semantic enrichment from @FLDr( … . )@ to be actionable, interactive and manipulable. With "DNA-like" archival express that is context-neutral, wiseCIO becomes smarter than ever to lay out in a bulleted list (V-layout) or multi-tab layout (H-layout), illustrated in **Table 3**.

The "DNA-like" eTokin-enabled semantic enrichments from a single profile to multiple folders maintain the core AIM for actionability, interactivity and manipulative, but as the number of folders is increased, both V-layout (for narrow screen) and H-layout (for wider screen) will not work properly, so the AIM should be empowered with new actionability, interactivity and manipulability by semantic enrichments, shown in **Figure 4**.

**Figure 4** addresses a much more intelligent solution to UnIX for the complex archival express through semantic enrichments. A foldable list is defined as a large number of profile folders and derived from both bulleted list and multiTab layout. The use case is about how to archive all 46 Presidents of the USA from the 1st President George Washington to 46th President Joseph R. Biden, Jr. Either V-layout (too high to scroll) nor H-layout (too wide to fit) would work well. A foldable list only occupies the screen space, the same size of a single folder, but the number of items in the list can be flexible, pretty large.

As a good example, a foldable list can be described by "DNA-like" eTokin that is context-neutral and empowered by semantic enrichments with AIM for actionability, interactivity and manipulability where algorithmic machine learning

#### **Figure 4.**

*"DNA-like" archival express empowered with a number of of profile folders.*

play a key part in seamless intercommunication and semantic enrichment among the CMD triad.

#### **4. iBEE via online analytical processing**

Collaborative archive repositories express promises to take CARE of integral content management (over DATA) and informative delivery (via wiseCIO) of intelligence for business, education and entertainment. Innovative online analytics has been utilized to support decision-making via machine learning patterns [18], as illustrated in **Figure 5**.

**Figure 5** shows UnIX-CARE that acts as double bridging between DATA for integral content management and wiseCIO for informative delivery, and between users and intelligence-driven Anything as a Service. Machine learning is central to context-neutral description in the "DNA-like" archival express that "implants" feasibility and flexibility for UnIX to become reasonable and possible through elastic process automation. The implanted flexibility makes it possible for wiseCIO to vary universal interface & experience without explicitly coding required, and the feasibility enables semantic enrichment with AIM for actionability, interactivity and manipulability. Furthermore, "DNA-like" archival express (eTokin) collaborates with DATA for integral content management and wiseCIO for informative delivery among the CMD triad as a whole as an activator of machine learning to orchestrate Anything as a Service.

#### **4.1 Innovative online analytics via elastic process automation**

A web-based cloud intelligent service may involve very complex scenarios in order to support a large variety of specific situations. The elasticity of automation represents a flexible and feasible process that is able to adjust and cover through specific scenarios while staying within the mainstream. Algorithmic processing, as part of machine

*UnIX-CARE: Universal Interface and Experience via Collaborative Archive Repository Express DOI: http://dx.doi.org/10.5772/intechopen.107443*

**Figure 5.** *CMD triad serves the user with UnIX for iBEE.*

learning, fulfills online analytics that computationally examines information to find useful patterns [13, 14, 18], so how to recognize the context under a specific situation is quite dependent on "DNA-like" archival express that becomes the foundation of innovative online analytics.

A pattern with parameters implies a particular way by which a piece of algorithm can be derived to get some job done and some content organized. Parameterization enables elasticity for procedural automation. **Table 3** explicitly illustrates a good example of elastic process automation as follows:

```
@FLDr( foldHead0 :> imgURL ,) videoID ,) emBody
;]foldHead1 :> imgURL ,) videoID ,) emBody
;]foldHead2 :> imgURL ,) videoID ,) emBody
;]foldHead3 :> imgURL ,) videoID ,) emBody
;] ... ...
)@
```
Where

**@FLDr** stands for a pattern providing a means in which a folder is presented. **Parameters** videoID denotes some elasticity of particular ways to play multimedia as embedded parts. wiseCIO is smart to play such multimedia as video, audio, traditional website, and anything via a URL that a browser can open. **More significant elasticity** is "trade-off" between V-layout, H-layout, and Foldable List of grouping profiles via algorithmic interactivity according to the view resolution.

Innovative online analytics through algorithmic interactivity represents parameterized solutions to content management and delivery via machine learning with

patterns. Semantic enrichment via algorithmic interactivity helps to vary for universal interface & experience as "One-Size-Fits-All".

#### **4.2 Business intelligence via automated processes**

Business intelligence is to utilize business data to drive decision making, which has become one of the significant objectives of intelligence via online analytics for business, education and entertainment (iBEE). In information technologies, wiseCIO takes CARE of intelligence to embody innovative online analytics through elastic process automation over DATA. In order to make decisions for business success trustworthily and dependably, reliable data is required to be IDEA (integral, digestible and elastically available).

"Business Intelligence" (BI) may be a generalized term, and it could be specialized for instructional / educational, or entertaining (business) intelligence, all of which is assumed to support decision making. Basically intelligence represents thinking ability, reasoning ability to understand and learn well in order to form judgments and opinions based on reason. The CMD triad propels computational thinking of "DNA-like" archival express for intelligence through elastic process automation.

According to the operational definition of computational thinking [11–13], computational thinking can be fulfilled in a feasible, operational and optimal approach. The CMD triad considers it done to get computational thinking through algorithmic problemsolving processes (shown in **Figure 5**) with operational activities such as: (1) by formatting problems "DNA-like" eTokin enables a computer to help solve those problems, (2) by analyzing data, "DNA-like" archival express establishes a transformational foundation over DATA, (3) by representing data through models and simulations, UnIX-CARE acts as a "fastlane" through elastic process automation, (4) by identifying, analyzing, and implementing possible solutions DATA aims for the goal of achieving the most efficient and effective combination of steps and resources, and (5) by generalizing a problemsolving process, wiseCIO transfers the liaison with UnIX to a wide variety of problems.

**Figure 6** Computational thinking via CMD triad is *feasible* via algorithmic interactivity to liaise with UnIX. It is also *operational* through elastic process automation and *optimal* for intelligence-driven decision making for Business, Education and Entertainment (iBEE). The highlight in terms of major contribution of CMD triad is applicable orchestration of Anything as a Service for decision-making, as detailed in Section 5.

#### **Figure 6.** *Computational thinking is feasible, operational and optimal via CMD triad.*

*UnIX-CARE: Universal Interface and Experience via Collaborative Archive Repository Express DOI: http://dx.doi.org/10.5772/intechopen.107443*

#### **4.3 Educational excellence via collaborative efforts in learning**

EXCEL—educational excellence via collaborative efforts in learning is considerably a specialized "business" that helps to excel education for student success. Generally instructional content under managed over DATA represents courseware design with aims for hybrid learning purposes, and instructional delivery via wiseCIO as intelligence to assist an instructor and/or students to make decisions on where, when, and how to browse in-depth hierarchy, or glance in-breadth context, and in-detail access, all of which assists to target educational excellence via collaborative efforts in learning (EXCEL).

UnIX-CARE strongly associate educational excellence with CIA-directed courseware presentation [14] of *Contextuality*, *Interactivity* and *Accessibility*: spontaneous contextuality enables exploring in breadth, sequential interactivity encourages browsing in depth, and sustainable accessibility enacts visitation in detail, which decisively promotes instructional engagement for student success, illustrated by **Figure 7**.

**Figure 7** presents a CIA-aided courseware via a cloud-based intelligent service to promote educational excellence for student success. The courseware of CIA propels comprehensive engagement in a hybrid instructional approach throughout: (a) contextuality in breadth to meet the spontaneous needs of individuals to overview the content, (b) interactivity in depth to dedicate students sequentially through learning process by one after another, and (c) accessibility in detail to incorporate sustainable advancement with individual coursework published as profiles,

CIA-aided courseware is organized and discussed in following aspects:

*Spontaneous contextuality* is embodied by the top-folder bar that organizes multiple aspects beneath via a multiTab so that individuals can spontaneously explore with self-paced interests—a student may first glance at what is about the course he/she

**Figure 7.** *CIA to excel education via contextuality, interactivity and accessibility.* is to take. Individual users'spontaneity helps to get rid of "monotony" by allowing the individual to go on self-paced interests without any boredom.

*Sequential interactivity* aligning on the left layout presents major learning modules sequentially so that both instructors and learners can follow with lectures & labs, and coursework as the class goes on. Algorithmic interactivity with each learning module serves learners in browsing in depth via fold-out (going down into) and fold-up (getting out of)—collaboratively only one module is allowed in fold-out at a time, and the other will automate in fold-up.

*Sustainable accessibility* is reflected by the bottom-folder bar with an intermediate media where all students have their own profile-boxes for coursework submission—a student may access to his/her own profile folder, or utilize instant typing online publishing (iTOP) to submit coursework according to the sequential learning paces; an instructor has the privilege to view, grade and interact hybridly with individuals for review & revision, and advancement (R<sup>2</sup> A-rising to grade A).

CIA-aided courseware acting like a "mirror" reflects educational intelligence to assist hybrid learning with collaborative efforts to engage for student success: educational excellence is embodied in self-paced interests via spontaneous contextuality, indepth learning via sequential interactivity, and in-details via sustainable accessibility between instructor and learners.

#### **4.4 Netflix-like movie entertaining reactivator**

Netflix is an American subscription streaming service and production company based in Los Gatos, California. Netflix can be accessed via web browsers or via application software installed on smart TVs, set-top boxes connected to televisions, tablet computers, smartphones, digital media players, Blu-ray players, video game consoles and virtual reality headsets on the list of Netflix-compatible devices. As a simulating service case, Netflix-like movie entertaining reactor (netFlyer) basically acts like Netflix to offer a film and television series library through distribution deals as well as its own productions. UnIX-CARE is presented here to provide users with the universal interface & experience by ultimately archiving all kinds of multimedia. With UnIX-CARE through the CMD triad as a whole, a Netflix-like reactivator performs integral content management over DATA and informative delivery via wiseCIO to enable contextuality in breadth for self-paced preview, and hierarchy in depth for serious movie watching. Self-paced preview acts as a heads-up on what a user wants to see, and serious movie watching means that security levels can be applied to manage and control accessibility for commercial purposes. As a vivid example, the hierarchical depth, multimedia at the higher level is more general and cheaper, and at a lower level, more special for higher profit, etc.

**Figure 8** illustrates a netFlyer service (NEMs) that presents a well-categorized archive: Multimedia Center (preview for free), Cartoon 2022 (for kids pleasure), c) Disaster 2020 (for shocking experience), etc. The netFlyer is well archived with multimedia as much as possible, but organized as neatly as novel to offer a set of universal interface that assists users to "*learn once for all*", and to prompt user-centric experience without often web page swapping. The netFlyer may go in such scenarios as a user explore: self-paced preview for free in contextual breadth, and "in-&-out" interaction for subscribers in hierarchical depth. Both free explorers and subscribers will enjoy previewing, watching videos, playing games, and so on, which demonstrates individualization and orchestration of Anything as a Service under netFlyer.

*UnIX-CARE: Universal Interface and Experience via Collaborative Archive Repository Express DOI: http://dx.doi.org/10.5772/intechopen.107443*

**Figure 8.** *netFlyer entertaining service for UnIX via contextuality and interactivity.*

More intelligently, a dynamic dropdown list is an algorithmic companion to the banner (say, Comedy 2022) as the user opens it to watch or preview items of multimedia, which automatically assembles a dropdown list that enables rapid accessibility without needing to re-open the same banner. Furthermore, it best embodies usercentric experience that the user opens and/or closes banners alternatively without leaving the current context, which is really beneficial to users. In particular netFlyer works perfectly for the user to enjoy by exploring a "oceanic" number of multimedia.

UnIX-CARE provides a fastlane for new multimedia to be published dynamically in "DNA-like" archival express to DATA and to explore via wiseCIO. Innovative online analytics is applied to netFlyer via machine learning as illustrated in **Figure 5** in which machine learning plays a key role in elastic processes automation for business intelligence to support decision making.

#### **5. Quinary XaaS orchestration**

Universal interface & experience (UnIX) represents a novel model for content management and delivery (CMD), and collaborative archive repository express (CARE) incorporates DATA into wiseCIO to orchestrate Anything as a Service. Quinary UnIX suggests quinary cases of Anything orchestrated as a Service based on the CMD triad as a whole to promote *instant* publishing, *integra*l management, and *informative* delivery. Quinary UnIX provides quinary (five) servicing templates with aims at queryability, ubiquity, interactivity, novelty and availability.

**Figure 9** addresses Quinary Servicing Templates described in the "DNA-like" archival express as context-neutral in wise Dictionary and/or UI Dictionary in light of

#### **Figure 9.**

*Wise (eTokin) dictionary takes CARE of web development over DATA.*

seamless intercommunication. Algorithmic patterns [16] apply semantic enrichment to turn wise (eTokin) Dictionary into context-sensitive Quinary UnIX. Deep learning enables universal interface design of context-sensitivity through elastic process automation. Consequently, context-sensitivity results from context-neutrality via algorithmic interactivity of REAP- retrievable (from the remote server), executable (on the client device), analytical (elastic automation), and pass-along (between wise eTokin and UI Dictionary).

Machine learning, according to IBM Cloud Learn Hub [8, 9], is a branch of artificial intelligence (AI) through computational thinking, which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. In this article, thorough discussion will be conducted with heuristics that are used to initiate machine learning through following characteristics, respectively.

**Queryability:** word-driven aggregation (5.1) **Ubiquity:** digital music avocation in composition (5.2) **Interactivity:** montage-selected animation (5.3) **Novelty:** computing machinery assembler of programmability (5.4) **Availability:** customizable name-featured activation (5.5)

#### **5.1 WDA: word-driven aggregation for queryability**

WDA represents the first XaaS template that initiates heuristics of word-driven queryability for universal interface & experience (UnIX) on **comprehensive aggregation**. WDA promotes queries to draw users' attention to categorized words: synonymous or opposite, which strategically encourages flexible queries [19] by applying heuristic wording to facilitate the extraction of relevant word-related DATA for presentation.

**Figure 10** illustrates how WDA heuristics can be applied to query for variously relevant words, such as LOVE, AGAPE, LIKE, HATE, … For instance, associating LOVE with a category of loving movies, a user drags the letter "L" lower than "E", which may lead less love ("LIKE") to series of multimedia. On the contrary, the letter "L" becomes higher than "E", which may lead to sacred love ("AGAPE") series of

*UnIX-CARE: Universal Interface and Experience via Collaborative Archive Repository Express DOI: http://dx.doi.org/10.5772/intechopen.107443*

**Figure 10.**

*Heuristic queries aggregated by WDA for wording guess.*

multimedia. Inversive "LOVE" is "EVOL", which may lead to the "HATE" (opposite) series, etc.

**Queryability** expresses something unsure of "what to explore" while a user is browsing a new / complex website. Therefore the wording guess heuristically encourages the user to ask for more information specific in his/her mind. Query heuristics can help the user into a specific field to explore, say APAGE series of multimedia. Machine learning is nothing to do from scratch, but provides a means of solving problems by discovering things itself and learning from its own experience. So an initial wording guess is a great heuristics to enable the user in proceeding with further exploration.

WDA showcase serves with a heuristic wording guess, then engages the user to go further and explore in more depth. In facing a giant number of entertaining resources, the wording heuristics becomes especially effective while exploring entertaining multimedia.

In cloud-based applications, WDA encourages queryability through active exploration to discover things that are interesting to the user from his/her own experience for individual pleasure in contextual breadth and enjoyment in hierarchical depth.

#### **5.2 DMA: digital music avocation for ubiquity in composition**

DMA represents the second XaaS template that initiates heuristics of digitallyadvocated ubiquity for universal interface & experience (UnIX) on **creative composability**. DMA prepares ubiquitous compositions anywhere with an iPAD or a laptop available and it does not matter with or without a musical instrument at hand. In general composability is a business principle that refers to the ability to combine modular business elements as needed [20, 21]. A simple DMA will do the trick on an iPAD or a laptop to foster early-age musical education for little kids to recognize music notes and perform music composition for fun. It is also helpful for them to discover their talent in composing music through coding and creativity Vividly a kid does compositions of a "song" by selecting and putting musical notes into a queue and he/she can also make chords (playable at the same time) by putting two or more musical notes into the same position in the queue so as to play simultaneously.

**Figure 11** illustrates how DMA orchestrates Anything as a Service includes "Songs" to play, "Chords" to make, and "Keyboard" to play the musical songs. The XaaS composer for DMA initiates with musical notes that can be play individually by clicking the key on the Playable Keyboard, and a kid can also click the speaker (button) to play a reserve song, or make a chord by grabbing two or notes to add on to the base notes so as to play simultaneously, and so on. A little kid would be pleased to learn how to compose a song by purchasing a piano, but it is apparently so expensive before his/her parents could find out whether or not the kid is interested in musical

#### **Figure 11.**

*Ubiquitous composer dashboard with songs, chords, and notes.*

composition. Obviously, a simple DMA on iPAD or a laptop will be helpful to foster the kid in musical composition.

**Ubiquity** in composing makes "piano" everywhere available for musical composition that particularly embodies potential production or creation of music, poetry, or formal writing. Ubiquity via DMA aims to foster coding and creativity through computational and compositional activities.

DMA showcase serves Anything as a Service with a web-based keyboard that is made anywhere available to foster little kids to taste by composing songs, making chords with super ease. The heuristic virtual keyboard would be the first teacher to help kids recognize musical notes, try chords in direct experience of what a chord really means. It can also help code in practice, test in performance, and revise in progress to inspire creativity through programmable composition.

#### **5.3 MSA: montage-selected animation for interactivity**

MSA represents the third XaaS template that initiates heuristics of montageselected interactivity for universal interface & experience (UnIX) on **vivid and friendly animation**. MSA promotes both manual and robotic operatings for humancomputer interfacing. Manual operations serve the user who gives it a try to test and preview animations, and robotics automates a process of multimedia to play and as it goes, the user can intervene the animation to stop and then enter the montage related content. A selected montage frame provides a means to control an animated asset that enables combinational animation sequences into a single asset. In a looping mantaged series, the user can view it like a movie until he or she breaks it up into sections for playback [22]. An animated montage series is conceptualized for operating interactivity to express human-computer interfacing automation.

**Figure 12** illustrates how a montage series plays slowly until the user finds his / her interest in the category of multimedia to play and browse in more depth. Let us use Mickey Mouse as an example. A little boy is watching the slowly-playing montage series from "Kung Fu Panda" to "Thomas Train". He could try to click the montage "Donald Duck", then he would be taken to the category underneath and stay to watch more he likes. He can also be back up to the montage series by clicking "Duck" via "in- &-out" interactivity.

**Interactivity** is animated to engage the user in the exchange of information between cloud-based Anything as a Service, and client devices, e.g., smartphone, tablet, laptop, and/or computer. The exchange of informative montage series is

*UnIX-CARE: Universal Interface and Experience via Collaborative Archive Repository Express DOI: http://dx.doi.org/10.5772/intechopen.107443*

**Figure 12.** *Animated montage series promotes operating interactivity.*

animated to control robots, play multimedia theater through robotic process automation, and so forth. In particular, MSA helps to enrich interactivity via wiseCIO to engage users with their exploration of entertaining services without boredom.

Everytime, when rendering a traditional website on the client screen, the view is almost the same as navigating header and/or footer, then a user has to scroll up/down to find a section of his/her interests in. The MSA prepares heuristics and/or visual "montage" via tab-based multi-sections to present preview animatedly until the user hits the section for better user experience.

The MSA supports human-computer interfacing via UnIX with heuristic scenarios to direct the user to preview primary categories of content, and which one to choose is quite customized.

#### **5.4 ACM: assembled coding machinery for novelty in programming**

ACM represents the fourth XaaS template that initiates heuristics of computing machinery-assembled novaly for universal interface & experience (UnIX) on **computational thinking and programmability**. ACM is a simulator of coding to promote programmable user interface design, similar to, but different from MSA. While montage series in play may be sequential, Assembled machinery plays a series in programmable order, and maybe with choice-making. It utilizes an assembly-like language to create new apps in a visual approach, so an instruction is encoded as an actionable token that consists of at least three elements: (a) a number (code), (b) a wording description (action), and (c) a visual illustration, such as an animated GIF, a video, or an audio. ACM encourages users to create their own instruction set from which they can program fun stories or scenarios in a programmable (sequential and selective) approach.

**Figure 13.** *Programmable series promotes operating novelty.*

**Figure 13** illustrates how a programmable series plays slowly until the user finds his / her interest in the category of multimedia to play and browse in more depth. The upper part represents ACM, and the lower part extends the illustrative picture related folder. Let us use Crying Posture as an example. A user programs a series, tests and performs the slowly-playing programmable series according to the program at the running. The user could try to catch an illustrative picture or description of "CRY", then by clicking he would be taken to the category underneath and stay to watch more he likes. He can also get back up to continue the programmable series via "in-&-out" interactivity. The ACM program is developed in an actionable tokens that relates visual illustrations dynamically, so program executing will produce a cartoonish movie that is runnable, presentable, playable, and programmable (rPPP).

**Novelty in programming** of data path processing is a universal feature in virtualized networks [23]. A given instruction series is executable through sequential (*one step after another*), and/or selective (*one or the other*) order for the sake of instructional teaching through computational thinking of programmability. Furthermore, the user can enhance the existing instruction set, or create a fully new instruction set for the coding machinery.

Theoretically, ACM showcase serves as a virtual coding machinery that supports coding algorithms by using the instruction set, which encourages coding and creativity to problem-solving in programmability.

Also ACM can enable rapid prototyping and responsive assembly from the wellcategorized multimedia to help users explore various scenarios for kids, adults, and so on.

#### **5.5 NFA: name-featured activation for availability in customizing**

NFA represents the fifth XaaS template that initiates heuristics of name-featured availability for universal interface & experience (UnIX) on **customization or**

#### *UnIX-CARE: Universal Interface and Experience via Collaborative Archive Repository Express DOI: http://dx.doi.org/10.5772/intechopen.107443*

**individualization**. NFA prioritizes customizable availability based on individual names, which denotes heuristics to encourage users to explore entertaining multimedia, such as audios or videos without boredom. By inputting the user's name the customizable availability gets started—the name becomes a key in ASCII to trigger a group of multimedia for preview until the user chooses to intervene.

**Figure 14** illustrates how a customizable series is made available to relate users' interest in the category of multimedia to play and browse in more depth. The upper part represents the NFA section that allows the user to input the name and whose ASCII series is used to draw specific background color, and the lower part denotes multimedia list to play. Based on a user when he / she input the name, the random availability is triggered toward a group of multimedia (list) for preview.

**Customizable Availability** represents adjustment making responsively to accommodate a users' individual needs for the sake of better user experience engaging the user with something new via cloud-based Anything as a Service. Most traditional websites start with a search port in addition to header and footer for further explorations in breadth. It is very beneficial for a new-hamd user to start with what to search and where to start. As initial heuristics, letters of a given name in ASCII are combined to bring out the customizable content for the user to get started with great ease.

Psychologically NFA showcase provides a customizable preview on the primary category of grouping content. According to Psychology Today—Hello, My Name is Unique [24], "Some parents want names for their children that are unique but not too trendy. Other parents seem to love alternative spellings. How important is a name to our self-perception?" A unique and special name will heuristically lead to pleasant experience while a user exploring entertainment through multimedia.

**Figure 14.** *Name-featured activation promotes customizable availability.*

NFA aims for responsive adjustment over multimedia grouping to accommodate a customer's particular needs for better user experience that encourages engagement without boredom.

#### **6. Conclusion**

UnIX-CARE or Universal Interface & Experience has emerged from Collaborative Archive Repository Express (CARE) that collaborates integral content over DATA with informative delivery via wiseCIO through algorithmic machine learning. Conceptualized as a "fastlane" into the CMD triad, CARE provides mathematical and computational solutions to achieve following UNIX objectives:

*Ubiquitous Manager* is everywhere across the CMD triad to harness *comprehensive* information for business, education and entertainment (iBEE), and to propel *composite* assembly of anything as a service (XaaS). With which the "controversial" agendas among IT personnel [5] have been resolved so that an ordinary end-user can be made a webmaster, a web designer and/or an extraordinary user while browsing in hierarchical depth via "In-&-Out" interactivity, and exploring in contextual breadth via self-paced spontaneity [14] without overwhelming, as discussed in Section 2.

*Novel Designer* takes CARE for universal interface design and user-centric experience by instant typing online publishing (iTOP) via express tokens for information interchange (eTokin) differing from traditional XML and JSON [17]. In which iTOP, assisted by algorithmic machine learning, presents universal interface design that advances the aims at user-centric experience without explicitly coding required, as deeply studied in Section 3.

*Intelligent Expert* represents one of the CMD goals to aggregate intelligence for business, education and entertainment in support of decision-making. The CMD triad is collaborated with integral content over DATA and informative delivery on wiseCIO. Where digital archiving ensures integral content under managed by DATA, and intelligent service serves informative delivery by wiseCIO throughout elastice process automation with algorithmic machine learning [8, 9], as presented in Section 4.

*Extraordinary Liaison* facilitates human-computer interfacing via eTokin to simplify collaborative communications without rendering related redundancies, but semantic enrichment that suffice to orchestrate Anything as a Service with machine learning patterns through elastic process automation [18], as discussed as Quinary XaaS in Section 5.

#### **7. Visible accomplishments**

This article presents following critical advancements technologically and practically through multiple best efforts to pave comprehensive roadmaps toward the above accomplishments:

**Novel Triad** provides comprehensively innovative solutions to cloud-based distributed problems for Anything as a Service that involves Automated interfacing Design (AiD for various users) via UnIX-CARE, Proactive online Analytics (PaA) over DATA, and User-centric Experience (UcX) via wiseCIO for the sake of capability of intelligence for Business (Section 4), Education (4.3-CIA) and Entertainment (4.4-Netflix-like Movies).

*UnIX-CARE: Universal Interface and Experience via Collaborative Archive Repository Express DOI: http://dx.doi.org/10.5772/intechopen.107443*

**Challenges (versus Chances)** on serving "controversial personnel" [5] turn from *controversial* agendas into *cohesive* advancement on the basis of seamless intercommunications (*context-neutrality*) among the CMD triad and semantic enrichments ( *context-specialty*) through algorithmic machine learning, which propels large teams united and working together effectively. Algorithmically with practical methods implemented as intelligent services, the CMD triad assists to empower users to be cohesive professionals: like a webmaster over DATA, an interface designer via UnIX-CARE, and an intelligent expert on wiseCIO to discover useful and usable information in support of decision-making.

**Archival Express via eTokin** for information interchange among CMD triad succeeds with a firm foundation for interoperability, the top level characteristics of networking applications, that enables orchestration of Anything as a Service. As an essential backbone, seamless intercommunication upgrades three CMD parties up to interoperability to ensure Anything-orchestrated as a Service, thoroughly discussed in Sections 4 and 5.

#### **8. Future work & practice**

In addition to feasible and visual accomplishments, there will be more efforts to make as future work in comprehensive practice as follows:


XaaS across platforms (iOS, Linux, Windows), and browsers (Chrome, Safari, Firefox, Microsoft Edge, and Opera, etc.)

### **Acknowledgements**

This work is partially supported by Department of Education-MESIP Award P120A180072 subaward 161206PMJ157 to M.V.S., National Science Foundation HRD 201138, and Apple- HBCU C2—Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Department of Education or National Science Foundation. Our sincere thanks to HBCU C<sup>2</sup> Partnership with Apple and Tennessee State University, *Dr. Robinson Blackman-* Senior Program Executive Director, HBCU C2 Project), *Dr Aminah F. Gooch*- Director of Lane Summer STEM Research Academy and Summer Interns: *Armon White, Innocent Munezearo, Jayleel George, Malcolm Little, and Mohamed Fall* for their contribution included in the article. Special thanks to *Dr Patricia LaGrow* (former Associate Provost of the University of Central Oklahoma, Edmond, OK) for her inspirational encouragement when needed and descriptive wording and writing. Last but not least, I am deeply thankful to Angela Hua for her always-encouragement and love of wiseCIO (!).

### **Author details**

Sheldon Liang<sup>1</sup> \*, Melanie Van Stry<sup>1</sup> and Hong Liu<sup>2</sup>

1 Lane College, a HBCU Institution, Jackson, Tennessee, USA

2 Embry-Riddle Aeronautical University, Daytona Beach, Florida, USA

\*Address all correspondence to: sliang@lanecollege.edu

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*UnIX-CARE: Universal Interface and Experience via Collaborative Archive Repository Express DOI: http://dx.doi.org/10.5772/intechopen.107443*

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