**1.5 Research contribution**


**49**

**Table 1.**

**Figure 1.**

*Bigdata integration platforms.*

EII Enterprise Information Integration

HIPAA Health Insurance Portability and Accountability Act

IoMT Internet of Medical Things SOC 2 Service organization control is an auditing procedure

*List of acronyms used in this chapter.*

*Big Data Integration Solutions in Organizations: A Domain-Specific Analysis*

6.Research issues, challenges and future directions of BDI technologies are

7.A case study of five BDI based solutions implemented healthcare, retail,

8.The set of a curated survey papers are compared with specific factors such as architecture, open issues and challenges, applications, taxonomy and security to understand the scope of coverage each paper and to understand the research

This section shows a list of all the acronyms used in this chapter for easy refer-

**Acronym Description Acronym Description** AI Artificial Intelligence BDI Big Data Integration

AWS Amazon Web Services AIG American International Group B2B Business to Business CAGR Compound Annual Growth Rate CCTV Closed Circuit TV ETL Extract, Transform, Load

IoT Internet of Things MDM Master Data Management

EDR Enterprise Data

ICT Information and

Replication

API Application Programming Interface

Communication Technology

*DOI: http://dx.doi.org/10.5772/intechopen.95800*

finance and tourism domains are discussed

discussed

gaps **Tables 2** and **3**

*1.5.1 Table of acronyms*

ence is presented **Table 1**.

*Big Data Integration Solutions in Organizations: A Domain-Specific Analysis DOI: http://dx.doi.org/10.5772/intechopen.95800*


## *1.5.1 Table of acronyms*

*Data Integrity and Quality*

customer domains.

Cloud Platform.

reviewed.

**1.5 Research contribution**

applications

**Figure 3**.

presented as a pie chart in **Figure 1**.

several issues as shown in **Figure 4**.

2.A table of acronyms is presented in **Table 1**

*1.3.3.12 SAP products - data services*

**1.4 Organization of the chapter**

The company's product portfolio includes services and technologies that permit organizations for data enrichment and full lifecycle data movement. Oracle data integration allows permanent and uninterrupted access to data across heterogeneous systems via transformation, bidirectional replication, bulk data movement, data services, metadata management, and data quality for product and

SAP provisions clouds and on-prem integration functionality by two primary

This chapter has been framed into seven sections. Section 1 explains the introduction, sub section 1.1 discusses the motivation and significance of the study. Sub section. 1.2 shows international market potential and 1.3 presents an overview of big data technologies and taxonomy. Sub section 1.4 Organization of the chapter, 1.5 summarizes the authors' research contribution. 1.6 Illustrates the list of acronyms used. The review of recent literature is described in Section 2. Section 2 is further divided into four subsections. 2.1 subsection describes the papers reviewed from one technology domain. The highlights and findings from each paper are tabulated. Sub section 2.2 deals with a comparative analysis of survey papers with specific parameters. Section 3 shows the architecture of BDI. 4th section deals with the research issues, challenges. Section 5. Presents the case studies of BDI solutions from various organizations. Section 6. Outlines the findings and conclusion. Section 7 is the references of the papers

1.This study has revealed that various technologies, systems, techniques,

algorithms are applied for implementing business intelligence systems across the world. These papers have been further classified technology-wise and

3.**Figure 2** presents taxonomy of concepts applied in BDI techniques in various

4.The overview of the organization of this chapter, section-wise is shown

5.In each concept of taxonomy, the existing literature has been mapped to

channels. Traditional capabilities are provided through a data management platform, SAP Data Services, that gives capabilities for data cleansing, integration, and quality. SAP Cloud Platform provides Integration Platform as a Service features are existing in it. Integration of processes and data between cloud apps, third-party applications, and on-prem solutions are arranged through SAP's

**48**

This section shows a list of all the acronyms used in this chapter for easy reference is presented **Table 1**.

#### **Figure 1.** *Bigdata integration platforms.*


#### **Table 1.**

*List of acronyms used in this chapter.*

#### **Figure 2.**

*Taxonomy of big data concepts.*

#### **Figure 3.** *Organization of the chapter.*

**51**

**Table 3.**

**2. Review of recent literature**

*Business sector review summary.*

Authors have selected papers from highly reputed research journals from IEEE, Elsevier, Science Direct and Springer publications. About fifty-five papers

*Big Data Integration Solutions in Organizations: A Domain-Specific Analysis*

**Ref. No. Methods Used Results Applications**

data.

The principal problems identified during the study are the hardship of administration, the ineffectiveness of human resource, politics, standards and absence of executives

The decorum status of high recognition of positive viewpoint is around 65%. Negative perspective has an absence of etiquette knowledge accounted for 56%

Semantic extract transform load system produces semantic information that would possibly be distributed on the web as Web of

Big data problems are resolved by appropriate BDI methods.

A set of open research questions such as scalability of data required in

> It is possible to integrate technologies to the need of SMEs

Shortage of machine learning examples, the requirement of clarifying business owners' outcomes and the expense of involving domain experts.

A prototype of a data integration

BDI implementation in business organizations improves business

As per the study, 63% of business reported that the implementation of big data is useful to business

real-time applications

**Ref. No. Methods Used Results Applications**

system

performance

Data fusion solutions for public sector

Talent refinement of college students

Innovative Big data applications in fuel economy, household transportation and

Open cross-domain

Labor agency, Online gambling operations, Public Safety, and Predictive policing

> Small and Medium-sized organizations

Scalable data integration challenges in the enterprise

E-Commerce Domain

Performance improvement in business organization

Decision making in business

vehicles

Big data

*DOI: http://dx.doi.org/10.5772/intechopen.95800*

1 Interviewing of experts and content analysis approach was used as a qualitative technique for data gathering.

> experiments and questionnaires

Resource description system, SPARQL, semantic query language

industrial needs and potential applications of BDI in the public sector

6 Review of literature on BDI,

7 Developing a deployable data

technical issues.

8 The amalgamation of diverse

9 A study on data collection,

benefits of BDI

10 Randomly selected articles on big

Computing

Business Intelligence and Cloud

integration tool that handles

sources in the Hadoop environment with HDFS. Spark computation model with a Hive database as a distributed data warehouse

analytics implementation, and

data are reviewed to analyze the role of big data in business

2 Comparisons,

3 Semantic data model,

4 Exploration of BDI based on stage, features and semantic meaning

5 A study on analysis of

*Public sector review summary.*

**Table 2.**

**Figure 4.** *Sector-wise reviewed papers.*


*Big Data Integration Solutions in Organizations: A Domain-Specific Analysis DOI: http://dx.doi.org/10.5772/intechopen.95800*

#### **Table 2.**

*Data Integrity and Quality*

**50**

**Figure 4.**

**Figure 3.**

*Organization of the chapter.*

**Figure 2.**

*Taxonomy of big data concepts.*

*Sector-wise reviewed papers.*

*Public sector review summary.*


#### **Table 3.**

*Business sector review summary.*

## **2. Review of recent literature**

Authors have selected papers from highly reputed research journals from IEEE, Elsevier, Science Direct and Springer publications. About fifty-five papers covering big data integration concepts and applications are reviewed. This section presents the findings and highlights from each reviewed paper which are organized domain-wise.

#### **2.1 Review of the public sector**

Hasliza et al. analyzed the fundamental problems and difficulties encountered by the BDI solutions in the public sector [1]. The discovery of the right dimensions and factors are important to find the solutions to these problems. Zhang has reported the BDI solutions for professional procedure amalgamation in modern decorum [2]. The comparisons, experiments and questionnaires concerning the BDI concepts are discussed. Bansal has proposed the use of semantic technologies for the distribution of information in the contest of semantic ETL [3]. This information is open and the data was gathered from various sources. Zheng et al. have presented significant standards, classification of strategies and models of BDI process [4]. The real BDI issues are discussed using these models.

Authors have classified BDI techniques based on different combinations of strategies such as stage, feature and semantics. Munne has explained the technological trends for current social and economical status. This paper highlighted BDI technologies and applications in the public sector [5]. **Table 2** presents a summary of the highlights of the papers reviewed the public sector.

#### **2.2 Review from business sector literature**

The study by Camargo et al. revealed the possibility of incorporating and implementing BDI technologies to the needs of small and medium scale enterprises [6]. Some companies are offering open source BDI tools for organizations for intelligent business decision making. Stonebraker et al. discussed the difficulties in the scalability of BDI solutions today and in near future [7]. This analysis was carried out using the past five years' data from large enterprises. The integration of data from heterogeneous sources in a distributed environment was explored by Sazontev et al. [8]. The authors have explained the process of BDI framework development and its methodology. Alsghaier et al. discussed BDI process in Hadoop platform for business organizations. Authors focused on the implementation and benefits of big data analytics in business organizations [9].

Alam et al. have reviewed the role of BDI in the business sector [10]. **Table 3** presents a summary of highlights from the business sector review.

#### **2.3 Review of the finance sector**

Fikri et al. presented a BDI approach combined with distributed datasets of financial ontology and a real-time data stream [11]. This model was associated with classic ETL. This model was suitable for handling BDI in real-time. Bucea-Manea-Tonis illustrated the use of predictive logic in deductive frameworks to integrate different sets of data types [12]. Chen et al. have proposed a framework for managing data with heterogeneity problems [13]. This unified data model was adaptable to different data sources by setting up panoramic data. Hussain and Prieto discussed the analysis of industrial needs, constraints and potential applications of BDI to insurance and finance sectors [14]. Authors have mapped the requirements to research queries. The paper by Avi and Kamaruddin reported the role of BDI in insurance, finance and banking sectors [15]. Authors have highlighted the benefits of cutting-edge technologies associated with BDI in the financial sector. **Table 4** presents a summary of highlights from the finance sector review.

**53**

sector review.

**Table 4.**

*Big Data Integration Solutions in Organizations: A Domain-Specific Analysis*

**Ref. No. Methods Used Results Applications**

types

The data integration pipeline in real-time. The use of Apache Spark enhances short time frames for quality and availability reporting

Integrates different kinds of data

Better performance in processing data integration from

Highlights the challenges in providing an effective technological solution

Various business problems are solved by using the latest technological trends and big data analytics in the banking

multiple sources

Data integration in realtime, Financial reporting

Power dispatching and control system

Big data analytics for the banking industry

E-Commerce applications

Manipulation recognition, threat management in finance and insurance sectors

*DOI: http://dx.doi.org/10.5772/intechopen.95800*

datasets of financial ontology and real-time

deductive systems

needs, constraints and

of the banking sector in terms of digital banking, analytics, mobile banking. Use cases of the latest technologies in the banking sector

13 Integrating heterogeneous data from multiple sources, Big data ETL in a distributed environment

14 A review of industrial

application

15 A comprehensive review

11 Combining distributed

stream

12 Predicate logic in

**2.4 Review of agriculture sector**

*Finance sector review summary.*

BDI and data analytics concepts are emphasized by Nabrzyski et al. [16]. This proposed solution incorporates the execution of complex queries on various datasets. These data sets contain the layers of raster and geospatial data. Kim and Tam (2020) have proposed a data integration estimator [17]. This is a classification technique with non-parametric and overlapping units which recognizes and corrects misclassification errors. Saggi and Jain (2018) have reported a data analytics solution for organizations [18]. This solution performs an exhaustive realistic analysis. The components of BDI application platforms are discussed. Authors have

industry

Ribarics (2016) explained the importance of big data in agricultural sector [19]. The author has highlighted the need for using technological innovations in farming.

The study results showed that big data analytics helps the farmers in crop management and yield forecasting. This study also revealed that BDI in farming is not fully established. **Table 5** presents a summary of highlights from the agriculture

Kaur and Kushwaha (2018) were motivated by different applications of BDI and IoT integration in smart cities [21]. The earlier researchers reviewed the critical data analysis issues. Huang et al. (2014) have proposed HiperFuse solution for addressing BDI challenges and automating the BDI process [22]. Nuaimi et al. (2015) reviewed the prospects, issues and advantages of BDI in smart cities. This study discussed the BDI challenges faced in smart cities [23]. Gomes et al. (2016)

thrown light on past, current research issues and future directions.

**2.5 Review of literature on BDI in smart cities**

Sarker et al. (2020) discussed the impact of BDI in digital farming [20].


*Big Data Integration Solutions in Organizations: A Domain-Specific Analysis DOI: http://dx.doi.org/10.5772/intechopen.95800*

**Table 4.**

*Data Integrity and Quality*

nized domain-wise.

**2.1 Review of the public sector**

real BDI issues are discussed using these models.

**2.2 Review from business sector literature**

data analytics in business organizations [9].

**2.3 Review of the finance sector**

of the highlights of the papers reviewed the public sector.

covering big data integration concepts and applications are reviewed. This section presents the findings and highlights from each reviewed paper which are orga-

Hasliza et al. analyzed the fundamental problems and difficulties encountered by the BDI solutions in the public sector [1]. The discovery of the right dimensions and factors are important to find the solutions to these problems. Zhang has reported the BDI solutions for professional procedure amalgamation in modern decorum [2]. The comparisons, experiments and questionnaires concerning the BDI concepts are discussed. Bansal has proposed the use of semantic technologies for the distribution of information in the contest of semantic ETL [3]. This information is open and the data was gathered from various sources. Zheng et al. have presented significant standards, classification of strategies and models of BDI process [4]. The

Authors have classified BDI techniques based on different combinations of strategies such as stage, feature and semantics. Munne has explained the technological trends for current social and economical status. This paper highlighted BDI technologies and applications in the public sector [5]. **Table 2** presents a summary

The study by Camargo et al. revealed the possibility of incorporating and implementing BDI technologies to the needs of small and medium scale enterprises [6]. Some companies are offering open source BDI tools for organizations for intelligent business decision making. Stonebraker et al. discussed the difficulties in the scalability of BDI solutions today and in near future [7]. This analysis was carried out using the past five years' data from large enterprises. The integration of data from heterogeneous sources in a distributed environment was explored by Sazontev et al. [8]. The authors have explained the process of BDI framework development and its methodology. Alsghaier et al. discussed BDI process in Hadoop platform for business organizations. Authors focused on the implementation and benefits of big

Alam et al. have reviewed the role of BDI in the business sector [10]. **Table 3**

Fikri et al. presented a BDI approach combined with distributed datasets of financial ontology and a real-time data stream [11]. This model was associated with classic ETL. This model was suitable for handling BDI in real-time. Bucea-Manea-Tonis illustrated the use of predictive logic in deductive frameworks to integrate different sets of data types [12]. Chen et al. have proposed a framework for managing data with heterogeneity problems [13]. This unified data model was adaptable to different data sources by setting up panoramic data. Hussain and Prieto discussed the analysis of industrial needs, constraints and potential applications of BDI to insurance and finance sectors [14]. Authors have mapped the requirements to research queries. The paper by Avi and Kamaruddin reported the role of BDI in insurance, finance and banking sectors [15]. Authors have highlighted the benefits of cutting-edge technologies associated with BDI in the financial sector. **Table 4**

presents a summary of highlights from the business sector review.

presents a summary of highlights from the finance sector review.

**52**

*Finance sector review summary.*

### **2.4 Review of agriculture sector**

BDI and data analytics concepts are emphasized by Nabrzyski et al. [16]. This proposed solution incorporates the execution of complex queries on various datasets. These data sets contain the layers of raster and geospatial data. Kim and Tam (2020) have proposed a data integration estimator [17]. This is a classification technique with non-parametric and overlapping units which recognizes and corrects misclassification errors. Saggi and Jain (2018) have reported a data analytics solution for organizations [18]. This solution performs an exhaustive realistic analysis. The components of BDI application platforms are discussed. Authors have thrown light on past, current research issues and future directions.

Ribarics (2016) explained the importance of big data in agricultural sector [19]. The author has highlighted the need for using technological innovations in farming. Sarker et al. (2020) discussed the impact of BDI in digital farming [20].

The study results showed that big data analytics helps the farmers in crop management and yield forecasting. This study also revealed that BDI in farming is not fully established. **Table 5** presents a summary of highlights from the agriculture sector review.

#### **2.5 Review of literature on BDI in smart cities**

Kaur and Kushwaha (2018) were motivated by different applications of BDI and IoT integration in smart cities [21]. The earlier researchers reviewed the critical data analysis issues. Huang et al. (2014) have proposed HiperFuse solution for addressing BDI challenges and automating the BDI process [22]. Nuaimi et al. (2015) reviewed the prospects, issues and advantages of BDI in smart cities. This study discussed the BDI challenges faced in smart cities [23]. Gomes et al. (2016)


#### **Table 5.**

 *Agriculture sector review summary.*

demonstrated a smart city project model using BDI solutions in Brazil [24]. This project proposed a model that can be hosted in big data servers.

Alshawish et al. (2016) discussed the role and potential of BDI solutions in smart cities [25]. The authors have explained the complete process of BDI applications in smart cities.

This study has incorporated some real-world examples of smart city components. **Table 6** presents a summary of highlights from the smart cities review.

### **2.6 Manufacturing**

Ahmed et al. (2016) have proposed a Generating Attributes with Rolled Paths (GARP) algorithm that creates a mining table attributes from multiple data sources [26]. The experiments were carried out on the U.S. consumer electric retailer dataset and revealed that classification accuracy was improved by using GAPR. Bennani et al. (2014) have reported a guided BDI solution with Service Level Agreement (SLA) for querying data from multiple clouds [27]. The methodologies and algorithms designed are applied to energy utilization. Product planning, product design, manufacturing and maintenance process are reviewed in terms of concepts and applications. Qi and Tao (2018) provided a 360-degree review of big data in smart manufacturing [28]. Product planning, product design, manufacturing and maintenance processes are reviewed in terms of concepts and applications. Hufnagel et al. (2015) demonstrated a distributed integration model applicable to the manufacturing industry [29]. This research has created the user-oriented integration platform using a modular approach. O'Donovan et al. (2015) reported a detailed review of BDI implementation in the manufacturing sector. This study has provided a detailed review of big data research in manufacturing [30]. **Table 7** presents a summary of highlights from the manufacturing sector review.

**55**

**Table 7.**

**2.7 Review of healthcare sector**

*Manufacturing sector review summary.*

studies

Hardiman has explored BDI methodologies for Omics data and network algorithm development [31]. The objective was to channel the gap between phenotype and genotype which were not applied earlier. These researchers used spectrometry permitted geneticists, deep sequencing technologies, biostatisticians and biologists. Bhandari et al. have explained HGBEnviroScreen in their paper [32].

*Big Data Integration Solutions in Organizations: A Domain-Specific Analysis*

**Ref. No. Methods Used Results Applications**

resources

Automates the data integration process and leverages key capabilities

Big data applications for smart use of data and operations in smart cities

This software can be used in

Classification accuracy improvement and discriminant feature generation. Mitigates the impact of class imbalance

A distributed data as a service for SLA guided data aggregation framework

Digital twin and big data have great significance in smart manufacturing

User-oriented integration platform using a modular

approach

Usage of big data technologies in manufacturing for maintenance and diagnosis

big data servers

applications

**Ref. No. Methods Used Results Applications**

Big data-driven smart city improves smart city

A new data architecture that supports IoT and other data

Critical data analysis solution for IoT and

Website visitors income analysis, retail business

Effective management of smart city resources

Software for smart city project in Brazil

Smart Energy, Smart public safety and Smart traffic systems.

Consumer electronic retailer in Circuit City

Energy consumption applications, data integration of political campaign and electronics

Smart manufacturing in workshop or factory

Workflows and product life cycles in the manufacturing industry

Various manufacturing domain

U.S.

Big Data

analytics

*DOI: http://dx.doi.org/10.5772/intechopen.95800*

21 Various technologies for the

22 Data mixing planner, domain-

23 Literature survey on prospects,

24 Design of smart city project

*Smart cities sector review summary.*

26 Automatic generation of

discriminant features, aggregation of information from

multiple resources

27 The economic model of the cloud

28 Compare and contrast of digital twin and big data. Product planning, product design, manufacturing and maintenance process are reviewed in terms of concepts and applications

29 Featuring missing connection

30 Captured the status of big data

between successful business integration concept and proven graphical description

research in manufacturing, and compared the secondary research

referred for lookup, aggregation and correlation in SLA data integration, handling SLA interoperability and collaboration

**Table 6.**

25 Collecting data from networks, processing data with various stages and visualization data

integration

interface

handling of big data and IoT

specific data models, robust type inference, and declarative

issues and advantages of big data technologies in smart cities

model using big data in Brazil

*Big Data Integration Solutions in Organizations: A Domain-Specific Analysis DOI: http://dx.doi.org/10.5772/intechopen.95800*


#### **Table 6.**

*Data Integrity and Quality*

16 Data acquisition and

techniques

17 Identifying overlapping

18 Characteristics of BDA,

applications

applications

20 The comprehensive review

infarming

 *Agriculture sector review summary.*

19 Summary of Oracle's strategic white paper on Big data

semantic integration, statistical data analysis, data visualization, data query language, and geospatial data

units, matching variables, and classification methods

architecture, technologies, the relationship between value creation and BDA,

reveals the impact of big data

demonstrated a smart city project model using BDI solutions in Brazil [24]. This

**Ref. No. Methods Used Results Applications**

Data integration and big data analytics solution are discussed

Estimation of the missing data stratum, independent probability of sample infinite population

Big data analytics framework for value

Big data analytics as technological innovation in farming

Farming is not fully equipped with big data technologies.

creation

Agriculture decision support system. Helps the policymakers to implement restoration

Agricultural census data analysis of Australia for the

Smart city, cybersecurity, agriculture and healthcare

Farming and food production

Big data analytics helps the farmer in crop management

and forecasting

year 2015–2016

domains

strategies

This study has incorporated some real-world examples of smart city components. **Table 6** presents a summary of highlights from the smart cities review.

Ahmed et al. (2016) have proposed a Generating Attributes with Rolled Paths (GARP) algorithm that creates a mining table attributes from multiple data sources [26]. The experiments were carried out on the U.S. consumer electric retailer dataset and revealed that classification accuracy was improved by using GAPR. Bennani et al. (2014) have reported a guided BDI solution with Service Level Agreement (SLA) for querying data from multiple clouds [27]. The methodologies and algorithms designed are applied to energy utilization. Product planning, product design, manufacturing and maintenance process are reviewed in terms of concepts and applications. Qi and Tao (2018) provided a 360-degree review of big data in smart manufacturing [28]. Product planning, product design, manufacturing and maintenance processes are reviewed in terms of concepts and applications. Hufnagel et al. (2015) demonstrated a distributed integration model applicable to the manufacturing industry [29]. This research has created the user-oriented integration platform using a modular approach. O'Donovan et al. (2015) reported a detailed review of BDI implementation in the manufacturing sector. This study has provided a detailed review of big data research in manufacturing [30]. **Table 7**

presents a summary of highlights from the manufacturing sector review.

Alshawish et al. (2016) discussed the role and potential of BDI solutions in smart cities [25]. The authors have explained the complete process of BDI applications in

project proposed a model that can be hosted in big data servers.

**54**

smart cities.

**Table 5.**

**2.6 Manufacturing**

*Smart cities sector review summary.*


**Table 7.**

*Manufacturing sector review summary.*

#### **2.7 Review of healthcare sector**

Hardiman has explored BDI methodologies for Omics data and network algorithm development [31]. The objective was to channel the gap between phenotype and genotype which were not applied earlier. These researchers used spectrometry permitted geneticists, deep sequencing technologies, biostatisticians and biologists. Bhandari et al. have explained HGBEnviroScreen in their paper [32].

This is an EJ mapping tool providing the key services online to local decisionmakers and communities. This study has resulted in multiple risk factors leading to the largest vulnerability census tracts. These risk factors lead to natural disaster, social vulnerability and flooding. Shayne et al. have carried out a comprehensive study of integration solutions for big medical data [33]. This study has covered the applications, tools and technologies of BDI in the healthcare domain. Eftekhari et al. have proposed software as a service architecture [34]. This provides backend infrastructure for database access operations on data from different data sources. This methodology was approved with a proof-of-concept prototype developed on the OpenStack cloud architecture. Vidal et al. have presented a knowledge-driven framework [35]. This framework extracts knowledge from short text and unstructured data.

This framework used controlled vocabularies and ontologies to clarify the extracted entities and relations. Husain et al. have reported SOCR data dashboard design, implementation, and testing. SOCR does exploratory questioning of multi-source and heterogeneous and datasets [36]. **Table 8** presents a summary of highlights from the healthcare sector review.


**57**

**Table 9.**

*Big Data Integration Solutions in Organizations: A Domain-Specific Analysis*

summary of highlights from the communication sector review.

Cheng et al. proposed a remote sensing data management system [37]. This system is distributed multisource and followed the MongoDB model. The remote sensing, data integration and access are examined by designing a set of experiments. Wang et al. have described the major aspects of BDI such as characteristics, advantages, platform architecture, and application areas in telecommunication [38]. This research can be extended by improving multiple levels of protection technologies in the big data platform. Yayah et al. explained a few use cases of machine learning implementation in big data platforms [39]. Scalability and extensibility are the parameters used for the evaluation of BDI technologies. Nwanga et al. studied the impact of big data analytics in mobile phone industry [40]. This study has revealed that BDI solutions and big data analytics has an impact on the growth of the telecommunication industry by adding huge data insights. **Table 9** presents a

Antonio et al. presented a literature survey of simulation techniques in supply chain risks [41]. The authors have highlighted the significance of BDI in supply chain systems. This analysis has concluded that the problem at hand is simplified without complexity in modeling. This study has complied with industry 4.0 standards. Ostrowski et al. have explored the potential of semantic web technologies by demonstrating a case study in the supply chain [42]. Authors have identified the system for supporting data from multiple sources. This study was carried out by semi-automated mapping using shared domain ontology. Awwad et al. provided a review on applications, advantages and issues of BDI technologies for the supply chain management [43]. The supply chain risk management was carried out using data analytics by making a proactive decision. Lia and Liu have illustrated a data-driven framework for supply chain management [44]. The various circumstances of the supply chain

**Ref. No. Methods Used Results Applications**

Scalable storage data integration architecture, latest technical support and

Internal data applications enhance the efficiency of big data applications. External cooperation provides better

Adoption and improvement

telecommunication industry

of big data in the

Big data analytics has impact on the growth of the telecommunication industry Professional remote sensing big data

Development in telecommunication organization

Telco, Retail, Financial Services and Energy

sector

big data

Growth of the telecommunication industry with help of

development

services.

*DOI: http://dx.doi.org/10.5772/intechopen.95800*

**2.8 Review of communication sector**

**2.9 Review of supply chain sector**

37 Multi-Source BDI

framework, Spatial Segmentation Indexing Model, integration based on distributed storage

major aspects of big data such as characteristics, advantages, platform architecture, and application areas in telecommunication

learning tools in the Hadoop platform

and analysis of the impact of big data in the mobile

38 Introduced and reviewed

39 Integrating machine

40 Comprehensive study

industry

*Communication sector review summary.*

#### **Table 8.**

*Healthcare sector review summary.*
