1. Introduction

This work aims to contribute to the fields of tech mining and technology forecasting (TF), based on science, technology & innovation (ST&I) data, from a quantitative methodological point of view. Tech mining aims to generate Competitive Technical Intelligence (CTI) using bibliometric and text mining (TM) software for analyses of ST&I information resources [1].

© 2016 The Author(s). Licensee InTech. 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 eproduction in any medium, provided the original work is properly cited. © 2018 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.

Meanwhile, TF can be generically defined as a prediction of the future characteristics of useful machines, procedures, or techniques [2]. The interrelation of both fields is proved by the fact that TF studies in companies are often called CTI [3].

opportunities analysis (TOA), which used ST&I data and bibliometrics with the purpose of identifying and assessing the implications of emerging scientific areas and new research technologies [7]. Following this path, Lee and Jeong used bibliometric data, co-word analysis, to generate a strategic diagram to be used for the analysis of the development trends of a specific technology domain [8]. Similarly, Lee et al. proposed a new TRM methodology to increase roadmapping effectiveness to support effective decision-making in new product and technology planning processes. The data source was patents and the method was founded on keyword-based product–technology maps, from which objective and quantitative information

Technology Roadmapping of Emerging Technologies: Scientometrics and Time Series Approach

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Latest efforts in this field are focused on the integration of more complex statistical methods and (semi)automatization proposals. In this regard, works can be found such as that proposed by Zhang et al. [10], in which a TRM composing method is described where data inputs are raw science textual data sources. The method seeks to identify macro-trends for R&D decision makers and is primarily based on a clustering-based topic identification model, a multiple science data sources integration model, and a semi-automated fuzzy set-based TRM composing model with expert aid. With similar goals, Joung and Kim propose technical keywordbased analysis of patents to monitor emerging technologies [11]. The approach includes the automatic selection of keywords and the identification of the relatedness among them. This task is based on the analysis of a technical keyword-context matrix, which is obtained by

However, when it comes to introduce a consistent forecasting method based on ST&I data, there is a lack of time series analysis (TSA) methods. In terms of statistical methods, the most common approach for forecasting the future evolution of a technology based on bibliometric data is growth curve analysis (see [12] for further discussion). When it comes to combine scientometrics and TF, the inclusion of specific time series models is hardly encountered within the reference literature (see for example [13, 14]). What is more, the time series commonly take the frequency of generic items, such as patents or articles, as indicators without going down to a lower level, such as keywords, which provide richer information about the technology or field that is being analyzed. This kind of strategy is roughly chosen by Park and Jun [15] within the patent analysis field. Here, time series regression and clustering techniques are combined to construct a technological trend model of identified clusters, and that furthermore, these

The following section describes the proposed approach, which is based on the combination of methods and techniques discussed here, in an attempt to identify an optimal combination of

As previously stated, the present approach combines a set of methods which belong to tech

• Scientometrics: to retrieve scientific publications related to an emerging technology and

structure a customized database of the corresponding records.

can be derived [9].

means of text-mining tools and techniques.

clusters are described by means of top keywords.

mining and technology forecasting fields. Namely:

the most representative ones.

3. Research approach

Both activities (CTI and TF) are crucial for current enterprises, since they address organizational and cultural barriers to adopt and harness the potential of strategic emerging technologies. In fact, literature suggests that this is even more important for SMEs, since they are slow adopters of technology, often purchasing long after release and regularly dealing with technology handed down from other companies [4]. If a company, especially medium or small, does not succeed in the early adoption of an emerging technology, it can be irremediably surpassed by those competitors who did know how to adopt it correctly. Additionally, the TF field also includes more social and diffuse measurements. For example, governments use national foresight studies to assess the course and impact of technological change for the purposes of effecting public policy [3], and some studies are also used as an awareness-raising tool, alerting industrialists to opportunities emerging in S&T or alerting researchers to the social or commercial significance and potential of their work [5].

Within this framework, the importance of correctly structuring the ST&I information for a consistent analysis of a given technology should be underscored, as it facilitates the elicitation of meaningful implications by reducing the dimensions of original data and eliminating noise that normally exists in multivariate data [6]. Accordingly, any attempt to understand the main characteristics of a technology and to discover its future evolution based on ST&I data should go through three phases: the application of scientometrics in order to structure and prepare the data related to it; the use of TM techniques, making it possible to go beyond processing the content of the data and transforming it into information; exploit the generated information to forecast the future evolution of the technology by means of TF techniques.

Based on the above, the present work proposes an approach which makes use of tech mining and TF techniques for describing an emerging technology in full. Its application to a specific field or technology brings out information that can be regarded as inputs for CTI activities. It provides the structure of the technology, the dominating subfields throughout its evolution and the potential dominating concepts of short-term future. Besides, all the information is condensed and structured in a technology roadmap (TRM), which allows a complete depiction of the technology in a single visual item.

The work is divided as follows. Section two introduces the background of the work, paying attention to similar efforts that can be found in literature. Section three describes the proposed approach, going into the detail of the techniques on which is structured and their combination. Section four is used to apply the approach to a specific technology: big data (BD). Finally, in section five the applicability and validity of the approach is discussed and the future lines of work are described.
