5. Conclusions and future work

values, thus its great importance within BD applications is expected to increase. Once again, there are some sub-technologies that present lower increase values, such as energy efficiency, visual data and disaster prevention; all of them with a 10% value. Accordingly, these should be considered as fields that will gradually lose importance at the level of development and investment. In any case there is a general conclusion, which is the fact that the whole set of series present a positive trend value. This leads to a clear conclusion: BD as such is still increasing its importance among researchers and practitioners. It is still

The final outcome of the approach is the TRM, in which all the previous partial results are integrated. What is more, the structuring and content of the TRM itself is conditioned by the partial results that have been obtained. The vertical structure is derived directly from the first level of the ontology in the case of the technology layer. This is not the case with the application layer, since the first line of its ontology had too many elements to sub-divide the layer based on them. Accordingly, the layer is presented without subdivisions. The included terms are the most frequent terms, year by year, extracted from the list generated by means of the NLP task. It is required that terms exceed a certain level of frequency to be included in the TRM, and that is why more gaps appear during the initial years. In fact, it is from year 2014 when the TRM starts to be full of information, which coincides with the moment that the time series grew consistently. Furthermore, it is in the last years when the diversity of terms grows significantly, and consequently, the terms that describe more general concepts give way to others that represent more specific fields. The terms are grouped within the main sub-technologies identified above, and those terms that do not belong to any of these are placed loose. The vertical position of both the sub-technologies and loose terms, in the case of the technology layer, is based on the vertical structure of the TRM itself. Whereas for the application layer, as there is no such sub-division, placement is done by following the structure of the technology layer, as far as possible, to maintain a unified criterion throughout the TRM. Finally, the slope value of the models for each sub-technology is incorporated. The set of sub-technologies have been divided into five levels, from least to greatest slope, and have been painted accordingly with the following colors: gray; green; blue; orange; and red. Additionally, those with greater slopes have been extended further into the future, representing the probability of these being dominating fields in the short-term future. Thus, a third dimen-

With regard to the content, the TRM provides a good summarization of the evolution of the technology characteristics. It can be seen how the first years show initial ideas that were developed within the different sub-technologies. For the technology layer, foundational terms such as distributed database systems in memory architecture and information management in competitive intelligence can be found. As time passes, more specific fields begin to appear, such as smart cities in internet of things and semantic web in knowledge based systems. Together with this, those topics within the fastest growing sub-technologies can be identified, which are candidates to have a strong presence in the short-term, such as business intelligence

an emerging technology.

112 Scientometrics

sion has been added through the colors.

The present work proposes an approach which makes use of tech mining and TF techniques for describing an emerging technology in full. The approach has been designed as a combination of quantitative methods through which various partial results are obtained, with which the technology analyzed is fully described. Within these methods, the main contribution is the idea of combining a more classical analysis based on scientometrics and common TM methods, such as clustering and text summarization; with less usual and more current methods such as PCA and especially TSA. Furthermore, technology roadmapping has been introduced to generate a final integrating element, in which all the information is aggregated. All this has permitted a fuller description of the technology, as well as a prospective exercise. To validate the applicability of the approach, it has been applied to BD technology, an emerging cutting edge technology. In that application, based on scientometrics analysis to generate a clean usable database, we have been able to apply the different methods with which the ontology of technology has been generated (hierarchical clustering method); and the main subtechnologies have been identified (PCA) (Figures 5 and 6).

Furthermore, a novel counting process has been presented to generate time series. These series have made it possible to understand the evolution of technology in detail. Additionally, they have been used to identify which sub-technologies have dominated the field throughout the years, and by means of a modeling process, which ones are expected to do so in the short-term future. It is at this point that it has been possible to identify that certain sub-technologies, such as memory architecture or energy efficiency, have shown limited growth in recent years, while others have accelerated their activity, with examples like competitive intelligence and smart power grids.

The results obtained come directly from the input data of the application: scientific publications. While more sophisticated results and deeper insights can be achieved on the analyzed technology, the aim has been to demonstrate that it is possible to generate such a powerful and information-filled element as the TRM by means of quantitative analysis of the data. In this sense, future lines of work should be directed towards the integration of more input data for the approach. In following with this, there are two elements that are being considered: patents

and web pages. The first will provide information about products or highly developed applications, while the webs will be used to analyze the technology at market level, based on web pages of enterprises that commercialize the technology. The same methods can be applied to

> Query processing

Health care

Technology Roadmapping of Emerging Technologies: Scientometrics and Time Series Approach

Data comm. syst.

Knowledge based syst.

http://dx.doi.org/10.5772/intechopen.76675

Internet of Things

Data visual. 115

these data and the results can be integrated by means of new layers in the TRM.

Data privacy

A. Appendix

Memory arch.

Competitive intelligence

Learning Systems

Figure 5. Technology roadmap of BD basic technology.

Figure 6. Technology roadmap of BD applications.

and web pages. The first will provide information about products or highly developed applications, while the webs will be used to analyze the technology at market level, based on web pages of enterprises that commercialize the technology. The same methods can be applied to these data and the results can be integrated by means of new layers in the TRM.
