Data Mining Practical Machine Learning Tools and Techniques

Disruptive Innovation Theory has created a significant impact on management practices and aroused plenty of rich debate within academia. Copious as the studies are, the scattered and conflicting nature of the literature on disruptive innovation in the last decade may pose a state of ambiguity for future research, thus necessitating a comprehensive review at this juncture. This paper first clarifies the basic concept and potential misinterpretations of the theory. Believing in the predictive value of the theory on firm performance, the authors then summarize and critique the research on how to enable potential disruptive innovation from internal, external, marketing and technology perspectives. The different perspectives inspired the authors to identify a number of key research directions within the disruptive innovation research domain. Potential future research is also briefly discussed by integrating disruptive innovation with other research domains, such as open innovation. Finally, in addition to theoretical contributions, the authors make practical contributions by outlining a series of potential inhibitors and enablers of disruptive innovation as managerial ‘take-aways’.

https://papers.ssrn.com/sol3/Delivery.cfm/ijmr_272.pdf?abstractid=1701977&mirid=4

A Reflective Review of Disruptive Innovation Theory

https://isiarticles.com/bundles/Article/pre/pdf/18064.pdf

An approach to discovering new technology opportunities: Keyword-based patent map approach

https://bkgsi.yonsei.ac.kr/bkgsi/paper/An%20integrated%20service-device-technology%20roadmap%20for%20smart%20city%20development.pdf

An integrated service-device-technology roadmap for smart city development

https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Abbas%20-%20Patent%20Analysis%20-%202014.pdf

A literature review on the state-of-the-art in patent analysis

Many methods have been developed to recognize those progresses of technologies, and one of them is to analyze patent information. And visualization methods are considered to be proper for representing patent information and its analysis results. However, current visualization methods for patent analysis patent maps have some drawbacks. Therefore, we propose an alternative visualization method in this paper. With colleted keywords from patent documents of a target technology field, we cluster patent documents by the k-Means algorithm. With the clustering results, we form a semantic network of keywords without respect of filing dates. And then we build up a patent map by rearranging each keyword node of the semantic network according to its earliest filing date and frequency in patent documents. Our approach contributes to establishing a patent map which considers both structured and unstructured items of a patent document. Besides, differently from previous visualization methods for patent analysis, ours is based on forming a semantic network of keywords from patent documents. And thereby it visualizes a clear overview of patent information in a more comprehensible way. And as a result of those contributions, it enables us to understand advances of emerging technologies and forecast its trend in the future.

Visualization of patent analysis for emerging technology

Dynamic patterns of industry convergence: Evidence from a large amount of unstructured data

The rapid changes in the market environment as well as technological innovations drive firms to devise differentiated products or services that functionally overlap with other products or services. Such overlapping in markets with converged products or services eventually drives industries to converge. However, despite the significant impact of industry convergence on the economy, our understanding of the phenomenon is limited because previous studies largely come from the scientific/technological perspective. Accordingly, in this paper, we investigate the trends and patterns of market-side industry convergence for entire sectors of the U.S. economy. To do so, we conduct a co-occurrence-based analysis of text mining for a large volume of unstructured data — newspaper articles from 1989 to 2012 — and suggest an industry convergence (IC) index based on normalized pointwise mutual information (PMI). We find that industry convergence has been increasing over time, but it does not take place throughout all industries. Some industries show strong convergence intensity while others do not. We also identify industry groups that are converging over time as well as those that are diverging, which creates heterogeneous patterns of industry convergence. These findings suggest that significant transformation is under way in the economy, but the trends and patterns are quite heterogeneous, depending on the industry. In addition, this study provides a method for anticipating the future direction of industry convergence.

Dynamic Patterns of Industry Convergence: Evidence from a Large Amount of Unstructured Data

As a consequence of the service economy, R&D of the service industry has become more essential nowadays. Therefore, technology roadmaps are required for selection and concentration of services and related technologies. However, now there are problems and challenging issues as follows. First, there is no objective and systematic method or analysis tool to evaluate emerging technologies for services. Second, current technology roadmaps do not provide technology's priority oriented to the service side. Therefore, we propose a patent map and a Service-oriented Technology Roadmap using the patent map, i.e. SoTRM. Our patent map is a three-dimensional visualization method and analysis tool based on keywords, which contributes to evaluating emerging technologies for services. It does not only overcome the subjectivity of experts, but it also discovers technologies missed out by experts initially. And SoTRM is a technology roadmap customized for the service industry. Based on four layers of patents, keywords, technologies, and services, the layer of service-oriented technologies provides the order of technologies in a service-oriented aspect. It also gives guidelines to assign roles in R&D to public and private sectors. As a result, we provide an objective and systematic framework required to form a technology roadmap oriented to services for R&D strategy of the service industry. Eventually, it helps decision makers from public and private sectors to select and concentrate on the first things among services and the related technologies in R&D of the service industry, and thereby to find the direction of distributing investment funds into technologies for services.

Service-oriented Technology Roadmap (SoTRM) using patent map for R&D strategy of service industry

As the Internet has been the virtual place where citizens are united and their opinions are promptly shifted into the action, two way communications between the government sector and the citizen have been more important among activities of e-Government. Hence, Anti-corruption and Civil Rights Commission (ACRC) in the Republic of Korea has constructed the online petition portal system named e-People. In addition, the nation's Open Innovation through e-People has gained increasing attention. That is because e-People can be applied for the virtual space where citizens participate in improving the national law and policy by simply filing petitions to e-People as the voice of the nation. However, currently there are problems and challenging issues to be solved until e-People can function as the virtual space for the nation's Open Innovation based on petitions collected from citizens. First, there is no objective and systematic method for analyzing a large number of petitions filed to e-People without a lot of manual works of petition inspectors. Second, e-People is required to forecast the trend of petitions filed to e-People more accurately and quickly than petition inspectors for making a better decision on the national law and policy strategy. Therefore, in this paper, we propose the framework of applying text and data mining techniques not only to analyze a large number of petitions filed to e-People but also to predict the trend of petitions. In detail, we apply text mining techniques to unstructured data of petitions to elicit keywords from petitions and identify groups of petitions with the elicited keywords. Moreover, we apply data mining techniques to structured data of the identified petition groups on purpose to forecast the trend of petitions. Our approach based on applying text and data mining techniques decreases time-consuming manual works on reading and classifying a large number of petitions, and contributes to increasing accuracy in evaluating the trend of petitions. Eventually, it helps petition inspectors to give more attention on detecting and tracking important groups of petitions that possibly grow as nationwide problems. Further, the petitions ordered by their petition groups' trend values can be used as the baseline for making a better decision on the national law and policy strategy.

Jong Hwan Suh

Papers

Visualization of patent analysis for emerging technology

Dynamic patterns of industry convergence: Evidence from a large amount of unstructured data

Dynamic Patterns of Industry Convergence: Evidence from a Large Amount of Unstructured Data

Service-oriented Technology Roadmap (SoTRM) using patent map for R&D strategy of service industry

Applying text and data mining techniques to forecasting the trend of petitions filed to e-People