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Big Data for Prediction: Patent Analysis – Patenting Big Data for Prediction Analysis

Big Data for Prediction: Patent Analysis – Patenting Big Data for Prediction Analysis

Mirjana Pejic-Bach, Jasmina Pivar, Živko Krstić
Copyright: © 2022 |Pages: 24
ISBN13: 9781668436622|ISBN10: 1668436620|EISBN13: 9781668436639
DOI: 10.4018/978-1-6684-3662-2.ch057
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MLA

Pejic-Bach, Mirjana, et al. "Big Data for Prediction: Patent Analysis – Patenting Big Data for Prediction Analysis." Research Anthology on Big Data Analytics, Architectures, and Applications, edited by Information Resources Management Association, IGI Global, 2022, pp. 1192-1215. https://doi.org/10.4018/978-1-6684-3662-2.ch057

APA

Pejic-Bach, M., Pivar, J., & Krstić, Ž. (2022). Big Data for Prediction: Patent Analysis – Patenting Big Data for Prediction Analysis. In I. Management Association (Ed.), Research Anthology on Big Data Analytics, Architectures, and Applications (pp. 1192-1215). IGI Global. https://doi.org/10.4018/978-1-6684-3662-2.ch057

Chicago

Pejic-Bach, Mirjana, Jasmina Pivar, and Živko Krstić. "Big Data for Prediction: Patent Analysis – Patenting Big Data for Prediction Analysis." In Research Anthology on Big Data Analytics, Architectures, and Applications, edited by Information Resources Management Association, 1192-1215. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-3662-2.ch057

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Abstract

Technical field of big data for prediction lures the attention of different stakeholders. The reasons are related to the potentials of the big data, which allows for learning from past behavior, discovering patterns and values, and optimizing business processes based on new insights from large databases. However, in order to fully utilize the potentials of big data, its stakeholders need to understand the scope and volume of patenting related to big data usage for prediction. Therefore, this chapter aims to perform an analysis of patenting activities related to big data usage for prediction. This is done by (1) exploring the timeline and geographic distribution of patenting activities, (2) exploring the most active assignees of technical content of interest, (3) detecting the type of the protected technical according to the international patent classification system, and (4) performing text-mining analysis to discover the topics emerging most often in patents' abstracts.

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