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Text-Driven Reasoning and Multi-Structured Data Analytics for Business Intelligence

Text-Driven Reasoning and Multi-Structured Data Analytics for Business Intelligence

Lipika Dey, Ishan Verma
Copyright: © 2016 |Pages: 32
ISBN13: 9781466695627|ISBN10: 1466695625|EISBN13: 9781466695634
DOI: 10.4018/978-1-4666-9562-7.ch001
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MLA

Dey, Lipika, and Ishan Verma. "Text-Driven Reasoning and Multi-Structured Data Analytics for Business Intelligence." Business Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2016, pp. 1-32. https://doi.org/10.4018/978-1-4666-9562-7.ch001

APA

Dey, L. & Verma, I. (2016). Text-Driven Reasoning and Multi-Structured Data Analytics for Business Intelligence. In I. Management Association (Ed.), Business Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 1-32). IGI Global. https://doi.org/10.4018/978-1-4666-9562-7.ch001

Chicago

Dey, Lipika, and Ishan Verma. "Text-Driven Reasoning and Multi-Structured Data Analytics for Business Intelligence." In Business Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1-32. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9562-7.ch001

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Abstract

Business Intelligence (BI) refers to an organization's capability to gather and analyze data about business operations and transactions in order to evaluate its performance. The abundance of information both within the enterprise and outside of it has necessitated a change in traditional Business Intelligence practices. There is a need to exploit heterogeneous resources. Text data like news, analyst reports, etc. helps in better interpretation of business data. In this chapter, the authors present a futuristic BI framework that facilitates acquisition, indexing, and analysis of heterogeneous data for extracting business intelligence. It enables integration of unstructured text data and structured business data seamlessly to generate insights. The authors propose methods that can help in extraction of events or significant happenings from both unstructured and structured data, correlate the events, and thereafter reason to generate insights. The insights extracted could be validated as cause-effect pairs based on the statistical significance of co-occurrence of events.

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