Toward total business intelligence incorporating structured and unstructured data

Authors:
Byung-Kwon Park;Il-Yeol Song
Affiliations:
Dong-A University, Busan, South Korea;Drexel University, Philadelphia, PA
Venue:
Proceedings of the 2nd International Workshop on Business intelligencE and the WEB
Year:
2011

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Abstract

As the amount of data grows very fast inside and outside of an enterprise, it is getting important to seamlessly analyze both of them for getting total business intelligence. The data can be classified into two categories: structured and unstructured. Especially, as most of valuable business information are encoded in the unstructured text documents including Web pages in Internet, we need a specialized Text OLAP solution to perform multi-dimensional analysis on text documents in the same way as on structured relational data. Since the technologies of text mining and information retrieval are major technologies handling text data, we first review the representative works selected for demonstrating how they can be applied for Text OLAP. And then, we survey the representative works selected for demonstrating how we can associate and consolidate both unstructured text documents and structured relation data for obtaining total business intelligence. Finally, we present an architecture for a total business intelligence platform incorporating structured and unstructured data. We expect the proposed architecture, which integrates information retrieval, text mining, and information extraction technologies all together as well as relational OLAP technologies, would make an effective platform toward total business intelligence.