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The GoOLAP Fact Retrieval Framework

Chapter
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 96)

Summary

We discuss the novel problem of supporting analytical business intelligence queries over web-based textual content, e.g., BI-style reports based on 100.000’s of documents from an ad-hoc web search result. Neither conventional search engines nor conventional Business Intelligence and ETL tools address this problem, which lies at the intersection of their capabilities. Three recent developments have the potential to become key components of such an ad-hoc analysis platform: significant improvements in cloud computing query languages, advances in self-supervised keyword generation techniques and powerful fact extraction frameworks. We will give an informative and practical look at the underlying research challenges in supporting ”Web-Scale Business Analytics” applications that we met when building GoOLAP, a system that already enjoys a broad user base and over 6 million objects and facts.

Keywords

Information Extraction Factual Information Keyword Query Computational Linguistics Fact Extractor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.FG DIMATechnische Universität BerlinBerlinGermany

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