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Artificial Intelligence Laboratory

Digital Libraries Projects - EBizPort

EBizPort - Business Intelligence Portal

Introduction

To make good decisions, businesses try to gather good intelligence information.  Yet managing large quantities of unstructured information and data stand in the way of greater business knowledge.  Effective tools for increasing business intelligence used to access relevant content, in different formats must include analysis tools for searching, browsing, and summarization.  Useful business intelligence content needs to be credible, timely, and relevant.  We created eBizPort to facilitate the process of acquiring business intelligence in the Information Technology industry.  The eBizPort contains content from the leading news and commentary providers in the IT domain, which was collected using a new vertical collection-building technique.  We have integrated a number of post-retrieval tools to support browsing and investigate techniques to overcome information overload.

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Acknowledgements

We would like to express our gratitude to NSF Digital Library Initiative-2, "High-performance Digital Library Systems:  From Information Retrieval to Knowledge Management,"  IIS-9817473, April 1999-March 2002

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Approach & Methodology

Content (over 400,00 URLs) from the following providers:

  • Computerworld
  • The Industry Standard
  • Wired
  • PCWorld
  • InternetWeek
  • InfoWorld
  • C|Net
  • IDG
  • ITWord
  • CIO
  • Business 2.0
  • Informationweek
  • RedHerring
Techniques:
  • Meta-searching spider that gathers content from major news providers
  • Java parsing and indexing programs to create importable database files
  • Kohonen Self-Organization Map (SOM): Algorithms-single and multi-layer self-organizing maps for information categorization and visualization.
  • Database stored procedures to import and manage inverted index and provide searching functionality
  • SOM display
  • Folder Clustering of main document topics
  • AZ Noun Phraser for topic extraction

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Team Members

   Dr. Hsinchun Chen hchen@eller.arizona.edu
   Byron Marshall  
   Daniel McDonald  
   Wingyan Chung  

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Publications

Byron Marshall, Dan McDonald, Hsinchun Chen, Wingyan Chung
“EBizPort: Collecting and Analyzing Business Intelligence Information”
Journal of the American Society for Information Science and Technology (JASIST) Special Issue on Document Search Interface Design for Large-scale Collections and Intelligent Access, (accepted for publication 2003, forthcoming 2004)

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