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Business Insights Workbench – An Interactive Insights Discovery Solution

  • Amit Behal
  • Ying Chen
  • Cheryl Kieliszewski
  • Ana Lelescu
  • Bin He
  • Jie Cui
  • Jeffrey Kreulen
  • James Rhodes
  • W. Scott Spangler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4558)

Abstract

Today’s businesses increasingly rely on vast amount of information. Yet effective use of information is becoming more and more difficult. This paper describes a general purpose analytics solution, “Business Insights Workbench” (BIW), which embeds two major classes of information analytics techniques and a unique set of visualizations to mine the available information and uncover critical business insights and enhance business performance. The two major classes of analytics technologies include the “taxonomy” analysis and the “relationship” analysis to facilitate understanding and detection of hidden concepts and patterns buried in the information respectively. The BIW technologies have been successfully applied in many application domains, e.g., Customer Relationship Management (CRM) for customer satisfaction analysis, Intellectual Property (IP) for patent portfolio analysis and licensee identification, and Healthcare Life-sciences (HCLS) for facilitating drug discovery by identifying the relationships among chemicals, DNA, proteins, drugs, and diseases. We show some BIW sample applications in this paper.

Keywords

Information Mining Human Interaction Visualization 

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References

  1. 1.
    Srikant, R., Vu, Q., Agrawal, R.: Mining association rules with item constraints. In: Proceedings of the 3rd Int’l Conf. on Knowledge Discovery in Databases and Data Mining. Newport Beach, CA (1997)Google Scholar
  2. 2.
    Frawley, W., Piatetsky-Shapiro, G., Matheus, C.: Knowledge discovery in Databases: An overview. AI Magazine. Fall 1992, pp. 213–228 (1992)Google Scholar
  3. 3.
    Tan, P., Steinbach, M., Kumar, V.: Introduction to data mining. Addison-Wesley Publishing, London (2005)Google Scholar
  4. 4.
    Agrawal, R.: Data mining: Crossing the chasm. In: Keynote at the 5th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. San Diego, CA (1999)Google Scholar
  5. 5.
    Bayardo, R.J., Agrawal, R.: Mining the most interesting rules. In: Proc. Of the 5th CAN SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining (1999)Google Scholar
  6. 6.
    Grossman, D., Frieder, O.: Information retrieval: Algorithms and heuristics, 2nd edn. Springer, Heidelberg (2006)Google Scholar
  7. 7.
    Baeza-Yates, R., Ribeiro-Beto, B.: Modern information retrieval. Addison-Wesley Publishing, London (1999)Google Scholar
  8. 8.
    Manning, C.D., Schutze, H.: Foundations of statistical natural language processing. MIT Press, Cambridge (1999)zbMATHGoogle Scholar
  9. 9.
    Jackson, P., Moulinier, I.: Natural language processing for online applications: Text retrieval, extraction, and categorization. John Benjamins Publishing Co, Amsterdam (2002)Google Scholar
  10. 10.
    Gotz, T., Suhre, O.: Design and implementation of the UIMA common analysis system. IBM System Journal 43(3) (2004)Google Scholar
  11. 11.
    Modha, D., Spangler, S.: Feature weighting in K-Means clustering. Machine learning 52(3), 217–237 (2003)zbMATHCrossRefGoogle Scholar
  12. 12.
    Spangler, S., Kreulen, J., Lesser, J.: Generating and browsing multiple taxonomies over a document collection. Journal of Management Information Systems 19(4), 191–212 (2003)Google Scholar
  13. 13.
    Spangler, W.S., Kreulen, J.T., Newswanger, J.F.: Machines in the conversation: Detecting themes and trends in information communication streams. IBM Systems Journal (to appear)Google Scholar
  14. 14.
    Kreulen, J., Spangler, W.S., Lesser, J.: MindMap: Utilizing multiple taxonomies and visualization to understand a document collection. HCCI (2002)Google Scholar
  15. 15.
    Spangler, S., Kreulen, J.: Interactive methods for taxonomy editing and validation. ACM CIKM (2002)Google Scholar
  16. 16.
    Press, W., et al.: Numerical Recipes in C, 2nd edn., pp. 620–623. Cambridge University Press, New York (1992)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Amit Behal
    • 1
  • Ying Chen
    • 1
  • Cheryl Kieliszewski
    • 1
  • Ana Lelescu
    • 1
  • Bin He
    • 1
  • Jie Cui
    • 2
  • Jeffrey Kreulen
    • 1
  • James Rhodes
    • 1
  • W. Scott Spangler
    • 1
  1. 1.IBM Almaden Research Center, San Jose, CaliforniaUSA
  2. 2.IBM China Research Lab, BeijingChina

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