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OLAP-based Scalable Profiling of Customer Behavior

  • Qiming Chen
  • Umesh Dayal
  • Meichun Hsu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1676)

Abstract

Profiling customers’ behavior has become increasingly important for many applications such as fraud detection, targeted marketing and promotion. Customer behavior profiles are created from very large collections of transaction data. This has motivated us to develop a data-warehouse and OLAP based, scalable and flexible profiling engine. We define profiles by probability distributions, and compute them using OLAP operations on multidimensional and multilevel data cubes. Our experience has revealed the simplicity and power of OLAP-based solutions to scalable profiling and pattern analysis.

Keywords

Customer Behavior Fraud Detection Calling Pattern Incoming Call Dimension Duration 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Qiming Chen
    • 1
  • Umesh Dayal
    • 1
  • Meichun Hsu
    • 1
  1. 1.HP Labs, Hewlett-PackardPalo AltoUSA

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