Discovery-driven exploration of OLAP data cubes

  • Sunita Sarawagi
  • Rakesh Agrawal
  • Nimrod Megiddo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1377)


Analysts predominantly use OLAP data cubes to identify regions of anomalies that may represent problem areas or new opportunities. The current OLAP systems support hypothesis-driven exploration of data cubes through operations such as drill-down, roll-up, and selection. Using these operations, an analyst navigates unaided through a huge search space looking at large number of values to spot exceptions. We propose a new discovery-driven exploration paradigm that mines the data for such exceptions and summarizes the exceptions at appropriate levels in advance. It then uses these exceptions to lead the analyst to interesting regions of the cube during navigation. We present the statistical foundation underlying our approach. We then discuss the computational issue of finding exceptions in data and making the process efficient on large multidimensional data bases.


Data Cube Aggregate Function Multiple Equation Cube Computation Huge Search Space 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Sunita Sarawagi
    • 1
  • Rakesh Agrawal
    • 1
  • Nimrod Megiddo
    • 1
  1. 1.IBM Almaden Research CenterSan JoseUSA

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