Applying clustering analysis on grouping similar OLAP reports

Kevin Chihcheng Hsu, Ming Zhong Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

On Line Analysis Processing (OLAP) is a common solution that modern enterprises use to generate, monitor, share, and administrate their analysis reports. When daily, weekly, and/or monthly reports are generated or published by the OLAP operators, the report readers can only rely on their smart eyes to find out hidden rules, similar reports, or trend inside the potentially huge amount of reports. Data mining is a well-developed field for finding hidden rules inside the data itself. However, there is few techniques focus on finding hidden rules, similarity, or trend using OLAP reports as the unit of analysis. In this paper, we explore how to use clustering analysis on OLAP reports in order to automatically and effectively find the grouping knowledge of OLAP reports. We also address the appropriate presentation of this grouping knowledge to OLAP users.

Original languageEnglish
Title of host publication2010 2nd International Conference on Computer Engineering and Applications, ICCEA 2010
Pages417-423
Number of pages7
DOIs
StatePublished - 2010
Event2nd International Conference on Computer Engineering and Applications, ICCEA 2010 - , Indonesia
Duration: 19 Mar 201021 Mar 2010

Publication series

Name2010 2nd International Conference on Computer Engineering and Applications, ICCEA 2010
Volume2

Conference

Conference2nd International Conference on Computer Engineering and Applications, ICCEA 2010
Country/TerritoryIndonesia
Period19/03/1021/03/10

Keywords

  • Clustering
  • Data mining
  • OLAM
  • OLAP

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