An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Towards on-line analytical mining in large databases
ACM SIGMOD Record
ACM Computing Surveys (CSUR)
Data mining: concepts and techniques
Data mining: concepts and techniques
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Data Warehousing, Data Mining, and Olap
Data Warehousing, Data Mining, and Olap
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Mining Constrained Gradients in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Data Mining with SQL Server 2005
Data Mining with SQL Server 2005
Expert Systems with Applications: An International Journal
Online analytical mining association rules using Chi-square test
International Journal of Business Intelligence and Data Mining
The Data Warehouse Lifecycle Toolkit
The Data Warehouse Lifecycle Toolkit
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 12.05 |
On-line analytical 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, all analyses on the contents of reports are left for the report readers. To discover hidden rules, similar reports, or trend inside the potentially huge amount of reports, the report readers can only rely on their smart eyes to find out any rules of such kinds. Data mining is a well-developed field for finding hidden rules inside the data itself. However, there are 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 data mining techniques on OLAP reports in order to automatically and effectively find the similarity knowledge of OLAP reports. We also address the appropriate presentation of this similarity knowledge to OLAP users. We compare the difference between traditional data mining and finding similarity knowledge from OLAP reports. We then proposed three methods (called OLAP_MDS, OLAP_CLU, and OLAP_M+C in this paper) to explore the effectiveness of discovering similarity knowledge from OLAP reports. Finally, we compare the pros and cons of the proposed three methods with experiments and conclude that the OLAP_M+C method should be the best in most cases.