Techniques for finding similarity knowledge in OLAP reports

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. ining 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. s 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. pare 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.