Applying Multi-dimensional Scaling Analysis for Finding Similarity Knowledge in OLAP Reports

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 Multi-Dimensional Scaling (MDS) 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.