Research on the integration architecture of OLAM and OLAP

Online Analytical Processing (OLAP) was widely used to visualize complex data for efficient, interactive and meaningful analysis. Its power comes in visualizing huge operational data for interactive analysis. Data mining techniques (DM) are strong at detecting pattern sand mining knowledge from historical data. OLAP and DM are believed to be able to complement each other to analyze large data sets in decision support systems. Some recent researches have shown the benefits of combining OLAP with Data Mining. In this paper, we review the OLAM and OLAP and data mining, and identify their limitations. We propose a conceptual model that overcomes the existing limitations, and provide a way for combining OLAP with OLAM. Furthermore, the proposed model offers directions to improving cube´s construction time and visualization over the data cube.