Obtaining relevant Data cubes in OLAP for Efficient Online Decision Support Systems

Abstract

Online trading requires a sharp Decision Support System that assists in rapid selection of stocks available in the market. This Decision Support System requires data about the pricing of stocks, the availability and the predictive updates in the market. Data gets accumulate day-to-day, forming huge volumes. This data needs multi dimensional modeling. Proper analysis has to be performed on the multi dimensional model to make an accurate decision, on investing in a stock. In the huge population of data, accurate data has to be selected and analyzed. Data cubes are very handy in an OLAP system as buyers always need personalized analysis in specific sets of data. This paper focuses on personalizing datacubes for traders which focuses on particular stocks and their specific data in an Online Analytical Processing System. The system also features the regular update of the database so as to include every new data is included in the analysis.