Study and Performance Analysis of Different Techniques for Computing Data Cubes

Abstract

Data is an integrated form of observable and recordable facts in operational or transactional systems in the data warehouse Usually data warehouse stores aggregated and historical data in multi-dimensional schemas Data only have value to end-users when it is formulated and represented as information And Information is a composed collection of facts for decision making Cube computation is the most efficient way for answering this decision making queries and retrieve information from data Online Analytical Process OLAP used in this purpose of the cube computation There are two types of OLAP Relational Online Analytical Processing ROLAP and Multidimensional Online Analytical Processing MOLAP This research worked on ROLAP and MOLAP and then compare both methods to find out the computation times by the data volume Generally a large data warehouse produces an extensive output and it takes a larger space with a huge amount of empty data cells To solve this problem data compression is inevitable Therefore Compressed Row Storage CRS is applied to reduce empty cell overhead