Summarizing Datacubes

Datacubes are especially useful for answering efficiently queries on data warehouses. Nevertheless the amount of generated aggregated data is huge with respect to the initial data which is itself very large. Recent research work has addressed the issue of summarizing Datacubes in order to reduce their size. In this chapter, we present three different approaches. They propose structures which make it possible to reduce the size of the data cube representation. The two former, the closed cube and the quotient cube, are said semantic and discard the redundancies captured within data cubes. The size of the underlying representations is especially reduced but the counterpart is an additional response time when answering the OLAP queries. The latter approach is rather syntactic since it enforces an optimization at the logical level. It is called Partition Cube and based on the concept of partition. We also give an algorithm to compute it. We propose a Relational Partition Cube, a novel R-Olap cubing solution for managing Partition Cubes using the relational technology. An analytical evaluation shows that the storage space of Partition Cubes is smaller than Datacubes. In order to confirm analytical comparison, experiments are performed in order to compare our approach with Datacubes and with two of the best reduction methods, the Quotient Cube and the Closed Cube.