One utilisation of multidimensional databases is the field of On-line Analytical Processing (OLAP). The applications in this area are designed to make the analysis of shared multidimensional information fast PendseB. On one hand, speed can be achieved by specially devised data structures and algorithms. On the other hand, the analytical process is cyclic. In other words, the user of the OLAP application runs his or her queries one after the other. The output of the last query may be there (at least partly) in one of the previous results. Therefore caching also plays an important role in the operation of these systems. However, caching itself may not be enough to ensure acceptable performance. Size does matter: The more memory is available, the more we gain by loading and keeping information in there. Oftentimes, the cache size is fixed. This limits the performance of the multidimensional database, as well, unless we compress the data in order to move a greater proportion of them into the memory. Caching combined with proper compression methods promise further performance improvements. In this paper, we investigate how caching influences the speed of OLAP systems. Different physical representations (multidimensional and table) are evaluated. For the thorough comparison, models are proposed. We draw conclusions based on these models, and the conclusions are verified with empirical data.
The paper of Westmann @cite_22 lists several related works in this field. It also discusses how compression can be integrated into a relational database system. It does not concern itself with the multidimensional physical representation, which is the main focus of our paper. They demonstrate that compression indeed offers high performance gains. It can, however, also increase the running time of certain update operations. In this paper we will analyse the retrieval (or point query) operation only, as a lot of On-line Analytical Processing (OLAP) applications handle the data in a or way. The database is updated outside working hours in batch. Despite this difference, we also encountered performance degradation due to compression when the entire physical representation was cached into the memory. In this case, at one of the benchmark databases (TPC-D), the multidimensional representation became slower than the table representation because of the CPU-intensive Huffman decoding.
@cite_22
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In this paper, we show how compression can be integrated into a relational database system. Specifically, we describe how the storage manager, the query execution engine, and the query optimizer of a database system can be extended to deal with compressed data. Our main result is that compression can significantly improve the response time of queries if very light-weight compression techniques are used. We will present such light-weight compression techniques and give the results of running the TPC-D benchmark on a so compressed database and a non-compressed database using the AODB database system, an experimental database system that was developed at the Universities of Mannheim and Passau. Our benchmark results demonstrate that compression indeed offers high performance gains (up to 50 ) for IO-intensive queries and moderate gains for CPU-intensive queries. Compression can, however, also increase the running time of certain update operations. In all, we recommend to extend today's database systems with light-weight compression techniques and to make extensive use of this feature.