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.
In @cite_17 , Xi predict the buffer hit rate using a Markov chain model for a given buffer pool size. In our article, instead of the buffer hit rate, we estimate the expected number of pages brought into the memory from the disk, because it is proportional to the retrieval time. Another difference is that we usually start with a cold (that is empty) cache and investigate its increase together with the decrease in retrieval time. In @cite_17 , the authors fix the size of the buffer pool and then predict the buffer hit rate with the Markov chain model.
@cite_17
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Computing multiple related group-bys and aggregates is one of the core operations of On-Line Analytical Processing (OLAP) applications. Recently, [GBLP95] proposed the “Cube” operator, which computes group-by aggregations over all possible subsets of the specified dimensions. The rapid acceptance of the importance of this operator has led to a variant of the Cube being proposed for the SQL standard. Several efficient algorithms for Relational OLAP (ROLAP) have been developed to compute the Cube. However, to our knowledge there is nothing in the literature on how to compute the Cube for Multidimensional OLAP (MOLAP) systems, which store their data in sparse arrays rather than in tables. In this paper, we present a MOLAP algorithm to compute the Cube, and compare it to a leading ROLAP algorithm. The comparison between the two is interesting, since although they are computing the same function, one is value-based (the ROLAP algorithm) whereas the other is position-based (the MOLAP algorithm). Our tests show that, given appropriate compression techniques, the MOLAP algorithm is significantly faster than the ROLAP algorithm. In fact, the difference is so pronounced that this MOLAP algorithm may be useful for ROLAP systems as well as MOLAP systems, since in many cases, instead of cubing a table directly, it is faster to first convert the table to an array, cube the array, then convert the result back to a table.