arXiv subjects

István Szépkúti

Publications and source records attributed to István Szépkúti.

5 recordsLinked to original sources

arXiv2011

Caching in Multidimensional Databases

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 [9]. 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.

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arXiv2011

Difference Sequence Compression of Multidimensional Databases

The multidimensional databases often use compression techniques in order to decrease the size of the database. This paper introduces a new method called difference sequence compression. Under some conditions, this new technique is able to create a smaller size multidimensional database than others like single count header compression, logical position compression or base-offset compression. Keywords: compression, multidimensional database, On-line Analytical Processing, OLAP.

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arXiv2011

On the Scalability of Multidimensional Databases

It is commonly accepted in the practice of on-line analytical processing of databases that the multidimensional database organization is less scalable than the relational one. It is easy to see that the size of the multidimensional organization may increase very quickly. For example, if we introduce one additional dimension, then the total number of possible cells will be at least doubled. However, this reasoning does not takethe fact into account that the multidimensional organization can be compressed. There are compression techniques, which can remove all or at least a part of the empty cells from the multidimensional organization, while maintaining a good retrieval performance. Relational databases often use B-tree indices to speed up the access to given rows of tables. It can be proven, under some reasonable assumptions, that the total size of the table and the B-tree index is bigger than a compressed multidimensional representation. This implies that the compressed array results in a smaller database and faster access at the same time. This paper compares several compression techniques and shows when we should and should not apply compressed arrays instead of relational tables.

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arXiv2011

Difference-Huffman Coding of Multidimensional Databases

A new compression method called difference-Huffman coding (DHC) is introduced in this paper. It is verified empirically that DHC results in a smaller multidimensional physical representation than those for other previously published techniques (single count header compression, logical position compression, base-offset compression and difference sequence compression). The article examines how caching influences the expected retrieval time of the multidimensional and table representations of relations. A model is proposed for this, which is then verified with empirical data. Conclusions are drawn, based on the model and the experiment, about when one physical representation outperforms another in terms of retrieval time. Over the tested range of available memory, the performance for the multidimensional representation was always much quicker than for the table representation.

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arXiv2011

Multidimensional or Relational? / How to Organize an On-line Analytical Processing Database

In the past few years, the number of OLAP applications increased quickly. These applications use two significantly different DB structures: multidimensional (MD) and table-based. One can show that the traditional model of relational databases cannot make difference between these two structures. Another model is necessary to make the differences visible. One of these is the speed of the system. It can be proven that the multidimensional DB organization results in shorter response times. And it is crucial, since a manager may become impatient, if he or she has to wait say more than 20 seconds for the next screen. On the other hand, we have to pay for the speed with a bigger DB size. Why does the size of MD databases grow so quickly? The reason is the sparsity of data: The MD matrix contains many empty cells. Efficient handling of sparse matrices is indispensable in an OLAP application. One way to handle sparsity is to take the structure closer to the table-based one. Thus the DB size decreases, while the application gets slower. Therefore, other methods are needed. This paper deals with the comparison of the two DB structures and the limits of their usage. The new results of the paper: (1) It gives a constructive proof that all relations can be represented in MD arrays. (2) It also shows when the MD array representation is quicker than the table-based one. (3) The MD representation results in smaller DB size under some conditions. One such sufficient condition is proved in the paper. (4) A variation of the single count header compression scheme is described with an algorithm, which creates the compressed array from the ordered table without materializing the uncompressed array. (5) The speed of the two different database organizations is tested with experiments, as well. The tests are done on benchmark as well as real life data. The experiments support the theoretical results.

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