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Erschienen in:
2013 | OriginalPaper | Buchkapitel
A Genetic Algorithm Approach for Minimizing the Number of Columnar Runs in a Column Store Table
verfasst von : Jane Jovanovski, Maja Siljanoska, Goran Velinov
Erschienen in: Adaptive and Natural Computing Algorithms
Verlag: Springer Berlin Heidelberg
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Column-oriented database systems, usually referred to as column stores, organize data in a column-wise manner. Column-wise data can be compressed efficiently, improving the performance of large read-mostly data repositories such as data warehouses. Many compression algorithms exploit the similarity among the column values, where repeats of the same value form columnar runs. In this paper we present a genetic algorithm for determining an optimal column sorting order which will minimize the number of columnar runs in a column store table and therefore maximize the RLE-based table compression. Experiments show that the algorithm performs consistently well on synthetic table instances as well as realistic datasets, resulting with higher run-reduction efficiency compared to existing heuristic for solving the given problem.