On analyzing errors in a selectivity estimation method based on dynamic maintenance of data distribution

초록

In this paper, we discuss the errors in selectivity estimation using the multilevel grid file (MLGF), a multidimensional file structure. We first demonstrate that estimation errors stem from the uniformity assumption that records are uniformly distributed in their belonging region represented by an entry in a level of an MLGF directory. Based on this demonstration, we then investigate five factors affecting the accuracy of estimation: (1) the data distribution in a region, (2) the number of records stored in an MLGF, (3) the page size, (4) the query region size, and (5) the level of an MLGF directory. Next, we present the tendency of estimation errors according to the change of values for each factor through extensive experiments. The results show that the errors decrease when: (1) the distribution of records in a region becomes closer to the uniform one; (2) the number of records in an MLGF increases; (3) the page size decreases; (4) the query region size increases; and (5) the level of an MLGF directory employed as data distribution information becomes lower. After defining the Granule Ratio, the core formula representing the basic relationship between the estimation errors and the above factors, we finally examine the change of estimation errors according to the change of the values for the Granule Ratio through experiments. The results indicate that errors tend to be similar depending on the values for the Granule Ratio regardless of the various changes of the values for the factors.