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• W2108441106 abstract "Aggregation is an operation that plays a key role in multidimensional OLAP (MOLAP). Existing aggregation methods in MOLAP have been proposed for file structures such as multidimensional arrays. These file structures are suitable for data with uniform distributions, but do not work well with skewed distributions. In this paper, we consider an aggregation method that uses dynamic multidimensional files adapting to skewed distributions. In these multidimensional files, the sizes of page regions vary according to the data density in these regions, and the pages that belong to a larger region are accessed multiple times while computing aggregations. To solve this problem, we first present an aggregation computation model, called the Disjoint-Inclusive Partition (DIP) computation model, that is the formal basis of our approach. Based on this model, we then present the one-pass aggregation algorithm. This algorithm computes aggregations using the one-pass buffer size, which is the minimum buffer size required for guaranteeing one disk access per page. We prove that our aggregation algorithm is optimal with respect to the one-pass buffer size under our aggregation computation model. Using the DIP computation model allows us to correctly predict the order of accessing data pages in advance. Thus, our algorithm achieves the optimal one-pass buffer size by using a buffer replacement policy, such as Belady's B0 or Toss-Immediate policies, that exploits the page access order computed in advance. Since the page access order is not known a priori in general, these policies have been known to lack practicality despite its theoretic significance. Nevertheless, in this paper, we show that these policies can be effectively used for aggregation computation.We have conducted extensive experiments. We first demonstrate that the one-pass buffer size theoretically derived is indeed correct in real environments. We then compare the performance of the one-pass algorithm with those of other ones. Experimental results for a real data set show that the one-pass algorithm reduces the number of disk accesses by up to 7.31 times compared with a naive algorithm. We also show that the memory requirement of our algorithm for processing the aggregation in one-pass is very small being 0.05%|0.6% of the size of the database. These results indicate that our algorithm is practically usable even for a fairly large database. We believe our work provides an excellent formal basis for investigating further issues in computing aggregations in MOLAP." @default.
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• W2108441106 date "2002-01-01" @default.
• W2108441106 modified "2023-09-28" @default.
• W2108441106 title "A One-Pass Aggregation Algorithm with the Optimal Buffer Size in Multidimensional OLAP" @default.
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• W2108441106 doi "https://doi.org/10.1016/b978-155860869-6/50075-5" @default.
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