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VLDB
2004
ACM

Evaluating holistic aggregators efficiently for very large datasets

13 years 2 hour ago
Evaluating holistic aggregators efficiently for very large datasets
Indatawarehousingapplications,numerousOLAP queries involve the processing of holistic aggregators such as computing the "top n," median, quantiles, etc. In this paper, we present a novel approach called dynamic bucketing to efficiently evaluate these aggregators. We partition data into equiwidth buckets and further partition dense buckets into subbuckets as needed by allocating and reclaiming memory space. The bucketing process dynamically adapts to the input order and distribution of input datasets. The histograms of the buckets and subbuckets are stored in our new data structure called structure trees. A recent selection algorithm based on regular sampling is generalized and its analysis extended. We have also compared our new algorithms with this generalized algorithm and several other recent algorithms. Experimental results show that our new algorithms significantly outperform prior ones not only in the runtime but also in accuracy.
Lixin Fu, Sanguthevar Rajasekaran
Added 05 Dec 2009
Updated 05 Dec 2009
Type Conference
Year 2004
Where VLDB
Authors Lixin Fu, Sanguthevar Rajasekaran
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