Evaluating holistic aggregators efficiently for very large datasets

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Lixin Fu, Associate Professor (Creator)
Institution
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/

Abstract: In data warehousing applications, numerous OLAP 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 sub-buckets 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.

Additional Information

Publication
The VLDB Journal, Volume 13, Number 2 (May 2004), pp. 148-161.
Language: English
Date: 2004
Keywords
Quantiles, Dynamic bucketing, Aggregation