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Title: Minimizing I/O Costs of Multi-Dimensional Queries with BitmapIndices

Abstract

Bitmap indices have been widely used in scientific applications and commercial systems for processing complex,multi-dimensional queries where traditional tree-based indices would not work efficiently. A common approach for reducing the size of a bitmap index for high cardinality attributes is to group ranges of values of an attribute into bins and then build a bitmap for each bin rather than a bitmap for each value of the attribute. Binning reduces storage costs,however, results of queries based on bins often require additional filtering for discarding it false positives, i.e., records in the result that do not satisfy the query constraints. This additional filtering,also known as ''candidate checking,'' requires access to the base data on disk and involves significant I/O costs. This paper studies strategies for minimizing the I/O costs for ''candidate checking'' for multi-dimensional queries. This is done by determining the number of bins allocated for each dimension and then placing bin boundaries in optimal locations. Our algorithms use knowledge of data distribution and query workload. We derive several analytical results concerning optimal bin allocation for a probabilistic query model. Our experimental evaluation with real life data shows an average I/O cost improvement of at least a factor of 10 formore » multi-dimensional queries on datasets from two different applications. Our experiments also indicate that the speedup increases with the number of query dimensions.« less

Authors:
; ;
Publication Date:
Research Org.:
Ernest Orlando Lawrence Berkeley NationalLaboratory, Berkeley, CA (US)
Sponsoring Org.:
USDOE Director. Office of Science. Office of AdvancedScientific Computing Research
OSTI Identifier:
898945
Report Number(s):
LBNL-59949
R&D Project: 429201; BnR: KJ0101030; TRN: US200706%%450
DOE Contract Number:
DE-AC02-05CH11231
Resource Type:
Conference
Resource Relation:
Conference: International Conference on Scientific andStatistical Database Management (SSDBM 2006), Vienna, Austria, July 3-5,2006
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ALGORITHMS; DIMENSIONS; DISTRIBUTION; EVALUATION; MANAGEMENT; PROCESSING; STORAGE; Bitmap index query optimization databases

Citation Formats

Rotem, Doron, Stockinger, Kurt, and Wu, Kesheng. Minimizing I/O Costs of Multi-Dimensional Queries with BitmapIndices. United States: N. p., 2006. Web.
Rotem, Doron, Stockinger, Kurt, & Wu, Kesheng. Minimizing I/O Costs of Multi-Dimensional Queries with BitmapIndices. United States.
Rotem, Doron, Stockinger, Kurt, and Wu, Kesheng. Thu . "Minimizing I/O Costs of Multi-Dimensional Queries with BitmapIndices". United States. doi:. https://www.osti.gov/servlets/purl/898945.
@article{osti_898945,
title = {Minimizing I/O Costs of Multi-Dimensional Queries with BitmapIndices},
author = {Rotem, Doron and Stockinger, Kurt and Wu, Kesheng},
abstractNote = {Bitmap indices have been widely used in scientific applications and commercial systems for processing complex,multi-dimensional queries where traditional tree-based indices would not work efficiently. A common approach for reducing the size of a bitmap index for high cardinality attributes is to group ranges of values of an attribute into bins and then build a bitmap for each bin rather than a bitmap for each value of the attribute. Binning reduces storage costs,however, results of queries based on bins often require additional filtering for discarding it false positives, i.e., records in the result that do not satisfy the query constraints. This additional filtering,also known as ''candidate checking,'' requires access to the base data on disk and involves significant I/O costs. This paper studies strategies for minimizing the I/O costs for ''candidate checking'' for multi-dimensional queries. This is done by determining the number of bins allocated for each dimension and then placing bin boundaries in optimal locations. Our algorithms use knowledge of data distribution and query workload. We derive several analytical results concerning optimal bin allocation for a probabilistic query model. Our experimental evaluation with real life data shows an average I/O cost improvement of at least a factor of 10 for multi-dimensional queries on datasets from two different applications. Our experiments also indicate that the speedup increases with the number of query dimensions.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Mar 30 00:00:00 EST 2006},
month = {Thu Mar 30 00:00:00 EST 2006}
}

Conference:
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