Multi-resolution bitmap indexes for scientific data
ACM Transactions on Database Systems (TODS)
Breaking the Curse of Cardinality on Bitmap Indexes
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Histogram-aware sorting for enhanced word-aligned compression in bitmap indexes
Proceedings of the ACM 11th international workshop on Data warehousing and OLAP
New binning strategy for bitmap indices on high cardinality attributes
Proceedings of the 2nd Bangalore Annual Compute Conference
Sorting improves word-aligned bitmap indexes
Data & Knowledge Engineering
Optimizing adaptive multi-route query processing via time-partitioned indices
Journal of Computer and System Sciences
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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 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.