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https://hdl.handle.net/2142/81791
Description
Title
Indexing Scientific Data
Author(s)
Sinha, Rishi Rakesh
Issue Date
2007
Doctoral Committee Chair(s)
Winslett, Marianne
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
Abstract
To address these three problems, we introduced multi-resolution bitmap indexes, which group data into bins at multiple granularities. We achieved a query performance which is 10 times faster than traditional bitmap indexes by using bitmap indexes built at these multiple granularities. To address the issue of size, we introduced an adaptive version of multi-resolution bitmap indexes. The adaptive index adds and drops auxiliary indexes as needed for the query workload and is a fraction of the size of the data being indexed. We achieved a performance improvement of a factor of 6, compared to an ordinary multi-resolution bitmap index of the same size. We also introduced a novel algorithm to consolidate data points into regions of interest. By exploiting the special properties of compressed bitmap indexes and scientific meshes we achieved sublinear running times, with respect to the number of points in the query result, for both the index lookup and region consolidation.
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