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Abstract:
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Growing complexity of enterprise -wide data and business processes necessitates the efficiency of complex decision support set queries . However , contemporary DBMS remain unsuccessful in handling set queries efficiently . In this thesis we propose efficient set query processing methods using bitmap index . The methods use bitmap vectors to represent attributes values in binary format . The methods test groups within a schema in a hierarchical fashion . Satisfying groups are bisected further and checked recursively while non -satisfying groups are pruned resulting in significant reduction in response times . In addition , our iterative implementation avoids the inefficiency that can be introduced by recursive implementation by reading the same bitmap vector for intersection many times . We also introduce pre -processing methods to reduce the complexity of the bitmap vectors , thus to improve the efficiency . Our implementation is based on FastBit , an open -source efficient compressed bitmap index framework . Experimental results on large datasets and comparison with results from PostgreSQL prove that our approach is superior owing to the fact that we are able to discard non -satisfying groups and capably optimize complex queries . |