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FastBit Reference Manual
Author(s) -
Kesheng Wu
Publication year - 2007
Publication title -
lawrence berkeley national laboratory
Language(s) - English
Resource type - Reports
DOI - 10.2172/913270
Subject(s) - bitmap , bitwise operation , computer science , search engine indexing , tree (set theory) , byte , compression ratio , data mining , b tree , compression (physics) , code (set theory) , data compression , algorithm , binary tree , information retrieval , set (abstract data type) , mathematics , artificial intelligence , mathematical analysis , materials science , automotive engineering , engineering , composite material , programming language , internal combustion engine , operating system
An index in a database system is a data structure that utilizes redundant information about the base data to speed up common searching and retrieval operations. Most commonly used indexes are variants of B-trees, such as B+-tree and B*-tree. FastBit implements a set of alternative indexes call compressed bitmap indexes. Compared with B-tree variants, these indexes provide very efficient searching and retrieval operations by sacrificing the efficiency of updating the indexes after the modification of an individual record. In addition to the well-known strengths of bitmap indexes, FastBit has a special strength stemming from the bitmap compression scheme used. The compression method is called the Word-Aligned Hybrid (WAH) code. It reduces the bitmap indexes to reasonable sizes and at the same time allows very efficient bitwise logical operations directly on the compressed bitmaps. Compared with the well-known compression methods such as LZ77 and Byte-aligned Bitmap code (BBC), WAH sacrifices some space efficiency for a significant improvement in operational efficiency. Since the bitwise logical operations are the most important operations needed to answer queries, using WAH compression has been shown to answer queries significantly faster than using other compression schemes. Theoretical analyses showed that WAH compressed bitmap indexes are optimal for one-dimensional range queries. Only the most efficient indexing schemes such as B+-tree and B*-tree have this optimality property. However, bitmap indexes are superior because they can efficiently answer multi-dimensional range queries by combining the answers to one-dimensional queries

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