high_scalability high_scalability-2009 high_scalability-2009-587 knowledge-graph by maker-knowledge-mining

587 high scalability-2009-05-01-FastBit: An Efficient Compressed Bitmap Index Technology


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Introduction: Data mining and fast queries are always in that bin of hard to do things where doing something smarter can yield big results. Bloom Filters are one such do it smarter strategy, compressed bitmap indexes are another. In one application "FastBit outruns other search indexes by a factor of 10 to 100 and doesn’t require much more room than the original data size." The data size is an interesting metric. Our old standard b-trees can be two to four times larger than the original data. In a test searching an Enron email database FastBit outran MySQL by 10 to 1,000 times. FastBit is a software tool for searching large read-only datasets. It organizes user data in a column-oriented structure which is efficient for on-line analytical processing (OLAP), and utilizes compressed bitmap indices to further speed up query processing. Analyses have proven the compressed bitmap index used in FastBit to be theoretically optimal for one-dimensional queries. Compared with other optimal indexing me


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1 Data mining and fast queries are always in that bin of hard to do things where doing something smarter can yield big results. [sent-1, score-0.486]

2 Bloom Filters are one such do it smarter strategy, compressed bitmap indexes are another. [sent-2, score-0.876]

3 In one application "FastBit outruns other search indexes by a factor of 10 to 100 and doesn’t require much more room than the original data size. [sent-3, score-0.391]

4 Our old standard b-trees can be two to four times larger than the original data. [sent-5, score-0.268]

5 In a test searching an Enron email database FastBit outran MySQL by 10 to 1,000 times. [sent-6, score-0.167]

6 FastBit is a software tool for searching large read-only datasets. [sent-7, score-0.158]

7 It organizes user data in a column-oriented structure which is efficient for on-line analytical processing (OLAP), and utilizes compressed bitmap indices to further speed up query processing. [sent-8, score-1.209]

8 Analyses have proven the compressed bitmap index used in FastBit to be theoretically optimal for one-dimensional queries. [sent-9, score-0.97]

9 Compared with other optimal indexing methods, bitmap indices are superior because they can be efficiently combined to answer multi-dimensional queries whereas other optimal methods can not. [sent-10, score-1.442]

10 An excellent description of how FastBit works, especially compared to b-trees. [sent-13, score-0.174]


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