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Brief Introduction to Bitmap Indices for Scientific Data

Brief Introduction to Bitmap Indices for Scientific Data. Kurt Stockinger CERN, IT-Division, Database Group Geneva, Switzerland Database Workshop, July 11-13, Geneva, Switzerland. Features of Bitmap Indices. Multi-dim. index data structure which is optimised for read-only data

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Brief Introduction to Bitmap Indices for Scientific Data

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  1. Brief Introduction to Bitmap Indices for Scientific Data Kurt Stockinger CERN, IT-Division, Database Group Geneva, Switzerland Database Workshop, July 11-13, Geneva, Switzerland

  2. Features of Bitmap Indices • Multi-dim. index data structure which is optimised for read-only data • “Good” performance for multi-dim. queries with low selectivity (few records result from the query) • Applied in Data Warehouses and Decision Support Systems(e.g. Oracle, Informix, Sybase)

  3. Encoding Techniques forDiscrete Attribute Values a) list of attributes b) equality encoding c) range encoding Attribute cardinality = 10 Range encoding optimised for one-sided range queries, e.g. a0 <= 3

  4. Pros and Cons of Bitmap Indices (BMI) • Pros: • Easy to build and to maintain • Easy to identify records that satisfy a complex multi-attribute predicate(multi-dim. ad-hoc queries) • Bit-wise operators (AND, OR, XOR, NOT) are very efficiently supported by HW • Very space efficient for attributes with low cardinality (number of distinct attribute values, e.g. “Yes”, “No”) • Cons: • Space inefficient for attributes with high cardinality • Commercial database systems only “efficiently” support bitmap indices for discrete attribute values

  5. Example: Bitmap Indices for HEP Data attribute indices (bit matrices) Events(bit vectors) bins (bit slices)

  6. 2-Sided Range Query • E.g.:(pT > 25.7) && (pT < 91.8) 1) Candidate slices 3) OR 2)Hit slices 5) “Check” 4) OR Bin ranges: [0;20) [20;40)[40;60) [60;80)[80;100) ...

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