Brief introduction to bitmap indices for scientific data
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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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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

  • “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)


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


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


Example: Bitmap Indices for HEP Data

attribute indices (bit matrices)

Events(bit vectors)

bins (bit slices)


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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