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

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

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 for discrete attribute values

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 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

Example: Bitmap Indices for HEP Data

attribute indices (bit matrices)

Events(bit vectors)

bins (bit slices)


2 sided range query

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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