Download Data Access Operations in OLAP

Sparse Data Storage of Multi-Dimensional Data
for OLAP and Data Mining
Sanjay Goil
Alok Choudhary
Department of Electrical & Computer Engineering and
Center for Parallel and Distributed Computing,
Northwestern University,
Technological Institute
데이터베이스 연구실
석사 2학기
임성은
OLAP Seminar
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Contents
 Introduction
 Multi-dimensional databases
Multi-dimensional Arrays
Compressed Multi-dimensional Arrays
 Sparse Data Storage
Bit-Encoded Sparse Storage(BESS)
 Other Sparse Storage Schemes
Bit-Index-Value pairs
Offset-Value pairs
Compressed Sparse Dimensions
Sparse-Dense split storage
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Contents
 Data Access Operations in OLAP and Data Mining
Data Cubes
Basic Operations
 Data Access Performance
Operations
Choosing a sparse structure
 Performance Results
Decoding dimension index values
Retrieval along a dimension and Range Queries
Aggregation along a combination of dimensions
 Conclusions
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Introduction
Introduction of BESS
retrieval of dimensional index for OLAP operation along
specified dimensions : bit operation
A comparison with other sparse data structure
Analysis and performance results in terms of design
parameters :
number of dimensions, dimension sizes, sparsity of data,
cell size
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Multi-dimensional databases
Multi-Dimensional databases
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Multi-dimensional databases
Multi-Dimensional databases
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Multi-dimensional databases
Multi-Dimensional Arrays
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Multi-dimensional databases
Compressed Multi-Dimensional Arrays
Figure 3: Storage of data in chunks
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Sparse Data Storage
Sparse Data Storage
Figure 4: Dense and sparse chunk storage for the datacube
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Sparse Data Storage
Bit-Encoded Sparse Storage (BESS)
Figure 5: Bit Encoded Sparse Storage
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Sparse Data Storage
Bit-Encoded Sparse Storage (BESS)
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Sparse Data Storage
Bit-Encoded Sparse Storage (BESS)
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Other Sparse Storage Schemes
Index-Value pairs
Offset-Value pairs
Compressed Sparse
Dimensions
Figure 8 : Sparse data structures for a
sample sparse array
(a) Index-Value pairs
(b) Offset- Value pairs
(c) Compressed Sparse Dimensions
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Other Sparse Storage Schemes
Sparse-Dense split storage
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Data Access Operations in OLAP
Data Cubes
Pivoting(Rotating) : rotating the cube to change the
dimensional orientation of a report or page on display
Slicing-dicing : selecting some subset of the cube
Roll-up : going up the hierarchy to higher levels of
generation
Drill-Down : traversing the hierarchy from lower to
higher levels of detail
Trend analysis over sequential time periods
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Data Access Operations in OLAP
Basic Operations
Retrieval of a random cell element
Retrieval along a dimension or a combination of
dimensions
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Data Access Operations in OLAP
Basic Operations
Retrieval for values of dimensions within a range (Range
Queries)
Aggregation operations on dimensions
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Data Access Operations in OLAP
Basic Operations
Aggregation operations on dimensions
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Data Access Operations in OLAP
Basic Operations
Multi-dimensional Aggregation
(Generalization/Consolidation: lower to higher level in
hierarchy)
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Data Access Performance
Data Access Performance
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Data Access Performance
Operations
Retrieval of a random cell element
Retrieval along a dimension
Aggregation of a dimension
Choosing a sparse structure
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Performance Results
Decoding dimension index values
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Performance Results
Decoding dimension index values
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Performance Results
Decoding dimension index values
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Performance Results
Decoding dimension index values
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Performance Results
Retrieval along a dimension and Range Queries
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Performance Results
Aggregation along a combination of dimensions
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Conclusions
Performance results show better performance for
OLAP queries using BESS over other storage
schemes.
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