New Algorithm for Computing Cube on Very Large Compressed Data Sets

Data compression is an effective technique to improve the performance of data warehouses. Since cube operation represents the core of online analytical processing in data warehouses, it is a major challenge to develop efficient algorithms for computing cube on compressed data warehouses. To our know...

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Bibliographic Details
Published inIEEE transactions on knowledge and data engineering Vol. 18; no. 12; pp. 1667 - 1680
Main Authors Wu, W., Hong Gao, Jianzhong Li
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.12.2006
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithm design and analysis
Algorithms
Applied sciences
Compressed
Computation
Computer applications
Computer science; control theory; systems
Costs
cube operation
Cubes
Data analysis
Data compression
Data processing. List processing. Character string processing
Data warehouses
Data warehousing
Decision making
Decompressing
Exact sciences and technology
Heuristic algorithms
Memory organisation. Data processing
Multidimensional systems
OLAP
Software
Input output
On line
Literature
Optimal algorithm
Data compression
Very large databases
Data warehouses
Data processing
OLAP
Multidimensional database
Cube
Algorithm complexity
Heuristic method
cube operation
Data warehouse
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Summary:Data compression is an effective technique to improve the performance of data warehouses. Since cube operation represents the core of online analytical processing in data warehouses, it is a major challenge to develop efficient algorithms for computing cube on compressed data warehouses. To our knowledge, very few cube computation techniques have been proposed for compressed data warehouses to date in the literature. This paper presents a novel algorithm to compute cubes on compressed data warehouses. The algorithm operates directly on compressed data sets without the need of first decompressing them. The algorithm is applicable to a large class of mapping complete data compression methods. The complexity of the algorithm is analyzed in detail. The analytical and experimental results show that the algorithm is more efficient than all other existing cube algorithms. In addition, a heuristic algorithm to generate an optimal plan for computing cube is also proposed
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2006.195