An Array-Based Algorithm for Simultaneous Multidimensional Aggregates.

Yihong Zhao, Prasad Deshpande, Jeffrey F. Naughton: An Array-Based Algorithm for Simultaneous Multidimensional Aggregates. SIGMOD Conference 1997: 159-170

@inproceedings{DBLP:conf/sigmod/ZhaoDN97,
  author    = {Yihong Zhao and
               Prasad Deshpande and
               Jeffrey F. Naughton},
  editor    = {Joan Peckham},
  title     = {An Array-Based Algorithm for Simultaneous Multidimensional Aggregates},
  booktitle = {SIGMOD 1997, Proceedings ACM SIGMOD International Conference
               on Management of Data, May 13-15, 1997, Tucson, Arizona, USA},
  publisher = {ACM Press},
  year      = {1997},
  pages     = {159-170},
  ee        = {http://doi.acm.org/10.1145/253260.253288, db/conf/sigmod/ZhaoDN97.html},
  crossref  = {DBLP:conf/sigmod/97},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Computing multiple related group-bys and aggregates is one of the core operations of On-Line Analytical Processing (OLAP) applications. Recently, Gray et al. [GBLP95] proposed the "Cube" operator, which computes group-by aggregations over all possible subsets of the specified dimensions. The rapid acceptance of the importance of this operator has led to a variant of the Cube being proposed for the SQL standard. Several efficient algorithms for Relational OLAP (ROLAP) have been developed to compute the Cube. However, to our knowledge there is nothing in the literature on how to compute the Cube for Multidimensional OLAP (MOLAP) systems, which store their data in sparse arrays rather than in tables. In this paper, we present a MOLAP algorithm to compute the Cube, and compare it to a leading ROLAP algorithm. The comparison between the two is interesting, since although they are computing the same function, one is value-based (the ROLAP algorithm) whereas the other is position-based (the MOLAP algorithm.) Our tests show that, given appropriate compression techniques, the MOLAP algorithm is significantly faster than the ROLAP algorithm. In fact, the difference is so pronounced that this MOLAP algorithm may be useful for ROLAP systems as well as MOLAP systems, since in many cases, instead of cubing a table directly, it is faster to first convert the table to an array, cube the array, then convert the result back to a table.

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Joan Peckham (Ed.): SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data, May 13-15, 1997, Tucson, Arizona, USA. ACM Press 1997

, SIGMOD Record 26(2), June 1997
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References

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[GBLP95]: Jim Gray, Adam Bosworth, Andrew Layman, Hamid Pirahesh: Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total. ICDE 1996: 152-159
[GC96]: George Colliat: OLAP, Relational, and Multidimensional Database Systems. SIGMOD Record 25(3): 64-69(1996)
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