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Dynamic Assembly of Views in Data Cubes.

John R. Smith, Chung-Sheng Li, Vittorio Castelli, Anant Jhingran: Dynamic Assembly of Views in Data Cubes. PODS 1998: 274-283
@inproceedings{DBLP:conf/pods/SmithLCJ98,
  author    = {John R. Smith and
               Chung-Sheng Li and
               Vittorio Castelli and
               Anant Jhingran},
  title     = {Dynamic Assembly of Views in Data Cubes},
  booktitle = {Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium
               on Principles of Database Systems, June 1-3, 1998, Seattle, Washington},
  publisher = {ACM Press},
  year      = {1998},
  isbn      = {0-89791-996-3},
  pages     = {274-283},
  ee        = {http://doi.acm.org/10.1145/275487.275518, db/conf/pods/SmithLCJ98.html},
  crossref  = {DBLP:conf/pods/98},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

In this paper, we present a method for dynamically assembling views in multi-dimensional data cubes in order to more efficiently support data analysis and querying involving aggregations. The proposed method decomposes the data cubes into an indexed hierarchy of view elements. The view elements differ from traditional data cube cells in that they correspond to partial and residual aggregations of the data cube. The view elements provide highly granular building blocks for synthesizing the aggregated and range-aggregated views of the data cubes.

We propose a strategy for selecting and materializing the view elements based on the frequency of view access. This allows the dynamic adaptation of the view element sets to patterns of retrieval. We present a fast and optimal algorithm for selecting non-expansive view element sets that minimize the processing costs for generating a population of aggregated views. We also present a greedy algorithm for selecting redundant view element sets in order to further reduce processing costs. We demonstrate that the view element approaches perform better in terms of lower processing and storage costs than methods based on materializing views.

Copyright © 1998 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.


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Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 1-3, 1998, Seattle, Washington. ACM Press 1998, ISBN 0-89791-996-3
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References

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Rakesh Agrawal, Ashish Gupta, Sunita Sarawagi: Modeling Multidimensional Databases. ICDE 1997: 232-243 BibTeX
[2]
Sameet Agarwal, Rakesh Agrawal, Prasad Deshpande, Ashish Gupta, Jeffrey F. Naughton, Raghu Ramakrishnan, Sunita Sarawagi: On the Computation of Multidimensional Aggregates. VLDB 1996: 506-521 BibTeX
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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 BibTeX
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Himanshu Gupta, Venky Harinarayan, Anand Rajaraman, Jeffrey D. Ullman: Index Selection for OLAP. ICDE 1997: 208-219 BibTeX
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Venky Harinarayan, Anand Rajaraman, Jeffrey D. Ullman: Implementing Data Cubes Efficiently. SIGMOD Conference 1996: 205-216 BibTeX
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Ching-Tien Ho, Rakesh Agrawal, Nimrod Megiddo, Ramakrishnan Srikant: Range Queries in OLAP Data Cubes. SIGMOD Conference 1997: 73-88 BibTeX
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Kenneth A. Ross, Divesh Srivastava: Fast Computation of Sparse Datacubes. VLDB 1997: 116-125 BibTeX
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Yihong Zhao, Prasad Deshpande, Jeffrey F. Naughton: An Array-Based Algorithm for Simultaneous Multidimensional Aggregates. SIGMOD Conference 1997: 159-170 BibTeX

Referenced by

  1. Mirek Riedewald, Divyakant Agrawal, Amr El Abbadi: Flexible Data Cubes for Online Aggregation. ICDT 2001: 159-173
  2. Prasad Deshpande, Jeffrey F. Naughton: Aggregate Aware Caching for Multi-Dimensional Queries. EDBT 2000: 167-182
  3. Chee Yong Chan, Yannis E. Ioannidis: Hierarchical Prefix Cubes for Range-Sum Queries. VLDB 1999: 675-686
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