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.
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BibTeX
Printed Edition
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
Contents BibTeX
[Index Terms]
[Full Text in PDF Format, 1194 KB]
References
- [1]
- 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
- [3]
- ...
- [4]
- ...
- [5]
- ...
- [6]
- 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
- [7]
- Himanshu Gupta, Venky Harinarayan, Anand Rajaraman, Jeffrey D. Ullman:
Index Selection for OLAP.
ICDE 1997: 208-219 BibTeX
- [8]
- Venky Harinarayan, Anand Rajaraman, Jeffrey D. Ullman:
Implementing Data Cubes Efficiently.
SIGMOD Conference 1996: 205-216 BibTeX
- [9]
- Ching-Tien Ho, Rakesh Agrawal, Nimrod Megiddo, Ramakrishnan Srikant:
Range Queries in OLAP Data Cubes.
SIGMOD Conference 1997: 73-88 BibTeX
- [10]
- Kenneth A. Ross, Divesh Srivastava:
Fast Computation of Sparse Datacubes.
VLDB 1997: 116-125 BibTeX
- [11]
- ...
- [12]
- ...
- [13]
- Yihong Zhao, Prasad Deshpande, Jeffrey F. Naughton:
An Array-Based Algorithm for Simultaneous Multidimensional Aggregates.
SIGMOD Conference 1997: 159-170 BibTeX
Referenced by
- Mirek Riedewald, Divyakant Agrawal, Amr El Abbadi:
Flexible Data Cubes for Online Aggregation.
ICDT 2001: 159-173
- Prasad Deshpande, Jeffrey F. Naughton:
Aggregate Aware Caching for Multi-Dimensional Queries.
EDBT 2000: 167-182
- Chee Yong Chan, Yannis E. Ioannidis:
Hierarchical Prefix Cubes for Range-Sum Queries.
VLDB 1999: 675-686
BibTeX
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