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Materialized View Selection for Multidimensional Datasets |
Full Paper (PDF)
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To fulfill the requirement of fast interactive multidimensional data analysis, database systems precompute aggregate views on some subsets of dimensions and their corresponding hierarchies.
However, the problem of what to precompute is difficult and intriguing.
The leading existing algorithm, BPUS, has a running time that is polynomial in the number of views and is guaranteed to be within (0.63 - f) of optimal, where f is the fraction of available space consumed by the largest aggregate.
Unfortunately, BPUS can be impractically slow, and in some instances may miss good solutions due to the coarse granularity at which it makes its decisions of what to precompute.
In view of this, we study the structure of the precomputation problem and show that under certain broad conditions on the multidimensional data, an even simpler and faster algorithm, PBS, achieves the same (0.63 - f) bound.
Our empirical study of the behavior of PBS shows that even when this condition does not hold, PBS picks a surprisingly good set of aggregates for precomputation.
Furthermore, BPUS and other previous work has assumed that all aggregates are either precomputed in their entirety or not at all.
We show that if one relaxes this and allows aggregates to be partially precomputed, not only is it possible to find solutions that are better than those found by previous algorithms, in some cases it is even possible to find solutions that are better than the solution that is 'optimal' by the previous definition.
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References, where available, link to the DBLP on the World Wide Web.
[AAD+96]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[BPT97]Elena Baralis, Stefano Paraboschi, Ernest Teniente:
Materialized Views Selection in a Multidimensional Database.
VLDB 1997: 156-165[DRSN98]Prasad Deshpande, Karthikeyan Ramasamy, Amit Shukla, Jeffrey F. Naughton:
Caching Multidimensional Queries Using Chunks.
SIGMOD Conference 1998: 259-270[GBLP96]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[GHRU97]Himanshu Gupta, Venky Harinarayan, Anand Rajaraman, Jeffrey D. Ullman:
Index Selection for OLAP.
ICDE 1997: 208-219[Gupt97]Himanshu Gupta:
Selection of Views to Materialize in a Data Warehouse.
ICDT 1997: 98-112[HRU96]Venky Harinarayan, Anand Rajaraman, Jeffrey D. Ullman:
Implementing Data Cubes Efficiently.
SIGMOD Conf. 1996: 205-216[RS97]Kenneth A. Ross, Divesh Srivastava:
Fast Computation of Sparse Datacubes.
VLDB 1997: 116-125[SS94]Sunita Sarawagi, Michael Stonebraker:
Efficient Organization of Large Multidimensional Arrays.
ICDE 1994: 328-336[SDNR96]Amit Shukla, Prasad Deshpande, Jeffrey F. Naughton, Karthikeyan Ramasamy:
Storage Estimation for Multidimensional Aggregates in the Presence of Hierarchies.
VLDB 1996: 522-531[SDN98]...
[Ull96]Jeffrey D. Ullman:
Efficient Implementation of Data Cubes Via Materialized Views.
KDD 1996: 386-388[ZDN97]Yihong Zhao, Prasad Deshpande, Jeffrey F. Naughton:
An Array-Based Algorithm for Simultaneous Multidimensional Aggregates.
SIGMOD Conference 1997: 159-170
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@inproceedings{DBLP:conf/vldb/ShuklaDN98,
author = {Amit Shukla and
Prasad Deshpande and
Jeffrey F. Naughton},
editor = {Ashish Gupta and
Oded Shmueli and
Jennifer Widom},
title = {Materialized View Selection for Multidimensional Datasets},
booktitle = {VLDB'98, Proceedings of 24rd International Conference on Very
Large Data Bases, August 24-27, 1998, New York City, New York,
USA},
publisher = {Morgan Kaufmann},
year = {1998},
isbn = {1-55860-566-5},
pages = {488-499},
crossref = {DBLP:conf/vldb/98},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
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DBLP: Copyright ©1999 by Michael Ley (ley@uni-trier.de).
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