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Restructuring batch view maintenance efficiently
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Source Conference on Information and Knowledge Management archive
Proceedings of the thirteenth ACM international conference on Information and knowledge management table of contents
Washington, D.C., USA
POSTER SESSION: Posters P-2 table of contents
Pages: 228 - 229  
Year of Publication: 2004
ISBN:1-58113-874-1
Authors
Bin Liu  Worcester Polytechnic Institute, MA
Elke A. Rundensteiner  Worcester Polytechnic Institute, MA
David Finkel  Worcester Polytechnic Institute, MA
Sponsors
SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 6,   Downloads (12 Months): 25,   Citation Count: 1
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ABSTRACT

Materialized views defined over distributed data sources are a well recognized technology for modern applications. State-of the-art incremental view maintenance requires O(<i>n</i><sup>2</sup>) or more maintenance queries to remote data sources with <i>n</i> being the number of data sources in the view definition. In this poster, we illustrate basic ideas of novel view maintenance strategies that dramatically reduce the number of maintenance queries. Such reduction brings the tradeoff between the number of maintenance queries and the complexity of each query. These algorithms have been implemented in a working prototype system. Experimental studies illustrate major performance improvement in terms of total processing time compared with existing batch algorithms.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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A. Gupta and I. Mumick. Maintenance of Materialized Views: Problems, Techniques, and Applications. IEEE Data Engineering Bulletin, 18(2):3--19, 1995.
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B. Liu, E. A. Rundensteiner, and D. Finkel. Restructuring View Maintenance Plans for Large Update Batches. Technical Report WPI-CS-TR-03-29, WPI, 2003.
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Collaborative Colleagues:
Bin Liu: colleagues
Elke A. Rundensteiner: colleagues
David Finkel: colleagues