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NiagaraCQ: A Scalable Continuous Query System for Internet Databases (2000)
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| Venue: | In SIGMOD |
| Citations: | 584 - 9 self |
BibTeX
@INPROCEEDINGS{Chen00niagaracq:a,
author = {Jianjun Chen and David J. Dewitt and Feng Tian and Yuan Wang},
title = {NiagaraCQ: A Scalable Continuous Query System for Internet Databases},
booktitle = {In SIGMOD},
year = {2000},
pages = {379--390}
}
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Abstract
Continuous queries are persistent queries that allow users to receive new results when they become available. While continuous query systems can transform a passive web into an active environment, they need to be able to support millions of queries due to the scale of the Internet. No existing systems have achieved this level of scalability. NiagaraCQ addresses this problem by grouping continuous queries based on the observation that many web queries share similar structures. Grouped queries can share the common computation, tend to fit in memory and can reduce the I/O cost significantly. Furthermore, grouping on selection predicates can eliminate a large number of unnecessary query invocations. Our grouping technique is distinguished from previous group optimization approaches in the following ways. First, we use an incremental group optimization strategy with dynamic re-grouping. New queries are added to existing query groups, without having to regroup already installed queries. Second, we use a query-split scheme that requires minimal changes to a general-purpose query engine. Third, NiagaraCQ groups both change-based and timer-based queries in a uniform way. To insure that NiagaraCQ is scalable, we have also employed other techniques including incremental evaluation of continuous queries, use of both pull and push models for detecting heterogeneous data source changes, and memory caching. This paper presents the design of NiagaraCQ system and gives some experimental results on the system’s performance and scalability. 1.
Keyphrases
continuous query internet database scalable continuous query system niagaracq system query-split scheme passive web grouping technique grouped query large number heterogeneous data source change persistent query query group niagaracq group system performance unnecessary query invocation active environment selection predicate memory caching new query common computation minimal change timer-based query dynamic re-grouping incremental group optimization strategy installed query new result uniform way incremental evaluation continuous query system following way push model experimental result general-purpose query engine previous group optimization approach
