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NiagaraCQ: A Scalable Continuous Query System for Internet Databases (2000)

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by Jianjun Chen , David J. Dewitt , Feng Tian , Yuan Wang
Venue:In SIGMOD
Citations:583 - 9 self
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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   

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