NDLI: NiagaraCQ: a scalable continuous query system for Internet databases

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NiagaraCQ: a scalable continuous query system for Internet databases

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NiagaraCQ: a scalable continuous query system for Internet databases

Content Provider	ACM Digital Library
Author	Chen, Jianjun Wang, Yuan DeWitt, David J. Tian, Feng
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.
Starting Page	379
Ending Page	390
Page Count	12
File Format	PDF
ISBN	1581132174
DOI	10.1145/342009.335432
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2000-05-16
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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