NDLI: Efficient Construction of Compact Shedding Filters for Data Stream Processing

Content Provider	IEEE Xplore Digital Library
Author	Gedik, B. Kun-Lung Wu Yu, P.S.
Copyright Year	2008
Description	Author affiliation: Thomas J. Watson Res. Center, IBM Res., Hawthorne, NY (Gedik, B.; Kun-Lung Wu; Yu, P.S.)
Abstract	High-volume source streams, coupled with fluctuating rates, necessitate adaptive load shedding in data stream processing. When ignored, a continual query (CQ) server may randomly drop items, when its capacity is inadequate to handle the arriving data, and degrade the quality of the query results. To alleviate this problem, filters can be used at the source nodes. However, regular source filtering in itself is not sufficient to prevent random dropping, because the amount of data passing through the filters can still surpass the server's capacity. In this case, intelligent load shedding can be applied by the source filters to minimize the degradation in result quality. In this paper, we introduce a novel type of load-shedding source filters, called non- uniformly regulated (NR) sifters. An NR sifter judiciously applies varying amounts of load shedding to different regions of the data space within the sifter. We formulate the problem of constructing NR sifters as an optimization one. NR sifters are compact and quickly configurable, allowing frequent adaptations, and provide fast lookup f.or deciding if a data item should be dropped. We structure NR sifters as a set of (sifter region, drop threshold) pairs to achieve compactness, develop query consolidation techniques to enable quick construction, and introduce flexible space partitioning mechanisms to realize fast lookup.
Starting Page	396
Ending Page	405
File Size	5293800
Page Count	10
File Format	PDF
ISBN	9781424418367
DOI	10.1109/ICDE.2008.4497448
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-07
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filtering Matched filters Adaptive filters Degradation Statistics Information processing Adaptive systems Data mining Stress Costs
Content Type	Text
Resource Type	Article

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