Download Stream Data Processing: A Quality of Service Perspective: by Sharma Chakravarthy PDF

By Sharma Chakravarthy

Traditional database administration structures, customary this day, aren't well-suited for a category of rising purposes. those purposes, comparable to community administration, sensor computing, and so forth, have to continually procedure quite a lot of info coming within the type of a flow and also, meet stringent reaction time specifications. help for dealing with QoS metrics, equivalent to reaction time, reminiscence utilization, and throughput, is valuable to any process proposed for the above applications.

Stream information Processing: a high quality of carrier standpoint (Modeling,Scheduling, Load laying off, and complicated occasion Processing), provides a brand new paradigm appropriate for circulate and complicated occasion processing. This e-book covers a wide variety of issues in movement info processing and contains exact technical discussions of a couple of proposed ideas from QoS perspective.

This quantity is meant as a textual content ebook for graduate classes and as a reference e-book for researchers, advanced-level scholars in computing device sciences, and IT practitioners.

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Extra info for Stream Data Processing: A Quality of Service Perspective: Modeling, Scheduling, Load Shedding, and Complex Event Processing

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However, the MOS strategy schedules an operator segment each time, instead of an operator as in the Chain strategy. As a result, the MOS strategy achieves the strictly optimal memory requirement as compared with the near-optimal memory requirement of the Chain strategy. The MOS strategy also achieves better tuple latency and smoother throughput than the Chain strategy. The Simplified Segment (SS) strategy is a variant of the MOS strategy as it partitions an operator path into at most two segments.

Hancock [154] is a domain-specific language created to express computationally efficient signature programs for mining very large amounts of stream data in the telecommunication domain. Tapestry [155, 156] is an experimental content-based filtering system over email and message streams developed at the Xerox Palo Alto Research Center. Tapestry translates filters into continuous queries and processes these continuous queries over an append-only database. An Expressive Stream Language (ESL) for stream processing has been proposed in [157].

This means a scheduling strategy has significant impact on various performance aspects of a DSMS and hence a DSMS can leverage different strategies to maximize the available physical resources to handle the bursty nature of data streams. Consider two operators A and B in the system; operator A needs 1 second to process 1 tuple with a size of 2 bytes and outputs 1 tuple with a size of 1 byte (processing rate is 1 tuple/second, and memory release rate is 1 byte/second). 5 byte/second). If input streams of both operator A and B have an input rate of 1 tuple per second for 5 consecutive seconds and then a pause, a scheduling strategy which schedules A first if there is any tuple waiting at A, then schedules B, requires a maximal memory of 10 bytes (or 5 tuples) that are waiting at B right after the bursty input period.

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