| Available via | http://dbpubs.stanford.edu/pub/2005-24 |
|
Submitted on |
15th of September 2005 |
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Author |
Babu, Shivnath |
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Title |
Adaptive Query Processing in Data Stream Management Systems |
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Date of publication |
2005 |
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Citation |
Babu, Shivnath. Adaptive Query Processing in Data Stream Management Systems |
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Number of pages |
235 |
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Language |
English |
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Project |
STREAM |
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Type |
Ph.D. thesis |
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Subject group |
Data Streams |
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Abstract |
This thesis addresses the problem of processing continuous queries in a Data Stream Management System (DSMS) when
stream characteristics and system conditions may vary unpredictably over time. We present
a generic framework, called StreaMon, for adaptive query processing in a DSMS. StreaMon has three core components:
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An Executor, which runs the current plan for each query
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A Profiler, which collects and maintains statistics about current stream characteristics and system conditions
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A Re-optimizer, which ensures the current plans are the
most efficient for current stream characteristics and system conditions
We instantiate the generic StreaMon framework for three distinct combinations of continuous query type and
adaptivity need:
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Adaptive processing of commutative filters over a stream to maximize throughput at
all points in time
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Adaptive placement of subresult caches in pipelined plans for windowed stream joins
to maximize throughput at all points in time
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Detecting relaxed constraints automatically in input streams and exploiting these
constraints to reduce memory requirements in plans for windowed stream joins
For each problem, we provide the definition and motivating examples, develop and analyze
adaptive algorithms, and present implementation techniques and experimental results from
the STREAM general-purpose DSMS prototype developed at Stanford. |
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Keywords |
Adaptive query processing, data streams, continuous queries |
| Fulltext source |
PDF (pdf, pdf.gz, pdf.zip)
| Management of the document by | rwesley@stanford.edu
| |