Scalable Scientific Stream Query Processing

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Ivanova, Milena,1967-Uppsala universitet,Avdelningen för datalogi,Datalogi (författare)

Scalable Scientific Stream Query Processing

BokEngelska2005

Förlag, utgivningsår, omfång ...

Uppsala :Acta Universitatis Upsaliensis,2005
137 s.
printrdacarrier

Nummerbeteckningar

LIBRIS-ID:oai:DiVA.org:uu-6024
ISBN:9155463517
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6024URI

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Språk:engelska
Sammanfattning på:engelska

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Ämneskategori:vet swepub-contenttype
Ämneskategori:dok swepub-publicationtype

Serie

Uppsala Dissertations from the Faculty of Science and Technology,1104-2516 ;66

Anmärkningar

Scientific applications require processing of high-volume on-line streams of numerical data from instruments and simulations. In order to extract information and detect interesting patterns in these streams scientists need to perform on-line analyses including advanced and often expensive numerical computations. We present an extensible data stream management system, GSDM (Grid Stream Data Manager) that supports scalable and flexible continuous queries (CQs) on such streams. Application dependent streams and query functions are defined through an object-relational model. Distributed execution plans for continuous queries are specified as high-level data flow distribution templates. A built-in template library provides several common distribution patterns from which complex distribution patterns are constructed. Using a generic template we define two customizable partitioning strategies for scalable parallel execution of expensive stream queries: window split and window distribute. Window split provides parallel execution of expensive query functions by reducing the size of stream data units using application dependent functions as parameters. By contrast, window distribute provides customized distribution of entire data units without reducing their size. We evaluate these strategies for a typical high volume scientific stream application and show that window split is favorable when expensive queries are executed on limited resources, while window distribution is better otherwise. Profile-based optimization automatically generates optimized plans for a class of expensive query functions. We further investigate requirements for GSDM in Grid environments. GSDM is a fully functional system for parallel processing of continuous stream queries. GSDM includes components such as a continuous query engine based on a data-driven data flow paradigm, a compiler of CQ specifications into distributed execution plans, stream interfaces and communication primitives. Our experiments with real scientific streams on a shared-nothing architecture show the importance of both efficient processing and communication for efficient and scalable distributed stream processing.

Ämnesord och genrebeteckningar

NATURVETENSKAP Data- och informationsvetenskap Datavetenskap hsv//swe
NATURAL SCIENCES Computer and Information Sciences Computer Sciences hsv//eng
data stream management systems
parallel stream processing
scientific stream query processing
user-defined stream partitioning
Computer science
Datalogi
Data- och systemvetenskap
Computer Systems Sciences

Biuppslag (personer, institutioner, konferenser, titlar ...)

Risch, Tore,ProfessorUppsala universitet,Avdelningen för datalogi(Swepub:uu)torerisc (preses)
Rundensteiner, Elke A.,ProfessorComputer Science Department, Worcester Polytechnic Institute, Worcester, MA (opponent)
Uppsala universitetAvdelningen för datalogi (creator_code:org_t)

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https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6024

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Av författaren/redakt...: Ivanova, Milena, ...; Risch, Tore, Pro ...; Rundensteiner, E ...

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Av lärosätet: Uppsala universitet

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