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- Ivanova, Milena,1967-Uppsala universitet,Avdelningen för datalogi,Datalogi (author)
Scalable Scientific Stream Query Processing
- BookEnglish2005
Publisher, publication year, extent ...
- Uppsala :Acta Universitatis Upsaliensis,2005
- 137 s.
- printrdacarrier
Numbers
- LIBRIS-ID:oai:DiVA.org:uu-6024
- ISBN:9155463517
- https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6024URI
Supplementary language notes
- Language:English
- Summary in:English
Part of subdatabase
- SwePubSwePub
Classification
Series
- Uppsala Dissertations from the Faculty of Science and Technology,1104-2516 ;66
Notes
- 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.
Subject headings and genre
- 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
Added entries (persons, corporate bodies, meetings, titles ...)
- Risch, Tore,ProfessorUppsala universitet,Avdelningen för datalogi(Swepub:uu)torerisc (thesis advisor)
- Rundensteiner, Elke A.,ProfessorComputer Science Department, Worcester Polytechnic Institute, Worcester, MA (opponent)
- Uppsala universitetAvdelningen för datalogi (creator_code:org_t)
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