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Scalable Scientific Stream Query Processing (Uppsala Dissertations from the Faculty of Science and Technology, 66)

Milena Ivanova
4.9/5 (16468 ratings)
Description:This is a Ph.D. dissertation. 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 System 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 CQs 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 have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Scalable Scientific Stream Query Processing (Uppsala Dissertations from the Faculty of Science and Technology, 66). To get started finding Scalable Scientific Stream Query Processing (Uppsala Dissertations from the Faculty of Science and Technology, 66), you are right to find our website which has a comprehensive collection of manuals listed.
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9155463517

Scalable Scientific Stream Query Processing (Uppsala Dissertations from the Faculty of Science and Technology, 66)

Milena Ivanova
4.4/5 (1290744 ratings)
Description: This is a Ph.D. dissertation. 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 System 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 CQs 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 have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Scalable Scientific Stream Query Processing (Uppsala Dissertations from the Faculty of Science and Technology, 66). To get started finding Scalable Scientific Stream Query Processing (Uppsala Dissertations from the Faculty of Science and Technology, 66), you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.
Pages
Format
PDF, EPUB & Kindle Edition
Publisher
Release
ISBN
9155463517
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