Data Integration in Large Scale
Mobile Query Execution Model
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Mobile Query Execution Model: Query optimization in data integration systems over large scale network, faces the challenges of dealing with autonomous, heterogeneous and distributed data sources, dynamic execution environment and changing user requirements. These issues initiate the need for crafting the traditional optimization methods in a way to produce stable query execution plans, use execution models which are able to adapt to run-time conditions and handle source restrictions. Centralization of the control in adaptive optimization methods result in bottleneck due to large amounts of mes...
Mobile Query Execution Model: Query optimization in data integration systems over large scale network, faces the challenges of dealing with autonomous, heterogeneous and distributed data sources, dynamic execution environment and changing user requirements. These issues initiate the need for crafting the traditional optimization methods in a way to produce stable query execution plans, use execution models which are able to adapt to run-time conditions and handle source restrictions. Centralization of the control in adaptive optimization methods result in bottleneck due to large amounts of message passing towards the site of the central authority over large scale network where network bandwidth is low and network latency is high. Hence, adaptive query optimization requires decentralized methods. A mobile execution model with mobile relational operators that are able to adapt in an autonomous way and reduce transfer cost is worth considering in large scale distributed data integration environments. This book covers two propositions related with mobile execution model: 1) operators for restricted sources, 2) a placement strategy for mobile operators.
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