
Approximation of Classification and Measures of Uncertainty in Rough Set on Two Universal Sets
The notion of rough set captures indiscernibility of elements in a set. ...
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Classbased Rough Approximation with Dominance Principle
Dominancebased Rough Set Approach (DRSA), as the extension of Pawlak's ...
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Dialectics of Knowledge Representation in a Granular Rough Set Theory
The concepts of rough and definite objects are relatively more determina...
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Auctionbased approximate algorithm for Grid system scheduling under resource provider strategies
In this paper a new mathematical model is proposed for task scheduling a...
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Prunnig Algorithm of Generation a Minimal Set of Rule Reducts Based on Rough Set Theory
In this paper it is considered rule reduct generation problem, based on ...
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Determining the Consistency factor of Autopilot using Rough Set Theory
Autopilot is a system designed to guide a vehicle without aid. Due to in...
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Improving Grid Computing Performance by Optimally Reducing Checkpointing Effect
Grid computing is a collection of computer resources that are gathered t...
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Resource Matchmaking Algorithm using Dynamic Rough Set in Grid Environment
Grid environment is a service oriented infrastructure in which many heterogeneous resources participate to provide the high performance computation. One of the bug issues in the grid environment is the vagueness and uncertainty between advertised resources and requested resources. Furthermore, in an environment such as grid dynamicity is considered as a crucial issue which must be dealt with. Classical rough set have been used to deal with the uncertainty and vagueness. But it can just be used on the static systems and can not support dynamicity in a system. In this work we propose a solution, called Dynamic Rough Set Resource Discovery (DRSRD), for dealing with cases of vagueness and uncertainty problems based on Dynamic rough set theory which considers dynamic features in this environment. In this way, requested resource properties have a weight as priority according to which resource matchmaking and ranking process is done. We also report the result of the solution obtained from the simulation in GridSim simulator. The comparison has been made between DRSRD, classical rough set theory based algorithm, and UDDI and OWL S combined algorithm. DRSRD shows much better precision for the cases with vagueness and uncertainty in a dynamic system such as the grid rather than the classical rough set theory based algorithm, and UDDI and OWL S combined algorithm.
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