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IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK IN CONCURRENCY CONTROL OF COMPUTER INTEGRATED MANUFACTURING(CIM) DATABASE

IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK IN CONCURRENCY CONTROL OF COMPUTER INTEGRATED MANUFACTURING(CIM) DATABASE,P. Raviram,R. S. D. Wahidabanu

IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK IN CONCURRENCY CONTROL OF COMPUTER INTEGRATED MANUFACTURING(CIM) DATABASE   (Citations: 2)
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Manufacturing database store large amount of interrelated data. The designers access specific information or group of information in the data. Each designer accessing an entity tries to modify the design parameters meeting the requirements of different customers. Sister concerns of the same group of company will be modifying the data as per design requirements. When information is updated with new modification by different group of designers, what is the order in which modification of the data has to be allowed. If simultaneous access of the information is done, how to maintain the consistency of the data. and a designer voluntarily corrupts the data, how to make sure the designer is responsible for the corruption of data. In any case if the transaction process corrupts the data, how to maintain the consistency of the data. Deleting the information wantedly can be identified with extra security for the data. However, when transaction protocol is not implemented properly, then corruption of data in the form of misleading information that showing less numerical value than what it has to be or showing more numerical than before updation. In this research work, we have proposed a neural network method for the managing the locks assigned to objects and the corresponding transactions are stored in a data structure. The main purpose of using the ANN is that it will require less memory in storing the lock information assigned to objects. We have attempted to use backpropagation algorithm for storing lock information when multi users are working on computer integrated manufacturing (CIM) database.
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    • ...Although the previous studies have shown that the ANNs can give good results with web caching, the ANNs have the following disadvantage: ANNs lack explanatory capabilities, the performance of ANNs relies on the optimal selection of the network topology and its parameters, ANNs learning process can be time consuming, and ANNs are also too dependent on the quality and amount of data available [14, 15, 16]...

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