Abstract
In this paper we address the problem of allocation scheme design of large database-objects in the Web environment, which may suffer significant changes in usage and access patterns and scaling of data. In these circumstances, if the design is not adjusted to new changes, the system can undergo severe degradations in data access costs and response time. Since this problem is NP-complete, obtaining optimal solutions for large problem instances requires applying approximate methods. We present a mathematical model to generate a new object allocation scheme and propose a new method to solve it. The method uses a Hopfield neural network with the mean field annealing (MFA) variant. The experimental results and a comparative study with other two methods are presented. The new method has a similar capacity to solve large problem instances, regular level of solution quality and excellent execution time with respect to other methods.
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Pérez O., J., Pazos R., R.A., Fraire H., H.J., Cruz R., L., Pecero S., J.E. (2003). Adaptive Allocation of Data-Objects in the Web Using Neural Networks. In: Cappelli, A., Turini, F. (eds) AI*IA 2003: Advances in Artificial Intelligence. AI*IA 2003. Lecture Notes in Computer Science(), vol 2829. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39853-0_13
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DOI: https://doi.org/10.1007/978-3-540-39853-0_13
Publisher Name: Springer, Berlin, Heidelberg
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