A major cost in retrieving multimedia data from multiple sites is the cost incurred in transferring multimedia data objects (MDO's) from different sites to the site where the query is initiated. The objective of a...
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A major cost in retrieving multimedia data from multiple sites is the cost incurred in transferring multimedia data objects (MDO's) from different sites to the site where the query is initiated. The objective of a data allocation algorithm is to locate the MDO's at different sites so as to minimize the total data transfer cost incurred in executing a given set of queries, There is a mutual dependency between data allocation and query execution strategies in that the optimal allocation of MDO's depends on the query execution strategy employed by a distributed multimedia system while the query execution strategy optimizes a query based on this allocation, In this paper, we flu the query execution strategy and develop a site-independent MDO dependency graph representation to model the dependencies among the MDO's accessed by a query, Given the MDO dependency graphs as well as the set of multimedia database sites, data transfer costs between the sites, the allocation limit on the number of MDO's that can be allocated at a site, and the query execution frequencies from the sites, an allocation scheme is generated, We formulate the data allocation problem as an optimization problem, We solve this problem with a number of techniques that broadly belong to three classes: max-flow min-cut, state-space search, and graph partitioning heuristics. The max-flow min-cut technique formulates the data allocation problem as a network-flow problem, and uses a hill-climbing approach to try to find the optimal solution, For the state-space search approach, the problem is solved using a best-first search algorithm, The graph partitioning approach uses two clustering heuristics, the agglomerative clustering and divisive clustering, We evaluate and compare these approaches, and assess their cost-performance trade-offs, All algorithms are also compared with optimal solutions obtained through exhaustive search, Conclusions are also made on the suitability of these approaches to different sce
Monte-Carlo methods are the basis for solving many computational problems using repeated random sampling in scenarios that may have a deterministic but very complex solution from a computational point of view. In rece...
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Monte-Carlo methods are the basis for solving many computational problems using repeated random sampling in scenarios that may have a deterministic but very complex solution from a computational point of view. In recent years, researchers are using the same idea to solve many problems through the so-called Monte-Carlo Tree search family of algorithms, which provide the possibility of storing and reusing previously calculated results to improve precision in the calculation of future outcomes. However, developers and researchers working in this area tend to have to carry out software developments from scratch to use their designs or improve designs previously created by other researchers. This makes it difficult to see improvements in current algorithms as it takes a lot of hard work. This work presents JGraphs, a toolset implemented in the Java programming language that will allow researchers to avoid having to reinvent the wheel when working with Monte-Carlo Tree search. In addition, it will allow testing experiments carried out by others in a simple way, reusing previous knowledge.
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