Abstract
In OLAP, the materialization of multidimensional structures is a sine qua non condition of performance. Problems that come along with this need have triggered a huge variety of proposals: the picking of the optimal set of aggregation combinations, to materialize into centralized OLAP repositories, emerges among them. This selection is based on general purpose combinatorial optimization algorithms, such as greedy, evolutionary, swarm and randomizing approaches. Only recently, the distributed approach has come to stage, introducing another source of complexity: space. Now, it’s not enough to select the appropriate data structures, but also to know where to locate them. To solve this extended problem, optimizing heuristics are faced with extra complexity, hardening its search for solutions. This paper presents a polymorphic algorithm, coined as metamorphosis algorithm that combines genetic, particle swarm and hill climbing metaheuristics. It is used to solve the extended cube selection and allocation problem generated in M-OLAP architectures.
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Loureiro, J., Belo, O. (2007). A Metamorphosis Algorithm for the Optimization of a Multi-node OLAP System. In: Neves, J., Santos, M.F., Machado, J.M. (eds) Progress in Artificial Intelligence. EPIA 2007. Lecture Notes in Computer Science(), vol 4874. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77002-2_32
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DOI: https://doi.org/10.1007/978-3-540-77002-2_32
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