Scalability and Fuzzy Systems: What Parallelization Can Do

Abstract : (Fuzzy) Database management systems aim to provide tools for data storage and ing. Based on the stored information, systems can offer analytical functionalities in order to deliver decisional database environments. In many application areas, fuzzy systems have proven to be efficient for modeling, reasoning, and predicting with imprecise information. However, expanding the frontiers of such areas or exploring new domains is often limited when facing real world data: as the space to search get bigger, more computation time and memory space are required. In this chapter, we discuss how the parallelization of fuzzy algorithms is crucial to tackle the problem of scalability and optimal performance in the context of fuzzy database mining. More precisely, we present the parallelization of fuzzy database mining algorithms on multi-core architectures of two knowledge discovery paradigms, namely fuzzy gradual pattern mining and fuzzy tree mining (for example in the case of XML databases). We also present a review of other two related problems, namely fuzzy association rule mining and fuzzy clustering.
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Perfecto Malaquias Quintero Flores, Anne Laurent, Federico del Razo Lopez, Nicolas Sicard, Pascal Poncelet. Scalability and Fuzzy Systems: What Parallelization Can Do. Flexible Approaches in Data, Information and Knowledge Management, 497, Springer, pp.291-320, 2014, Studies in Computational Intelligence, 978-3-319-00953-7. ⟨10.1007/978-3-319-00954-4_13⟩. ⟨lirmm-01381090⟩

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