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Scalability and Fuzzy Systems: What Parallelization Can Do

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Flexible Approaches in Data, Information and Knowledge Management

Part of the book series: Studies in Computational Intelligence ((SCI,volume 497))

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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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Notes

  1. 1.

    According to OXFORD DICTIONARY. Fuzziness is deterministic uncertainty Fuzziness is concerned with the degree to which events occur rather than the likelihood of their occurrence (probability).

  2. 2.

    Detailed results are available on-line at http://www.lirmm.fr/~laurent/.

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Acknowledgments

This work was realized with the support of HPC@LR, a Center of Competence in High-Performance Computing from the Languedoc-Roussillon region, funded by the Languedoc-Roussillon region, the Europe and the Universit Montpellier 2 Sciences et Techniques. The HPC@LR Center is equipped with an IBM hybrid Supercomputer.

Author information

Authors and Affiliations

  1. LIRMM, University Montpellier 2 CNRS, 161 rue Ada 34095, Montpellier, France

    Malaquias Q. Flores & Anne Laurent

  2. Instituto Tecnológico de Apizaco, Tlaxcala, Mexico

    Malaquias Q. Flores

  3. Instituto Tecnológico de Toluca, DGEST-SEP Av. S/N, Metepec, Edo de Mexico, Mexico

    Federico Del Razo

  4. AllianSTIC-EFREI, Paris, 30-32 av. de la République, 94800, Villejuif Cedex, France

    Nicolas Sicard

Authors
  1. Malaquias Q. Flores
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  2. Federico Del Razo
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  3. Anne Laurent
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  4. Nicolas Sicard
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Corresponding author

Correspondence to Malaquias Q. Flores .

Editor information

Editors and Affiliations

  1. IRISA/ENSSAT, Université de Rennes 1, Lannion Cedex, France

    Olivier Pivert

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Sławomir Zadrożny

Appendix 1: Results of Parallel Gradual Pattern Mining

Appendix 1: Results of Parallel Gradual Pattern Mining

Graphic representation of the Speedup obtained in the experimental work with the parallel fuzzyGPM algorithm (Figs. 10, 11, 12, 13, 14, 15).

Fig. 10
figure 10

Threads versus elapsed time with a database of \(500\times 50\) and \(\mathrm{minSupp} =.30\) and .35, using uncompressed binary matrices of concordance degrees

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Fig. 11
figure 11

Speedup with a database of \(500\times 50\) and minSupp = .30 and .35, using uncompressed binary matrices of concordance degrees

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Fig. 12
figure 12

Threads versus elapsed time with a database of \(500\times 100\) and minSupp = .375 and .38, using uncompressed binary matrices of concordance degrees

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Fig. 13
figure 13

Speedup with a database of \(500\times 100\) and minSupp = .375 and .38, using uncompressed binary matrices of concordance degrees

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Fig. 14
figure 14

Threads versus elapsed time with a database of \(500\times 100\) and minSupp = .375 and .38, using compressed matrices of concordance degrees

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Fig. 15
figure 15

Speedup with a database of \(500\times 100\) and minSupp = .375 and .38, using compressed matrices of concordance degrees

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Q. Flores, M., Del Razo, F., Laurent , A., Sicard, N. (2014). Scalability and Fuzzy Systems: What Parallelization Can Do. In: Pivert, O., Zadrożny, S. (eds) Flexible Approaches in Data, Information and Knowledge Management. Studies in Computational Intelligence, vol 497. Springer, Cham. https://doi.org/10.1007/978-3-319-00954-4_13

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