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Running Multi-relational Data Mining Processes in the Cloud: A Practical Approach for Social Networks

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High Performance Computing (CARLA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 565))

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Abstract

Multi-relational Data Mining algorithms (MRDM) are the appropriate approach for inferring knowledge from databases containing multiple relationships between non-homogenous entities, which are precisely the case of datasets obtained from social networks. However, to acquire such expressivity, the search space of candidate hypotheses in MRDM algorithms is more complex than those obtained from traditional data mining algorithms. To allow a feasible search space of hypotheses, MRDM algorithms adopt several language biases during the mining process. Because of that, when running a MRDM-based system, the user needs to execute the same set of data mining tasks a number of times, each assuming a different combination of parameters in order to get a final good hypothesis. This makes manual control of such complex process tedious, laborious and error-prone. In addition, running the same MRDM process several times consumes much time. Thus, the automatic execution of each setting of parameters throughout parallelization techniques becomes essential. In this paper, we propose an approach named LPFlow4SN that models a MRDM process as a scientific workflow and then executes it in parallel in the cloud, thus benefiting from the existing Scientific Workflow Management Systems. Experimental results reinforce the potential of running parallel scientific workflows in the cloud to automatically control the MRDM process while improving its overall execution performance.

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Notes

  1. 1.

    http://aws.amazon.com/.

  2. 2.

    Download SciCumulus at: https://scicumulusc2.wordpress.com/.

  3. 3.

    http://web.comlab.ox.ac.uk/oucl/research/areas/machlearn/Aleph/aleph.html.

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Acknowledgments

The authors would like to thank FAPERJ (grant E-26/111.370/2013) and CNPq (grant 478878/2013-3) for partially sponsoring this research.

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Authors and Affiliations

  1. Instituto de Computação, Universidade Federal Fluminense – IC/UFF, Niterói, Brazil

    Aline Paes & Daniel de Oliveira

Authors
  1. Aline Paes
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  2. Daniel de Oliveira
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Corresponding author

Correspondence to Daniel de Oliveira .

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Editors and Affiliations

  1. LNCC, National Laboratory for Scientific Computing, Rio de Janeiro, Brazil

    Carla Osthoff

  2. Instituto de Informática, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

    Philippe Olivier Alexandre Navaux

  3. High Performance and Scientific Computing Center, Universidad Industrial de Santander, Bucaramanga, Chile

    Carlos Jaime Barrios Hernandez

  4. LNCC, National Laboratory for Scientific Computing, Rio de Janeiro, Brazil

    Pedro L. Silva Dias

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Paes, A., de Oliveira, D. (2015). Running Multi-relational Data Mining Processes in the Cloud: A Practical Approach for Social Networks. In: Osthoff, C., Navaux, P., Barrios Hernandez, C., Silva Dias, P. (eds) High Performance Computing. CARLA 2015. Communications in Computer and Information Science, vol 565. Springer, Cham. https://doi.org/10.1007/978-3-319-26928-3_1

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  • DOI: https://doi.org/10.1007/978-3-319-26928-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26927-6

  • Online ISBN: 978-3-319-26928-3

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