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Modeling and implementing distributed data mining strategies in JaCa-DDM

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Abstract

This work introduces JaCa-DDM, a novel distributed data mining system founded on the agents and artifacts paradigm, conceived to design, implement, deploy, and evaluate learning strategies. Jason rational agents conform to such strategies to cope with distributed computing environments, where CArtAgO artifacts encapsulate learning algorithms, data sources, evaluation tools, and other services implemented in Weka for data mining tasks. The set of strategies presented in this paper aims at encouraging the use of JaCa-DDM to develop new ones, suited to different needs. For this, our system provides tools to evaluate the resulting models in terms of accuracy, number of instances employed to learn, time of convergence, and volume of communications. Although the emphasis in decision trees, JaCa-DDM can be easily extended by adopting new artifacts, e.g., for meta-learning. The main contributions of the paper are as follows: (i) From the multi-agent systems perspective, our approach illustrates how to exploit the so-called “agentification” of Weka for the sake of code reusability, while preserving the benefits of reasoning at the Belief–Desire–Intention level with Jason; (ii) from the data mining perspective, JaCa-DDM is promoted as an extensible tool to define and test distributed strategies; and (iii) a set of strategies including centralizing, meta-learning and Windowing-based approaches, is carefully analyzed to provide comparisons among them.

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Notes

  1. http://jacaddm.sourceforge.net/.

  2. A tutorial is available at https://sourceforge.net/p/jacaddm/wiki.

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Funding

The first author was funded by Conacyt Scholarship 362384. This work has been partly supported by the Spanish Ministry of Science and Innovation through Project TIN2015-66972-C5-5-R.

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Authors and Affiliations

  1. Universidad Veracruzana, Centro de Investigación en Inteligencia Artificial, Sebastián Camacho No 5, Xalapa, Ver., México, 91000, Mexico

    Xavier Limón, Alejandro Guerra-Hernández & Nicandro Cruz-Ramírez

  2. Departament d’Informàtica, Universitat de València, Avinguda de la Universitat, s/n, Burjassot, València, 46100, Spain

    Francisco Grimaldo

Authors
  1. Xavier Limón
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  2. Alejandro Guerra-Hernández
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  3. Nicandro Cruz-Ramírez
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  4. Francisco Grimaldo
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Corresponding author

Correspondence to Alejandro Guerra-Hernández.

Appendix

Appendix

Table 5 Experimental results
Full size table

Table 5 shows the results for all the datasets. The column DS refers to the indexes for the datasets in Table 3. Displayed values are the average of 20 runs (two repetitions of a tenfold stratified cross-validation). Time is measured in seconds and traffic in megabytes. Remember that the strategies round, round counter, and parallel round counter use the VFDT learning algorithm.

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Limón, X., Guerra-Hernández, A., Cruz-Ramírez, N. et al. Modeling and implementing distributed data mining strategies in JaCa-DDM. Knowl Inf Syst 60, 99–143 (2019). https://doi.org/10.1007/s10115-018-1222-x

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