Abstract
We suggest an approach to optimize data mining in modern applications that work on distributed data. We formally transform a high-level functional representation of a data-mining algorithm into a parallel implementation that performs as much as possible computations locally at the data sources, rather than accumulating all data for processing at a central location as in the traditional MapReduce approach. Our approach avoids the main disadvantages of the state-of-the-art MapReduce frameworks in the context of distributed data: increased run time, high network traffic, and an unauthorized access to data. We use the popular data-mining algorithm – Naive Bayes – for illustrating our approach and evaluating it experimentally. Our experiments confirm that the implementation of Naive Bayes developed by using our approach significantly outperforms the traditional MapReduce-based implementation regarding the run time and the network traffic.
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Acknowledgments
We thank the anonymous referees for very helpful remarks on the preliminary version of the paper. This work was supported by the Ministry of Education and Science of the Russian Federation in the framework of the state order “Organization of Scientific Research”, task #2.6113.2017/BУ, and by the German Ministry of Education and Research (BMBF) in the framework of project HPC2SE at the University of Muenster.
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Kholod, I., Shorov, A., Efimova, M., Gorlatch, S. (2019). Parallelization of Algorithms for Mining Data from Distributed Sources. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2019. Lecture Notes in Computer Science(), vol 11657. Springer, Cham. https://doi.org/10.1007/978-3-030-25636-4_23
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DOI: https://doi.org/10.1007/978-3-030-25636-4_23
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