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Distributed and scalable sequential pattern mining through stream processing

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Abstract

Scalability is a primary issue in existing sequential pattern mining algorithms for dealing with a large amount of data. Previous work, namely sequential pattern mining on the cloud (SPAMC), has already addressed the scalability problem. It supports the MapReduce cloud computing architecture for mining frequent sequential patterns on large datasets. However, this existing algorithm does not address the iterative mining problem, which is the problem that reloading data incur additional costs. Furthermore, it did not study the load balancing problem. To remedy these problems, we devised a powerful sequential pattern mining algorithm, the sequential pattern mining in the cloud-uniform distributed lexical sequence tree algorithm (SPAMC-UDLT), exploiting MapReduce and streaming processes. SPAMC-UDLT dramatically improves overall performance without launching multiple MapReduce rounds and provides perfect load balancing across machines in the cloud. The results show that SPAMC-UDLT can significantly reduce execution time, achieves extremely high scalability, and provides much better load balancing than existing algorithms in the cloud.

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Notes

  1. OpenMP, http://www.openmp.org/.

  2. MPI, http://www.open-mpi.org/.

  3. If the bitmap vector is extremely sparse, the word-aligned hybrid code (WAH) [44] can serve for our goal. Specifically, WAH is a run-length encoding for compressing input data to words, where ANDs can be efficiently performed on any two words, and thus the bitmap representations can still work in this situation.

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Authors and Affiliations

  1. Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan

    Chun-Chieh Chen

  2. Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan

    Chun-Chieh Chen & Ming-Syan Chen

  3. Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan

    Hong-Han Shuai

  4. Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan

    Ming-Syan Chen

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  1. Chun-Chieh Chen
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  2. Hong-Han Shuai
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Correspondence to Chun-Chieh Chen.

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Chen, CC., Shuai, HH. & Chen, MS. Distributed and scalable sequential pattern mining through stream processing. Knowl Inf Syst 53, 365–390 (2017). https://doi.org/10.1007/s10115-017-1037-1

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