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A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data

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  • Dawen Xia
  • Xiaonan Lu
  • Huaqing Li
  • Wendong Wang
  • Yantao Li
  • Zili Zhang

Abstract

Frequent pattern mining is an effective approach for spatiotemporal association analysis of mobile trajectory big data in data-driven intelligent transportation systems. While existing parallel algorithms have been successfully applied to frequent pattern mining of large-scale trajectory data, two major challenges are how to overcome the inherent defects of Hadoop to cope with taxi trajectory big data including massive small files and how to discover the implicitly spatiotemporal frequent patterns with MapReduce. To conquer these challenges, this paper presents a MapReduce-based Parallel Frequent Pattern growth (MR-PFP) algorithm to analyze the spatiotemporal characteristics of taxi operating using large-scale taxi trajectories with massive small file processing strategies on a Hadoop platform. More specifically, we first implement three methods, that is, Hadoop Archives (HAR), CombineFileInputFormat (CFIF), and Sequence Files (SF), to overcome the existing defects of Hadoop and then propose two strategies based on their performance evaluations. Next, we incorporate SF into Frequent Pattern growth (FP-growth) algorithm and then implement the optimized FP-growth algorithm on a MapReduce framework. Finally, we analyze the characteristics of taxi operating in both spatial and temporal dimensions by MR-PFP in parallel. The results demonstrate that MR-PFP is superior to existing Parallel FP-growth (PFP) algorithm in efficiency and scalability.

Suggested Citation

  • Dawen Xia & Xiaonan Lu & Huaqing Li & Wendong Wang & Yantao Li & Zili Zhang, 2018. "A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data," Complexity, Hindawi, vol. 2018, pages 1-16, January.
    • Handle: RePEc:hin:complx:2818251
    DOI: 10.1155/2018/2818251
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    References listed on IDEAS

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    1. Robert Ross & Philip Carns & David Metheny, 2009. "Parallel File Systems," International Series in Operations Research & Management Science, in: Yupo Chan & John Talburt & Terry M. Talley (ed.), Data Engineering, chapter 8, pages 143-168, Springer.
    2. Vivien Marx, 2013. "The big challenges of big data," Nature, Nature, vol. 498(7453), pages 255-260, June.
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    1. Maryam Barzegar & Abolghasem Sadeghi-Niaraki & Maryam Shakeri & Soo-Mi Choi, 2019. "An Improved Route-Finding Algorithm Using Ubiquitous Ontology-Based Experiences Modeling," Complexity, Hindawi, vol. 2019, pages 1-15, November.

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