A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data
Joint Authors
Xia, Dawen
Li, Yantao
Lu, Xiaonan
Li, Huaqing
Wang, Wendong
Zhang, Zili
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-28
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Xia, Dawen& Lu, Xiaonan& Li, Huaqing& Wang, Wendong& Li, Yantao& Zhang, Zili. 2018. A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data. Complexity،Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1133379
Modern Language Association (MLA)
Xia, Dawen…[et al.]. A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data. Complexity No. 2018 (2018), pp.1-16.
https://search.emarefa.net/detail/BIM-1133379
American Medical Association (AMA)
Xia, Dawen& Lu, Xiaonan& Li, Huaqing& Wang, Wendong& Li, Yantao& Zhang, Zili. A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data. Complexity. 2018. Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1133379
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1133379