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While existing parallel algorithms have been successfully applied to frequent pattern mining of large\u2010scale 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\u2010based Parallel Frequent Pattern growth (MR\u2010PFP) algorithm to analyze the spatiotemporal characteristics of taxi operating using large\u2010scale 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\u2010growth) algorithm and then implement the optimized FP\u2010growth algorithm on a MapReduce framework. Finally, we analyze the characteristics of taxi operating in both spatial and temporal dimensions by MR\u2010PFP in parallel. The results demonstrate that MR\u2010PFP is superior to existing Parallel FP\u2010growth (PFP) algorithm in efficiency and scalability.<\/jats:p>","DOI":"10.1155\/2018\/2818251","type":"journal-article","created":{"date-parts":[[2018,1,28]],"date-time":"2018-01-28T23:31:03Z","timestamp":1517182263000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A MapReduce\u2010Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-0151-9643","authenticated-orcid":false,"given":"Dawen","family":"Xia","sequence":"first","affiliation":[]},{"given":"Xiaonan","family":"Lu","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6310-8965","authenticated-orcid":false,"given":"Huaqing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Wendong","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7648-5671","authenticated-orcid":false,"given":"Yantao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zili","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2018,1,28]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1038\/498255a"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.109"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2011.2158001"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.08.006"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.10.121"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2014.2325029"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/cplx.21756"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1002\/cplx.21728"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/2743025"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/2629592"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/mcom.2013.6525604"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.12.013"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2016.2570021"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/793010"},{"key":"e_1_2_9_15_2","unstructured":"AgrawalR.andSrikantR. 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