{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T15:19:10Z","timestamp":1776698350573,"version":"3.51.2"},"reference-count":29,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61661015, 61861013"],"award-info":[{"award-number":["61661015, 61861013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangxi Innovation-Driven Development Project","award":["AA17202024"],"award-info":[{"award-number":["AA17202024"]}]},{"DOI":"10.13039\/100009950","name":"Ministry of Education","doi-asserted-by":"publisher","award":["CRKL160101"],"award-info":[{"award-number":["CRKL160101"]}],"id":[{"id":"10.13039\/100009950","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangxi Key Laboratory","award":["GCIS201701"],"award-info":[{"award-number":["GCIS201701"]}]},{"name":"Guangxi Collaborative Innovation Center","award":["YD16801"],"award-info":[{"award-number":["YD16801"]}]},{"name":"Guangxi Collaborative Innovation Center","award":["C77KYS02SX18"],"award-info":[{"award-number":["C77KYS02SX18"]}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1016\/j.neucom.2018.09.107","type":"journal-article","created":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T20:37:23Z","timestamp":1564087043000},"page":"194-202","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"special_numbering":"C","title":["Mining moving object gathering pattern based on Resilient Distributed Datasets and R-tree index"],"prefix":"10.1016","volume":"393","author":[{"given":"Qian","family":"He","sequence":"first","affiliation":[]},{"given":"Yiting","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Qinghe","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Wang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.neucom.2018.09.107_bib0001","first-page":"959","article-title":"Trajectory big data: a review of key technologies in data processing","volume":"28","author":"Gao","year":"2017","journal-title":"J. Softw."},{"key":"10.1016\/j.neucom.2018.09.107_bib0002","series-title":"Proceedings of International Symposium on Spatial Databases","first-page":"251","article-title":"Algorithms for joining R-Trees and linear region quadtrees","volume":"1651","author":"Corral","year":"1999"},{"key":"10.1016\/j.neucom.2018.09.107_bib0003","unstructured":"V. Prasad, C. Adam, C. Everspaugh, et al. Indexing Large Trajectory Data Sets with SETI. 2003."},{"issue":"2","key":"10.1016\/j.neucom.2018.09.107_bib0004","first-page":"348","article-title":"Mining moving object gathering pattern method via spatio-temporal graph","volume":"27","author":"Zhang","year":"2016","journal-title":"J. Softw."},{"key":"10.1016\/j.neucom.2018.09.107_bib0005","series-title":"Proceedings of IEEE International Conference on Data Engineering.","first-page":"242","article-title":"On discovery of gathering patterns from trajectories","author":"Zheng","year":"2013"},{"issue":"2","key":"10.1016\/j.neucom.2018.09.107_bib0006","doi-asserted-by":"crossref","first-page":"23","DOI":"10.4018\/IJGHPC.2016040102","article-title":"Discovering gathering pattern using a taxicab service rate analysis method based on neural network","volume":"8","author":"Zhang","year":"2016","journal-title":"Int. J. Grid High Perform. Comput."},{"issue":"12","key":"10.1016\/j.neucom.2018.09.107_bib0007","first-page":"97","article-title":"Trajectory big data: data, applications, and techniques","volume":"36","author":"Xu","year":"2015","journal-title":"J. Commun."},{"key":"10.1016\/j.neucom.2018.09.107_bib0008","series-title":"Proceedings of IEEE International Conference on Trust, Security and Privacy in Computing and Communications","first-page":"644","article-title":"HBaseSpatial: a scalable spatial data storage based on HBase","author":"Zhang","year":"2015"},{"key":"10.1016\/j.neucom.2018.09.107_bib0009","series-title":"Proceedings of ACM International Conference on Conference on Information and Knowledge Management","first-page":"1409","article-title":"SharkDB: an in-memory column-oriented trajectory storage","author":"Wang","year":"2014"},{"issue":"7","key":"10.1016\/j.neucom.2018.09.107_bib0010","doi-asserted-by":"crossref","first-page":"12990","DOI":"10.3390\/s140712990","article-title":"A hybrid spatio-temporal data indexing method for trajectory databases","volume":"14","author":"Ke","year":"2014","journal-title":"Sensors"},{"key":"10.1016\/j.neucom.2018.09.107_bib0011","unstructured":"A. Boulmakoul, L. Karim, M.H. Laarabi, et al. MongoDB-Hadoop Distributed and Scalable Framework for Spatio-temporal Hazardous Materials Data Warehousing Ames, D.p. Quinn, N.w.t. Rizzoli, A.e. 2014."},{"key":"10.1016\/j.neucom.2018.09.107_bib0012","series-title":"Proceedings of International Conference on Knowledge Discovery and Data Mining","first-page":"226","article-title":"A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise","author":"Ester","year":"1996"},{"key":"10.1016\/j.neucom.2018.09.107_bib0013","unstructured":"http:\/\/spark.apache.org\/, 2018, 2018-02-18."},{"issue":"3","key":"10.1016\/j.neucom.2018.09.107_bib0014","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.comgeo.2007.10.003","article-title":"Reporting flock patterns","volume":"41","author":"Benkert","year":"2008","journal-title":"Comput. Geom."},{"key":"10.1016\/j.neucom.2018.09.107_bib0015","series-title":"Proceedings of the Second International Conference on Geographic Information Science, GIScience 2002, Boulder, CO, USA, September 25\u201328, 2002","first-page":"132","article-title":"Analyzing relative motion within groups of trackable moving point objects","author":"Laube","year":"2002"},{"key":"10.1016\/j.neucom.2018.09.107_bib0016","doi-asserted-by":"crossref","unstructured":"H. Jeung, H.T. Shen, X. Zhou, Convoy Queries in Spatio-temporal Databases. 2008:1457\u20131459.","DOI":"10.1109\/ICDE.2008.4497588"},{"issue":"1","key":"10.1016\/j.neucom.2018.09.107_bib0017","doi-asserted-by":"crossref","first-page":"723","DOI":"10.14778\/1920841.1920934","article-title":"Swarm: mining relaxed temporal moving object clusters","volume":"3","author":"Li","year":"2010","journal-title":"Proc. Vldb Endow."},{"issue":"3","key":"10.1016\/j.neucom.2018.09.107_bib0018","doi-asserted-by":"crossref","first-page":"465","DOI":"10.3724\/SP.J.1001.2013.04248","article-title":"Clustering trajectories of entities in computer wargames","volume":"24","author":"Shi","year":"2013","journal-title":"J. Softw."},{"issue":"1","key":"10.1016\/j.neucom.2018.09.107_bib0019","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/TPDS.2014.2308221","article-title":"Performance analysis and optimization for SpMV on GPU using probabilistic modeling","volume":"26","author":"Li","year":"2015","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"issue":"12","key":"10.1016\/j.neucom.2018.09.107_bib0020","first-page":"1765","article-title":"GFlink: an in-memory computing architecture on heterogeneous CPU-GPU clusters for big data","volume":"67","author":"Chen","year":"2018","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.neucom.2018.09.107_bib0021","series-title":"Proceedings of International Conference on Natural Computation","first-page":"769","article-title":"Parallel clustering of big data of spatio-temporal trajectory","author":"Hu","year":"2015"},{"key":"10.1016\/j.neucom.2018.09.107_bib0022","series-title":"Proceedings of International Conference on\u00a0Intelligent Data Engineering and Automated Learning, IDEAL","article-title":"MR-swarm: mining swarms from big spatio-temporal trajectories using mapreduce","author":"Yu","year":"2016"},{"key":"10.1016\/j.neucom.2018.09.107_bib0023","series-title":"Managing Big Data in Cloud Computing Environments","article-title":"Modeling and indexing spatiotemporal trajectory data in non-relational databases","author":"Aydin","year":"2016"},{"issue":"3","key":"10.1016\/j.neucom.2018.09.107_bib0024","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s10619-014-7156-8","article-title":"Efficient top-(k,l) range query processing for uncertain data based on multicore architectures","volume":"33","author":"Xiao","year":"2015","journal-title":"Distrib. Parallel Databases"},{"issue":"10","key":"10.1016\/j.neucom.2018.09.107_bib0025","doi-asserted-by":"crossref","first-page":"2808","DOI":"10.1109\/TKDE.2016.2584606","article-title":"Top $k$, favorite probabilistic products queries","volume":"28","author":"Zhou","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2018.09.107_bib0026","series-title":"Proceedings of IEEE International Conference on Data Engineering","first-page":"317","article-title":"Ranking with uncertain scores","author":"Soliman","year":"2009"},{"issue":"1","key":"10.1016\/j.neucom.2018.09.107_bib0027","doi-asserted-by":"crossref","first-page":"A34","DOI":"10.1007\/BF01999507","article-title":"Grundz\u00fcge der mengenlehre","volume":"26","author":"Hausdorff","year":"1915","journal-title":"Monatshefte Math. Phys."},{"key":"10.1016\/j.neucom.2018.09.107_bib0028","series-title":"Proceedings of International Conference on High Performance Computing & Simulation","first-page":"531","article-title":"DBSCAN on resilient distributed datasets","author":"Cordova","year":"2015"},{"key":"10.1016\/j.neucom.2018.09.107_bib0029","series-title":"Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD\u201911","article-title":"Driving with knowledge from the physical world","author":"Yuan","year":"2011"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231219310501?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231219310501?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T19:05:38Z","timestamp":1760382338000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231219310501"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6]]},"references-count":29,"alternative-id":["S0925231219310501"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2018.09.107","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2020,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Mining moving object gathering pattern based on Resilient Distributed Datasets and R-tree index","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2018.09.107","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}