{"id":168066,"date":"2015-09-01T00:00:00","date_gmt":"2015-09-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/trajectory-data-mining-an-overview\/"},"modified":"2018-10-16T21:08:33","modified_gmt":"2018-10-17T04:08:33","slug":"trajectory-data-mining-an-overview","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/trajectory-data-mining-an-overview\/","title":{"rendered":"Trajectory Data Mining: An Overview"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles and animals. Many techniques have been proposed for processing, managing and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into <em>trajectory data mining<\/em>, providing a panorama of the field as well as the scope of its research topics. Following a roadmap from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of <em>trajectory data mining<\/em>, providing a quick understanding of this field to the community.<\/p>\n<\/div>\n<p>&nbsp;<\/p>\n<p>A <strong>tutorial on Trajectory data mining\u00a0<\/strong>can be found at <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/project\/trajectory-data-mining\/\">here<\/a>.<\/p>\n<p>Slide decks for different parts of this survey paper.<\/p>\n<ol>\n<li>Trajectory Preprocessing (<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2011\/11\/01-Trajectory-Preprocessing.pptx\">Slides<\/a>)<\/li>\n<li>Trajectory Data Management\u00a0(<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2011\/11\/02-Trajectory-Data-Mining-Trajectory-data-management.pptx\">Slides<\/a>)<\/li>\n<li>Trajectory Uncertainty (<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2011\/11\/03-Trajectory-Data-Mining-Trajectory-Uncertainty.pptx\">Slides<\/a>)<\/li>\n<li>Trajectory Pattern Mining (<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2011\/11\/05-Trajectory-Pattern-Mining.ppt\">Slides<\/a>)<\/li>\n<li>Trajectory Classification (<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2011\/11\/05-Trajectory-Data-Mining-Trajectory-classification.pptx\">Slides<\/a>)<\/li>\n<li>Anomaly Detection in Trajectories\u00a0(<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2011\/11\/06-Trajectory-Data-Mining-Trajectory-Anomaly-detection.pptx\">Slides<\/a>)<\/li>\n<li>Turning\u00a0Trajectories into Graphs (<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/www.dropbox.com\/s\/7mvq4xmbakkuyfa\/07-Trajectory%20Data%20Mining-Graph%20for%20trajectories.pptx?dl=0\">Slides<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>)<\/li>\n<li>Turning Trajectories into Matrix (<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/www.dropbox.com\/s\/c4nopf3mjyxerjf\/08-Trajectory%20Data%20Mining-Matrix%20for%20trajectories.pptx?dl=0\">Slides<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>)<\/li>\n<li>Turning Trajectories into Tensors (<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/www.dropbox.com\/s\/ypiog7aegh7k1n6\/09-Trajectory%20Data%20Mining-Tensor%20for%20trajectories.pptx?dl=0\">Slides<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>)<\/li>\n<\/ol>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles and animals. Many techniques have been proposed for processing, managing and mining trajectory data in the past decade, fostering a broad range of applications. In this article, [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"yuzheng","user_id":"35088"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"ACM Transaction on Intelligent Systems and Technology","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"ACM Transaction on Intelligent Systems and Technology","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"\u00a9 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version can be found at http:\/\/dl.acm.org.","msr_conference_name":"","msr_doi":"10.1145\/2743025","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2015-09-01","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":2015,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13561,13556,13563],"msr-publication-type":[193715],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-168066","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-research-area-artificial-intelligence","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"","msr_edition":"ACM Transaction on Intelligent Systems and Technology","msr_affiliation":"","msr_published_date":"2015-09-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"ACM Transaction on Intelligent Systems and Technology","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"365009","msr_publicationurl":"","msr_doi":"10.1145\/2743025","msr_publication_uploader":[{"type":"file","title":"TrajectoryDataMining-tist-yuzheng","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2015\/09\/TrajectoryDataMining-tist-yuzheng.pdf","id":365009,"label_id":0},{"type":"doi","title":"10.1145\/2743025","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"yuzheng","user_id":35088,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=yuzheng"}],"msr_impact_theme":[],"msr_research_lab":[199560],"msr_event":[],"msr_group":[],"msr_project":[170845,170858,170824],"publication":[],"video":[],"msr-tool":[234745],"msr_publication_type":"article","related_content":{"projects":[{"ID":170845,"post_title":"Computing with Spatial Trajectories","post_name":"computing-with-spatial-trajectories","post_type":"msr-project","post_date":"2011-11-08 23:36:50","post_modified":"2017-06-06 09:31:37","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/computing-with-spatial-trajectories\/","post_excerpt":"Editor: Yu Zheng,\u00a0Xiaofang Zhou Foreword by Jiawei Han Editorial board: Ralf Hartmut G\u00fcting, Hans-Peter Kriegel, Hanan Samet [Order it on Amazon] [Buy it from Springer] [Preview this book (Outline and Preface)] &nbsp; With the rapid development of wireless communication and mobile computing technologies and global positioning and navigational systems, spatial trajectory data has been mounting up, calling for systematic research and development of new computing technologies for storage, preprocessing, retrieving, and mining of trajectory data&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/170845"}]}},{"ID":170858,"post_title":"Location-Based Social Networks","post_name":"location-based-social-networks","post_type":"msr-project","post_date":"2011-11-13 23:09:13","post_modified":"2017-09-20 20:52:44","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/location-based-social-networks\/","post_excerpt":"The dimension of location brings social networks back to reality, bridging the gap between the physical world and online social networking services. In this project, we introduce and define the meaning of location-based social network (LBSN) and discuss the research philosophy behind LBSNs from the perspective of users and locations. News The 4th International Workshop on Location-Based Social Networks (LBSN 2012) will be held in conjunction with UbiComp 2012 at (CMU) Pittsburgh, USA. Dr. Yu&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/170858"}]}},{"ID":170824,"post_title":"Urban Computing","post_name":"urban-computing","post_type":"msr-project","post_date":"2016-07-03 10:26:01","post_modified":"2018-04-07 17:32:40","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/urban-computing\/","post_excerpt":"Concept\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 (\u4e2d\u6587\u4e3b\u9875) Urban computing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings, and human, to tackle the major issues that cities face, e.g. air pollution, increased energy consumption and traffic congestion. Urban computing connects unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods, to create win-win-win solutions that improve&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/170824"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/168066","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":3,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/168066\/revisions"}],"predecessor-version":[{"id":533203,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/168066\/revisions\/533203"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=168066"}],"wp:term":[{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=168066"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=168066"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=168066"},{"taxonomy":"msr-publisher","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publisher?post=168066"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=168066"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=168066"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=168066"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=168066"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=168066"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=168066"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=168066"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=168066"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}