{"id":333041,"date":"2016-12-07T12:55:33","date_gmt":"2016-12-07T20:55:33","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=333041"},"modified":"2018-10-16T19:58:32","modified_gmt":"2018-10-17T02:58:32","slug":"trajectory-preprocessing","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/trajectory-preprocessing\/","title":{"rendered":"Trajectory Preprocessing"},"content":{"rendered":"<p><span lang=\"EN\">A spatial trajectory is a sequences of (x,y) points, each with a time stamp. This chapter discusses low-level preprocessing of trajectories. First, it discusses how to reduce the size of data required to store a trajectory, in order to save storage costs and reduce redundant data. The data reduction techniques can run in a batch mode after the data is collected or in an on-line mode as the data is collected. Part of this discussion consists of methods to measure the error introduced by the data reduction techniques. The second part of the chapter discusses methods for filtering spatial trajectories to reduce measurement noise and to estimate higher level properties of a trajectory like its speed and direction. The methods include mean and median filtering, the Kalman filter, and the particle filter.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A spatial trajectory is a sequences of (x,y) points, each with a time stamp. This chapter discusses low-level preprocessing of trajectories. First, it discusses how to reduce the size of data required to store a trajectory, in order to save storage costs and reduce redundant data. The data reduction techniques can run in a batch [&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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13562,13563],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-333041","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-computer-vision","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"Springer New York","msr_edition":"Yu Zheng and Xiaofang Zhou (ed.) Computing with Spatial Trajectories, 2011","msr_affiliation":"","msr_published_date":"2011-09-26","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"3-33","msr_chapter":"","msr_isbn":"978-1-4614-1628-9","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"Yu Zheng and Xiaofang Zhou","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":"","msr_publicationurl":"http:\/\/link.springer.com\/book\/10.1007%2F978-1-4614-1629-6","msr_doi":"","msr_publication_uploader":[{"type":"url","title":"http:\/\/link.springer.com\/book\/10.1007%2F978-1-4614-1629-6","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"http:\/\/link.springer.com\/book\/10.1007%2F978-1-4614-1629-6"}],"msr-author-ordering":[{"type":"user_nicename","value":"jckrumm","user_id":32203,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=jckrumm"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[144633],"msr_project":[170845],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","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"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/333041"}],"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":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/333041\/revisions"}],"predecessor-version":[{"id":515993,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/333041\/revisions\/515993"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=333041"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=333041"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=333041"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=333041"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=333041"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=333041"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=333041"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=333041"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=333041"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=333041"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=333041"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=333041"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=333041"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=333041"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=333041"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=333041"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}