{"id":161208,"date":"2011-08-24T00:00:00","date_gmt":"2011-08-24T00:00:00","guid":{"rendered":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/msr-research-item\/retrieving-k-nearest-neighboring-trajectories-by-a-set-of-point-locations\/"},"modified":"2018-10-16T21:29:03","modified_gmt":"2018-10-17T04:29:03","slug":"retrieving-k-nearest-neighboring-trajectories-by-a-set-of-point-locations","status":"publish","type":"msr-research-item","link":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/publication\/retrieving-k-nearest-neighboring-trajectories-by-a-set-of-point-locations\/","title":{"rendered":"Retrieving k-Nearest Neighboring Trajectories by a Set of Point Locations"},"content":{"rendered":"
\n

The advance of object tracking technologies leads to huge volumes of spatio-temporal data accumulated in the form of location trajectories. Such data bring us new opportunities and challenges in efficient trajectory retrieval. In this paper, we study a new type of query that finds the k Nearest Neighboring Trajectories (k-NNT) with the minimum aggregated distance to a set of query points. Such queries, though have a broad range of applications like trip planning and moving object study, cannot be handled by traditional k-NN query processing techniques that only find the neighboring points of an object. To facilitate scalable, flexible and effective query execution, we propose a k-NN trajectory retrieval algorithm using a candidate-generation-and-verification strategy. The algorithm utilizes a data structure called global heap to retrieve candidate trajectories near each individual query point. Then, at the verification step, it refines these trajectory candidates by a lower-bound computed based on the global heap. The global heap guarantees the candidate\u2019s completeness (i.e., all the k-NNTs are included), and reduces the computational overhead of candidate verification. In addition, we propose a qualifier expectation measure that ranks partial-matching candidate trajectories to accelerate query processing in the cases of non-uniform trajectory distributions or outlier query locations. Extensive experiments on both real and synthetic trajectory datasets demonstrate the feasibility and effectiveness of proposed methods.<\/p>\n<\/div>\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

The advance of object tracking technologies leads to huge volumes of spatio-temporal data accumulated in the form of location trajectories. Such data bring us new opportunities and challenges in efficient trajectory retrieval. In this paper, we study a new type of query that finds the k Nearest Neighboring Trajectories (k-NNT) with the minimum aggregated distance […]<\/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":null,"msr_publishername":"SSTD 2011","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the 12th Symposium on Spatial and Temporal Databases - SSTD 2011","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of the 12th Symposium on Spatial and Temporal Databases - SSTD 2011","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Lu-An Tang, Xiao Yu, Jiawei Han, Jing Yuan","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":"2011-08-24","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":2011,"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":[13563],"msr-publication-type":[193716],"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-161208","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"SSTD 2011","msr_edition":"Proceedings of the 12th Symposium on Spatial and Temporal Databases - 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