{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:54:19Z","timestamp":1743112459607,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031208904"},{"type":"electronic","value":"9783031208911"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20891-1_34","type":"book-chapter","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:03:02Z","timestamp":1667779382000},"page":"478-492","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Learning-Based Approach for\u00a0Multi-scenario Trajectory Similarity Search"],"prefix":"10.1007","author":[{"given":"Chunhui","family":"Feng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhicheng","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhua","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pingfu","family":"Chao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"An","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,7]]},"reference":[{"issue":"9","key":"34_CR1","first-page":"2049","volume":"25","author":"C Ma","year":"2012","unstructured":"Ma, C., Hua, L., Shou, L., Chen, G.: Ksq: Top-k similarity query on uncertain trajectories. IEEE Trans. Knowl. Data Eng. 25(9), 2049\u20132062 (2012)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"34_CR2","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1145\/2782759.2782767","volume":"7","author":"K Toohey","year":"2015","unstructured":"Toohey, K., Duckham, M.: Trajectory similarity measures. Sigspatial Spec. 7(1), 43\u201350 (2015)","journal-title":"Sigspatial Spec."},{"key":"34_CR3","doi-asserted-by":"crossref","unstructured":"Fang, Z., Du, Y., Chen, L., Hu, Y., Gao, Y., Chen, G.: E2 dtc: an end to end deep trajectory clustering framework via self-training. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 696\u2013707. IEEE (2021)","DOI":"10.1109\/ICDE51399.2021.00066"},{"issue":"11","key":"34_CR4","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673\u20132681 (1997)","journal-title":"IEEE Trans. Signal Process."},{"key":"34_CR5","unstructured":"Yi, B.-K., Jagadish, H.V., Faloutsos, C.: Efficient retrieval of similar time sequences under time warping. In: Proceedings 14th International Conference on Data Engineering, pp. 201\u2013208. IEEE (1998)"},{"key":"34_CR6","doi-asserted-by":"crossref","unstructured":"Sakurai, Y., Yoshikawa, M., Faloutsos, C.: Ftw: fast similarity search under the time warping distance. In: Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 326\u2013337 (2005)","DOI":"10.1145\/1065167.1065210"},{"key":"34_CR7","doi-asserted-by":"crossref","unstructured":"Rakthanmanon, T., et al. Searching and mining trillions of time series subsequences under dynamic time warping. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 262\u2013270 (2012)","DOI":"10.1145\/2339530.2339576"},{"key":"34_CR8","doi-asserted-by":"crossref","unstructured":"Chen, L., Tamer \u00d6zsu, M., Oria, V.: Robust and fast similarity search for moving object trajectories. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 491\u2013502 (2005)","DOI":"10.1145\/1066157.1066213"},{"key":"34_CR9","doi-asserted-by":"crossref","unstructured":"Li, X., Zhao, K., Cong, G., Jensen, C.S., Wei, W.: Deep representation learning for trajectory similarity computation. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp. 617\u2013628. IEEE (2018)","DOI":"10.1109\/ICDE.2018.00062"},{"key":"34_CR10","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, A., Liu, G., Li, Z., Li, Q.: Deep representation learning of activity trajectory similarity computation. In: 2019 IEEE International Conference on Web Services (ICWS), pp. 312\u2013319. IEEE (2019)","DOI":"10.1109\/ICWS.2019.00059"},{"key":"34_CR11","doi-asserted-by":"crossref","unstructured":"Yao, D., Cong, G., Zhang, C., Bi, J.: Computing trajectory similarity in linear time: a generic seed-guided neural metric learning approach. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 1358\u20131369. IEEE (2019)","DOI":"10.1109\/ICDE.2019.00123"},{"key":"34_CR12","unstructured":"Yao, D., Cong, G., Zhang, C., Meng, X., Duan, R., Bi, J.: A linear time approach to computing time series similarity based on deep metric learning. IEEE Trans. Knowl. Data Eng. (2020)"},{"key":"34_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhao, K., Cong, G.: Efficient similar region search with deep metric learning. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1850\u20131859 (2018)","DOI":"10.1145\/3219819.3220031"},{"key":"34_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, D., Li, N., Zhou, Z.H., Chen, C., Sun, L., Li, S.: ibat: detecting anomalous taxi trajectories from gps traces. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 99\u2013108 (2011)","DOI":"10.1145\/2030112.2030127"},{"key":"34_CR15","doi-asserted-by":"crossref","unstructured":"Yang, P., Wang, H., Zhang, Y., Qin, L., Zhang, W., Lin, X.: T3s: effective representation learning for trajectory similarity computation. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 2183\u20132188. IEEE (2021)","DOI":"10.1109\/ICDE51399.2021.00221"},{"key":"34_CR16","doi-asserted-by":"crossref","unstructured":"Siami-Namini, S., Tavakoli, N., Namin, A.S.: The performance of LSTM and BILSTM in forecasting time series. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 3285\u20133292. IEEE (2019)","DOI":"10.1109\/BigData47090.2019.9005997"},{"key":"34_CR17","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.eswa.2015.08.048","volume":"44","author":"L Moreira-Matias","year":"2016","unstructured":"Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L.: Time-evolving OD matrix estimation using high-speed GPS data streams. Expert Syst. Appl. 44, 275\u2013288 (2016)","journal-title":"Expert Syst. Appl."},{"key":"34_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2019.101452","volume":"98","author":"S Taghizadeh","year":"2021","unstructured":"Taghizadeh, S., Elekes, A., Sch\u00e4ler, M., B\u00f6hm, K.: How meaningful are similarities in deep trajectory representations? Inf. Syst. 98, 101452 (2021)","journal-title":"Inf. Syst."}],"container-title":["Lecture Notes in Computer Science","Web Information Systems Engineering \u2013 WISE 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20891-1_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:43:19Z","timestamp":1667781799000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20891-1_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031208904","9783031208911"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20891-1_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"7 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Biarritz","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wise2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/wise2022.sigappfr.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"94","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The proceedings include 3 demo papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}