{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T22:20:56Z","timestamp":1780438856844,"version":"3.54.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000},"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":["Geoinformatica"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s10707-020-00408-9","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T22:02:25Z","timestamp":1592258545000},"page":"269-289","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["SWS: an unsupervised trajectory segmentation algorithm based on change detection with interpolation kernels"],"prefix":"10.1007","volume":"25","author":[{"given":"Mohammad","family":"Etemad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amilcar","family":"Soares","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elham","family":"Etemad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jordan","family":"Rose","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis","family":"Torgo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stan","family":"Matwin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,6,15]]},"reference":[{"key":"408_CR1","doi-asserted-by":"crossref","unstructured":"Adibi P, Pranovi F, Raffaet\u00e0 A, Russo E, Silvestri C, Simeoni M, Soares A, Matwin S (2019) Predicting fishing effort and catch using semantic trajectories and machine learning. In: International workshop on multiple-aspect analysis of semantic trajectories. Springer, Berlin, pp 83\u201399","DOI":"10.1007\/978-3-030-38081-6_7"},{"key":"408_CR2","doi-asserted-by":"publisher","unstructured":"Alvares LO, Bogorny V, Kuijpers B, de Macedo JAF, Moelans B, Vaisman A (2007) A model for enriching trajectories with semantic geographical information. In: Proceedings of the 15th annual ACM international symposium on advances in geographic information systems, GIS \u201907. ACM, New York, pp 22:1\u201322:8. https:\/\/doi.org\/10.1145\/1341012.1341041","DOI":"10.1145\/1341012.1341041"},{"issue":"2","key":"408_CR3","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1080\/13658816.2012.692791","volume":"27","author":"F Biljecki","year":"2013","unstructured":"Biljecki F, Ledoux H, Van Oosterom P (2013) Transportation mode-based segmentation and classification of movement trajectories. Int J Geogr Inf Sci 27 (2):385\u2013407","journal-title":"Int J Geogr Inf Sci"},{"key":"408_CR4","unstructured":"Carlini E, de Lira VM, Soares A, Etemad M, Machado BB, Matwin S (2020) Uncovering vessel movement patterns from AIS data with graph evolution analysis. In: EDBT\/ICDT Workshops"},{"issue":"2","key":"408_CR5","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1145\/2543581.2543584","volume":"46","author":"PS Castro","year":"2013","unstructured":"Castro PS, Zhang D, Chen C, Li S, Pan G (2013) From taxi gps traces to social and community dynamics: a survey. ACM Computing Surveys (CSUR) 46(2):17","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"408_CR6","doi-asserted-by":"publisher","unstructured":"Dividino R, Soares A, Matwin S, Isenor AW, Webb S, Brousseau M (2018) Semantic integration of real-time heterogeneous data streams for ocean-related decision making. In: Big data and artificial intelligence for military decision making. STO. https:\/\/doi.org\/10.14339\/STO-MP-IST-160-S1-3-PDF","DOI":"10.14339\/STO-MP-IST-160-S1-3-PDF"},{"issue":"6","key":"408_CR7","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/j.compenvurbsys.2009.07.008","volume":"33","author":"S Dodge","year":"2009","unstructured":"Dodge S, Weibel R, Forootan E (2009) Revealing the physics of movement: comparing the similarity of movement characteristics of different types of moving objects. Comput Environ Urban Syst 33(6):419\u2013434","journal-title":"Comput Environ Urban Syst"},{"issue":"1","key":"408_CR8","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/s40462-016-0086-5","volume":"4","author":"H Edelhoff","year":"2016","unstructured":"Edelhoff H, Signer J, Balkenhol N (2016) Path segmentation for beginners: an overview of current methods for detecting changes in animal movement patterns. Movement Ecology 4(1):21","journal-title":"Movement Ecology"},{"key":"408_CR9","unstructured":"Ester M, Kriegel HP, Sander J, Xu X, et al. (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, vol 96, pp 226\u2013231"},{"key":"408_CR10","doi-asserted-by":"crossref","unstructured":"Etemad M, Etemad Z, Soares A, Bogorny V, Matwin S, Torgo L (2020) Wise sliding window segmentation: a classification-aided approach for trajectory segmentation. In: Goutte C, Zhu X (eds) Advances in artificial intelligence. Canadian AI 2020. Lecture notes in computer science, vol 12109. Springer, Cham","DOI":"10.1007\/978-3-030-47358-7_20"},{"key":"408_CR11","unstructured":"Etemad M, Soares A, Hoseyni A, Rose J, Matwin S (2019) A trajectory segmentation algorithm based on interpolation-based change detection strategies. In: EDBT\/ICDT workshops"},{"key":"408_CR12","doi-asserted-by":"crossref","unstructured":"Etemad M, Soares J\u00fanior A, Matwin S (2018) Predicting transportation modes of gps trajectories using feature engineering and noise removal. In: Advances in artificial intelligence: 31st Canadian conference on artificial intelligence, Canadian AI 2018, Toronto, ON, Canada, May 8\u201311, 2018, Proceedings 31. Springer, pp 259\u2013264","DOI":"10.1007\/978-3-319-89656-4_24"},{"key":"408_CR13","doi-asserted-by":"crossref","unstructured":"Etemad M, Zare N, Sarvmaili M, Soares A, Brandoli Machado B, Matwin S (2020) Using deep reinforcement learning methods for autonomous vessels in 2D environments. In: Goutte C, Zhu X (eds) Advances in artificial intelligence. Canadian AI 2020. Lecture notes in computer science, vol 12109. Springer, Cham","DOI":"10.1007\/978-3-030-47358-7_21"},{"key":"408_CR14","doi-asserted-by":"crossref","unstructured":"Feng S, Cong G, An B, Chee YM (2017) POI2Vec: geographical latent representation for predicting future visitors. In: AAAI, pp 102\u2013108","DOI":"10.1609\/aaai.v31i1.10500"},{"issue":"1","key":"408_CR15","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1111\/1365-2656.12379","volume":"85","author":"E Gurarie","year":"2016","unstructured":"Gurarie E, Bracis C, Delgado M, Meckley TD, Kojola I, Wagner CM (2016) What is the animal doing? tools for exploring behavioural structure in animal movements. J Anim Ecol 85(1):69\u201384","journal-title":"J Anim Ecol"},{"key":"408_CR16","volume-title":"Data mining: concepts and techniques","author":"J Han","year":"2011","unstructured":"Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, Amsterdam"},{"issue":"5","key":"408_CR17","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1109\/MCG.2017.3621221","volume":"37","author":"AS J\u00fanior","year":"2017","unstructured":"J\u00fanior AS, Renso C, Matwin S (2017) ANALYTIc: an active learning system for trajectory classification. IEEE Comput Graph Appl 37(5):28\u201339. https:\/\/doi.org\/10.1109\/MCG.2017.3621221","journal-title":"IEEE Comput Graph Appl"},{"key":"408_CR18","doi-asserted-by":"crossref","unstructured":"Junior AS, Times VC, Renso C, Matwin S, Cabral LAF (2018) A semisupervised approach for the semantic segmentation of trajectories. In: 19th IEEE international conference on mobile data management (MDM). IEEE, pp 145\u2013154","DOI":"10.1109\/MDM.2018.00031"},{"key":"408_CR19","doi-asserted-by":"crossref","unstructured":"Lee JG, Han J, Whang KY (2007) Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD international conference on management of data. ACM, pp 593\u2013604","DOI":"10.1145\/1247480.1247546"},{"key":"408_CR20","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.ins.2013.02.042","volume":"237","author":"LA Leiva","year":"2013","unstructured":"Leiva LA, Vidal E (2013) Warped K-Means: an algorithm to cluster sequentially-distributed data. Inform Sci 237:196\u2013210. https:\/\/doi.org\/10.1016\/j.ins.2013.02.042, http:\/\/www.sciencedirect.com\/science\/article\/pii\/S002002551300159X. Prediction, Control and Diagnosis using Advanced Neural Computations","journal-title":"Inform Sci"},{"issue":"5","key":"408_CR21","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1080\/13658816.2015.1081909","volume":"30","author":"JA Long","year":"2016","unstructured":"Long JA (2016) Kinematic interpolation of movement data. Int J Geogr Inf Sci 30(5):854\u2013868","journal-title":"Int J Geogr Inf Sci"},{"key":"408_CR22","unstructured":"MacQueen J, et al. (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. Oakland, CA, USA, vol 1, pp 281\u2013297"},{"key":"408_CR23","doi-asserted-by":"publisher","unstructured":"Moreno BN, Soares J\u00fanior A, Times VC, Tedesco P, Matwin S (2014) Weka-SAT: a hierarchical context-based inference engine to enrich trajectories with semantics. In: Advances in artificial intelligence. Springer International Publishing, Cham, pp 333\u2013338. https:\/\/doi.org\/10.1007\/978-3-319-06483-3_34","DOI":"10.1007\/978-3-319-06483-3_34"},{"key":"408_CR24","doi-asserted-by":"publisher","unstructured":"Palma AT, Bogorny V, Kuijpers B, Alvares LO (2008) A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the 2008 ACM symposium on applied computing, SAC \u201908. ACM, New York, pp 863\u2013868. https:\/\/doi.org\/10.1145\/1363686.1363886","DOI":"10.1145\/1363686.1363886"},{"key":"408_CR25","doi-asserted-by":"crossref","unstructured":"Rocha JAM, Times VC, Oliveira G, Alvares LO, Bogorny V (2010) DB-SMOt: a direction-based spatio-temporal clustering method. In: 2010 5th IEEE international conference intelligent systems (IS). IEEE, pp 114\u2013119","DOI":"10.1109\/IS.2010.5548396"},{"key":"408_CR26","doi-asserted-by":"crossref","unstructured":"Soares A, Dividino R, Abreu F, Brousseau M, Isenor AW, Webb S, Matwin S (2019) CRISIS: integrating AIS and ocean data streams using semantic web standards for event detection. In: International conference on military communications and information systems (ICMCIS). IEEE, pp 1\u20137","DOI":"10.1109\/ICMCIS.2019.8842749"},{"key":"408_CR27","unstructured":"Soares A, Rose J, Etemad M, Renso C, Matwin S (2019) VISTA: A visual analytics platform for semantic annotation of trajectories. In: Proceedings of the 22nd international conference on extending database technology (EDBT)"},{"issue":"1","key":"408_CR28","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1080\/13658816.2014.938078","volume":"29","author":"A Soares J\u00fanior","year":"2015","unstructured":"Soares J\u00fanior A, Moreno BN, Times VC, Matwin S, Cabral LAF (2015) GRASP-UTS: An algorithm for unsupervised trajectory segmentation. Int J Geogr Inf Sci 29(1):46\u201368","journal-title":"Int J Geogr Inf Sci"},{"issue":"7","key":"408_CR29","doi-asserted-by":"publisher","first-page":"e0158248","DOI":"10.1371\/journal.pone.0158248","volume":"11","author":"EN de Souza","year":"2016","unstructured":"de Souza EN, Boerder K, Matwin S, Worm B (2016) Improving fishing pattern detection from satellite AIS using data mining and machine learning. Plos one 11(7):e0158248","journal-title":"Plos one"},{"issue":"6","key":"408_CR30","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1080\/13658816.2014.999682","volume":"29","author":"G Technitis","year":"2015","unstructured":"Technitis G, Othman W, Safi K, Weibel R (2015) From a to b, randomly: a point-to-point random trajectory generator for animal movement. Int J Geogr Inf Sci 29(6):912\u2013934","journal-title":"Int J Geogr Inf Sci"},{"issue":"10","key":"408_CR31","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1038\/nclimate2342","volume":"4","author":"TM Thompson","year":"2014","unstructured":"Thompson TM, Rausch S, Saari RK, Selin NE (2014) A systems approach to evaluating the air quality co-benefits of us carbon policies. Nat Clim Chang 4(10):917","journal-title":"Nat Clim Chang"},{"issue":"1","key":"408_CR32","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1242\/jeb.01970","volume":"209","author":"Y Tremblay","year":"2006","unstructured":"Tremblay Y, Shaffer SA, Fowler SL, Kuhn CE, McDonald BI, Weise MJ, Bost CA, Weimerskirch H, Crocker DE, Goebel ME, et al. (2006) Interpolation of animal tracking data in a fluid environment. J Exp Biol 209(1):128\u2013140","journal-title":"J Exp Biol"},{"key":"408_CR33","unstructured":"Varlamis I, Tserpes K, Etemad M, J\u00fanior AS, Matwin S (2019) A network abstraction of multi-vessel trajectory data for detecting anomalies. In: EDBT\/ICDT workshops"},{"key":"408_CR34","doi-asserted-by":"publisher","unstructured":"Varlamis I, Tserpes K, Sardianos C (2018) Detecting search and rescue missions from AIS data. In: 2018 IEEE 34th international conference on data engineering workshops (ICDEW), pp 60\u201365. https:\/\/doi.org\/10.1109\/ICDEW.2018.00017","DOI":"10.1109\/ICDEW.2018.00017"},{"key":"408_CR35","doi-asserted-by":"crossref","unstructured":"Yan Z, Giatrakos N, Katsikaros V, Pelekis N, Theodoridis Y (2011) Setrastream: semantic-aware trajectory construction over streaming movement data. In: International symposium on spatial and temporal databases. Springer, pp 367\u2013385","DOI":"10.1007\/978-3-642-22922-0_22"},{"key":"408_CR36","doi-asserted-by":"crossref","unstructured":"Zhang D, Li J, Wu Q, Liu X, Chu X, He W (2017) Enhance the AIS data availability by screening and interpolation. In: 2017 4th international conference on transportation information and safety (ICTIS). IEEE, pp 981\u2013986","DOI":"10.1109\/ICTIS.2017.8047888"},{"key":"408_CR37","unstructured":"Zheng Y, Fu H, Xie X, Ma W, Li Q (2011) Geolife gps trajectory dataset-user guide"},{"issue":"1","key":"408_CR38","first-page":"5","volume":"5","author":"Y Zheng","year":"2011","unstructured":"Zheng Y, Zhang L, Ma Z, Xie X, Ma WY (2011) Recommending friends and locations based on individual location history. ACM Transactions on the Web (TWEB) 5(1):5","journal-title":"ACM Transactions on the Web (TWEB)"}],"container-title":["GeoInformatica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-020-00408-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10707-020-00408-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-020-00408-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T19:31:31Z","timestamp":1666985491000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10707-020-00408-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,15]]},"references-count":38,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["408"],"URL":"https:\/\/doi.org\/10.1007\/s10707-020-00408-9","relation":{},"ISSN":["1384-6175","1573-7624"],"issn-type":[{"value":"1384-6175","type":"print"},{"value":"1573-7624","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,15]]},"assertion":[{"value":"7 August 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}