{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T05:59:01Z","timestamp":1771567141262,"version":"3.50.1"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030438869","type":"print"},{"value":"9783030438876","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-43887-6_32","type":"book-chapter","created":{"date-parts":[[2020,3,27]],"date-time":"2020-03-27T15:03:32Z","timestamp":1585321412000},"page":"390-401","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Mining Human Mobility Data to Discover Locations and Habits"],"prefix":"10.1007","author":[{"given":"Thiago","family":"Andrade","sequence":"first","affiliation":[]},{"given":"Brais","family":"Cancela","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o","family":"Gama","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,3,28]]},"reference":[{"key":"32_CR1","series-title":"EAI\/Springer Innovations in Communication and Computing","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/978-3-030-30911-4_21","volume-title":"Mobility Internet of Things 2018","author":"T Andrade","year":"2020","unstructured":"Andrade, T., Gama, J.: Identifying points of interest and similar individuals from raw GPS data. In: Cag\u00e1\u00f1ov\u00e1, D., Hor\u0148\u00e1kov\u00e1, N. (eds.) Mobility Internet of Things 2018. EAISICC, vol. 39, pp. 293\u2013305. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-30911-4_21"},{"issue":"5","key":"32_CR2","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s00779-003-0240-0","volume":"7","author":"D Ashbrook","year":"2003","unstructured":"Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Pers. Ubiquit. Comput. 7(5), 275\u2013286 (2003)","journal-title":"Pers. Ubiquit. Comput."},{"key":"32_CR3","first-page":"1","volume":"12","author":"D Berry","year":"2011","unstructured":"Berry, D.: The computational turn: thinking about the digital humanities. Cult. Mach. 12, 1\u201322 (2011)","journal-title":"Cult. Mach."},{"key":"32_CR4","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226\u2013231 (1996)"},{"key":"32_CR5","unstructured":"Herder, E., Siehndel, P.: Daily and weekly patterns in human mobility. In: UMAP Workshops. Citeseer (2012)"},{"issue":"5915","key":"32_CR6","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1126\/science.1167742","volume":"323","author":"D Lazer","year":"2009","unstructured":"Lazer, D., et al.: Computational social science. Science 323(5915), 721\u2013723 (2009)","journal-title":"Science"},{"key":"32_CR7","doi-asserted-by":"crossref","unstructured":"Lee, I., Cai, G., Lee, K.: Mining points-of-interest association rules from geo-tagged photos. In: 2013 46th Hawaii International Conference on System Sciences, pp. 1580\u20131588. IEEE (2013)","DOI":"10.1109\/HICSS.2013.401"},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., Ma, W.Y.: Mining user similarity based on location history. In: Proceedings of the 16th ACM SIGSPATIAL, p. 34. ACM (2008)","DOI":"10.1145\/1463434.1463477"},{"issue":"6","key":"32_CR9","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1109\/TSMCC.2007.905750","volume":"37","author":"H Liu","year":"2007","unstructured":"Liu, H., Darabi, H., Banerjee, P., Liu, J.: Survey of wireless indoor positioning techniques and systems. IEEE Trans. Syst. Man Cybern. B Cybern. Part C (Appl. Rev.) 37(6), 1067\u20131080 (2007)","journal-title":"IEEE Trans. Syst. Man Cybern. B Cybern. Part C (Appl. Rev.)"},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Sardianos, C., Varlamis, I., Bouras, G.: Extracting user habits from google maps history logs. In: 2018 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 690\u2013697. IEEE (2018)","DOI":"10.1109\/ASONAM.2018.8508442"},{"key":"32_CR11","doi-asserted-by":"crossref","unstructured":"Suzuki, J., Suhara, Y., Toda, H., Nishida, K.: Personalized visited-poi assignment to individual raw gps trajectories. arXiv preprint arXiv:1901.06257 (2019)","DOI":"10.1145\/3317667"},{"issue":"4","key":"32_CR12","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1109\/TMC.2017.2742953","volume":"17","author":"E Thuillier","year":"2018","unstructured":"Thuillier, E., Moalic, L., Lamrous, S., Caminada, A.: Clustering weekly patterns of human mobility through mobile phone data. IEEE Trans. Mob. Comput. 17(4), 817\u2013830 (2018)","journal-title":"IEEE Trans. Mob. Comput."},{"issue":"3","key":"32_CR13","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/s10115-018-1186-x","volume":"58","author":"E Toch","year":"2018","unstructured":"Toch, E., Lerner, B., Ben-Zion, E., Ben-Gal, I.: Analyzing large-scale human mobility data: a survey of machine learning methods and applications. Knowl. Inf. Syst. 58(3), 501\u2013523 (2018). https:\/\/doi.org\/10.1007\/s10115-018-1186-x","journal-title":"Knowl. Inf. Syst."},{"issue":"3","key":"32_CR14","doi-asserted-by":"publisher","first-page":"126","DOI":"10.3390\/ijgi7030126","volume":"7","author":"M Yang","year":"2018","unstructured":"Yang, M., Cheng, C., Chen, B.: Mining individual similarity by assessing interactions with personally significant places from GPS trajectories. ISPRS Int. J. Geo-Inf. 7(3), 126 (2018)","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"32_CR15","doi-asserted-by":"crossref","unstructured":"Ye, Y., Zheng, Y., Chen, Y., Feng, J., Xie, X.: Mining individual life pattern based on location history. In: Mobile Data Management: Systems, Services and Middleware, MDM 2009, pp. 1\u201310. IEEE (2009)","DOI":"10.1109\/MDM.2009.11"},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.Y.: Understanding mobility based on GPS data. In: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 312\u2013321. ACM (2008)","DOI":"10.1145\/1409635.1409677"},{"issue":"2","key":"32_CR17","first-page":"32","volume":"33","author":"Y Zheng","year":"2010","unstructured":"Zheng, Y., Xie, X., Ma, W.Y.: GeoLife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2), 32\u201339 (2010)","journal-title":"IEEE Data Eng. Bull."},{"key":"32_CR18","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th International Conference on World Wide Web, pp. 791\u2013800. ACM (2009)","DOI":"10.1145\/1526709.1526816"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-43887-6_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T01:07:57Z","timestamp":1707786477000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-43887-6_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030438869","9783030438876"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-43887-6_32","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"28 March 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"W\u00fcrzburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ecmlpkdd2019.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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"733","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":"130","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":"0","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":"18% - 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":"3.04","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":"5.3","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":"ECML PKDD Workshops Information: single-blind review, submissions: 200, full papers accepted: 70, short papers accepted: 46","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}