{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:49:42Z","timestamp":1750308582226,"version":"3.41.0"},"reference-count":23,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2016,12,9]],"date-time":"2016-12-09T00:00:00Z","timestamp":1481241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGSPATIAL Special"],"published-print":{"date-parts":[[2016,12,9]]},"abstract":"<jats:p>To unify and integrate various types, resolutions, and levels of uncertainty of user location data, we employ a Bayesian learning approach, which allows to iteratively add new knowledge to refine a model describing the motion of an object in space and time. By explicitly modelling uncertainty, we maintain a notion of data reliability, such that the confidence of any query and mining results can be assessed. Based on this motion model for individual users, we present our algorithms for estimating the continuous location of users based on sparse observations. Our approach uses a global traffic model using a Markov-chain as an apriori-model, which is learned from all available historic trajectory data. Starting from this apriori-model, we use observations of individual users to adapt this model, using a forward backward approach to add new knowledge to the model. This yields as user-specific aposteriori-model, which captures information of their observation, and uses the apriori-knowledge to model the error and uncertainty in-between these observations. As verified by our empirical study on real trajectory data, this model allows to predict the location of objects in-between discrete observation much more accurate than competing approaches, thus significantly reducing the uncertainty in spatio-temporal data.<\/jats:p>","DOI":"10.1145\/3024087.3024090","type":"journal-article","created":{"date-parts":[[2016,12,13]],"date-time":"2016-12-13T14:34:05Z","timestamp":1481639645000},"page":"18-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Bayesian network movement model"],"prefix":"10.1145","volume":"8","author":[{"given":"Andreas","family":"Z\u00fcfle","sequence":"first","affiliation":[{"name":"George Mason University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2016,12,9]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-02279-1_31"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2011.5767890"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2396761.2396813"},{"key":"e_1_2_1_4_1","volume-title":"A First Course in Stochastic Processes","author":"Karlin S.","year":"1975","unstructured":"S. Karlin and H. M. Taylor . A First Course in Stochastic Processes , volume 2 . Academic Pr Inc , 1975 . S. Karlin and H. M. Taylor. A First Course in Stochastic Processes, volume 2. Academic Pr Inc, 1975."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcss.2009.10.002"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/MDM.2004.1263051"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732232.2732239"},{"key":"e_1_2_1_8_1","volume-title":"Random Variables, and Stochastic Processes","author":"Papoulis A.","year":"1984","unstructured":"A. Papoulis . Probability , Random Variables, and Stochastic Processes , 2 nd ed. McGraw-Hill , 1984 . A. Papoulis. Probability, Random Variables, and Stochastic Processes, 2nd ed. McGraw-Hill, 1984.","edition":"2"},{"key":"e_1_2_1_9_1","first-page":"396","volume-title":"Novel approaches to the indexing of moving object trajectories","author":"Pfoser D.","year":"2000","unstructured":"D. Pfoser , C. S. Jensen , and Y. Theodoridis . Novel approaches to the indexing of moving object trajectories . pages 396 -- 406 , 2000 . D. Pfoser, C. S. Jensen, and Y. Theodoridis. Novel approaches to the indexing of moving object trajectories. pages 396--406, 2000."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1376616.1376688"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335427"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1007568.1007637"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/564691.564730"},{"key":"e_1_2_1_14_1","first-page":"287","volume-title":"Continuous nearest neighbor search","author":"Tao Y.","year":"2002","unstructured":"Y. Tao , D. Papadias , and Q. Shen . Continuous nearest neighbor search . pages 287 -- 298 , 2002 . Y. Tao, D. Papadias, and Q. Shen. Continuous nearest neighbor search. pages 287--298, 2002."},{"key":"e_1_2_1_15_1","first-page":"790","volume-title":"The tpr*tree: An optimized spatio-temporal access method for predictive queries","author":"Tao Y.","year":"2003","unstructured":"Y. Tao , D. Papadias , and J. Sun . The tpr*tree: An optimized spatio-temporal access method for predictive queries . pages 790 -- 801 , 2003 . Y. Tao, D. Papadias, and J. Sun. The tpr*tree: An optimized spatio-temporal access method for predictive queries. pages 790--801, 2003."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/MDM.2010.76"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1516360.1516460"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1016028.1016030"},{"key":"e_1_2_1_19_1","first-page":"233","volume-title":"The geometry of uncertainty in moving objects databases","author":"Trajcevski G.","year":"2002","unstructured":"G. Trajcevski , O. Wolfson , F. Zhang , and S. Chamberlain . The geometry of uncertainty in moving objects databases . pages 233 -- 250 , 2002 . G. Trajcevski, O. Wolfson, F. Zhang, and S. Chamberlain. The geometry of uncertainty in moving objects databases. pages 233--250, 2002."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1516360.1516439"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020462"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1869790.1869807"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/2124413"}],"container-title":["SIGSPATIAL Special"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3024087.3024090","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3024087.3024090","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T19:05:03Z","timestamp":1750273503000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3024087.3024090"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12,9]]},"references-count":23,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2016,12,9]]}},"alternative-id":["10.1145\/3024087.3024090"],"URL":"https:\/\/doi.org\/10.1145\/3024087.3024090","relation":{},"ISSN":["1946-7729"],"issn-type":[{"type":"electronic","value":"1946-7729"}],"subject":[],"published":{"date-parts":[[2016,12,9]]},"assertion":[{"value":"2016-12-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}