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What has to be cleaned is the set of trajectories that are the possible interpretations of the readings: a trajectory in this set is a sequence whose generic element is a location covered by the reader(s) that made the detection at the corresponding time point. The cleaning is guided by integrity constraints and consists of discarding the inconsistent trajectories and assigning to the others a suitable probability of being the actual one. The probabilities are evaluated by adopting\n            <jats:italic>probabilistic conditioning<\/jats:italic>\n            that logically consists of the following steps. First, the trajectories are assigned\n            <jats:italic>a priori<\/jats:italic>\n            probabilities that rely on the independence assumption between the time points. Then, these probabilities are revised according to the spatio-temporal correlations encoded by the constraints. This is done by conditioning the\n            <jats:italic>a priori<\/jats:italic>\n            probability of each trajectory to the event that the constraints are satisfied: this means taking the ratio of this\n            <jats:italic>a priori<\/jats:italic>\n            probability to the sum of the\n            <jats:italic>a priori<\/jats:italic>\n            probabilities of all the consistent trajectories. Instead of performing these steps by materializing all the trajectories and their\n            <jats:italic>a priori<\/jats:italic>\n            probabilities (which is infeasible, owing to the typically huge number of trajectories), our approach exploits a data structure\n            <jats:italic>called conditioned trajectory graph (ct-graph)<\/jats:italic>\n            that compactly represents the trajectories and their conditioned probabilities, and an algorithm for efficiently constructing the ct-graph, which progressively builds it while avoiding the construction of components encoding inconsistent trajectories.\n          <\/jats:p>","DOI":"10.1145\/2939368","type":"journal-article","created":{"date-parts":[[2016,11,4]],"date-time":"2016-11-04T12:49:04Z","timestamp":1478263744000},"page":"1-52","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Exploiting Integrity Constraints for Cleaning Trajectories of RFID-Monitored Objects"],"prefix":"10.1145","volume":"41","author":[{"given":"Bettina","family":"Fazzinga","sequence":"first","affiliation":[{"name":"ICAR-CNR, Rende (CS), Italy"}]},{"given":"Sergio","family":"Flesca","sequence":"additional","affiliation":[{"name":"DIMES, University of Calabria, Italy"}]},{"given":"Filippo","family":"Furfaro","sequence":"additional","affiliation":[{"name":"DIMES, University of Calabria, Italy"}]},{"given":"Francesco","family":"Parisi","sequence":"additional","affiliation":[{"name":"DIMES, University of Calabria, Italy"}]}],"member":"320","published-online":{"date-parts":[[2016,11,2]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_2_1_1","DOI":"10.1109\/78.978374"},{"doi-asserted-by":"publisher","key":"e_1_2_2_2_1","DOI":"10.1109\/MDM.2014.21"},{"doi-asserted-by":"publisher","key":"e_1_2_2_3_1","DOI":"10.1109\/MDM.2013.68"},{"volume-title":"Proc. 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