M. Ba, Sébastien Montenez, T. Abdessalem, P. Senellart
{"title":"Monitoring moving objects using uncertain web data","authors":"M. Ba, Sébastien Montenez, T. Abdessalem, P. Senellart","doi":"10.1145/2666310.2666370","DOIUrl":null,"url":null,"abstract":"A number of applications deal with monitoring moving objects: cars, aircrafts, ships, persons, etc. Traditionally, this requires capturing data from sensor networks, image or video analysis, or using other application-specific resources. We show in this demonstration paper how Web content can be exploited instead to gather information (trajectories, metadata) about moving objects. As this content is marred with uncertainty and inconsistency, we develop a methodology for estimating uncertainty and filtering the resulting data. We present as an application a demonstration of a system that constructs trajectories of ships from social networking data, presenting to a user inferred trajectories, meta-information, as well as uncertainty levels on extracted information and trustworthiness of data providers.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2666310.2666370","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
A number of applications deal with monitoring moving objects: cars, aircrafts, ships, persons, etc. Traditionally, this requires capturing data from sensor networks, image or video analysis, or using other application-specific resources. We show in this demonstration paper how Web content can be exploited instead to gather information (trajectories, metadata) about moving objects. As this content is marred with uncertainty and inconsistency, we develop a methodology for estimating uncertainty and filtering the resulting data. We present as an application a demonstration of a system that constructs trajectories of ships from social networking data, presenting to a user inferred trajectories, meta-information, as well as uncertainty levels on extracted information and trustworthiness of data providers.