{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T05:10:17Z","timestamp":1755925817782,"version":"3.37.3"},"reference-count":17,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2014,3,1]],"date-time":"2014-03-01T00:00:00Z","timestamp":1393632000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2014,3,1]]},"abstract":"<jats:p> The Internet of Things (IoT) provides a new way to improve the transportation system. The key issue is how to process the numerous events generated by IoT. In this paper, a proactive complex event processing method is proposed for large-scale transportation IoT. Based on a multilayered adaptive dynamic Bayesian model, a Bayesian network structure learning algorithm using search-and-score is proposed to support accurate predictive analytics. A parallel Markov decision processes model is designed to support proactive event processing. State partitioning and mean field based approximation are used to support large-scale application. The experimental evaluations show that this method can support proactive complex event processing well in large-scale transportation Internet of Things. <\/jats:p>","DOI":"10.1155\/2014\/159052","type":"journal-article","created":{"date-parts":[[2014,3,23]],"date-time":"2014-03-23T21:00:52Z","timestamp":1395608452000},"page":"159052","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":23,"title":["A Proactive Complex Event Processing Method for Large-Scale Transportation Internet of Things"],"prefix":"10.1177","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7675-2327","authenticated-orcid":false,"given":"Yongheng","family":"Wang","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Hunan University, Changsha 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kening","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan University, Changsha 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2014,3,23]]},"reference":[{"volume-title":"The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems","year":"2002","author":"Luckham D. 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