@inproceedings{c04360d243a9462d989166d7560b3445,
title = "A sliding window method for online tracking of spatiotemporal event patterns",
abstract = "Online Tracking of Spatiotemporal Event Patterns (OTSEP) is important in the fields of smart home and Internet of Things (IoT), but difficult to be resolved due to various noises. On account of the strong learning capability in noisy environments, Learning Automaton (LA) has been adopted in the existing literature to notify users once a pattern disappears, and suppress the notification to avoid the distraction from noise if a pattern exists. However, the LA-based models require continuous and identical responses from the environment to jump to another action, which lowers their learning speed especially when the noise level is high. This paper proposes a sliding window method, with which the learning speed is stable in different environments. Experimental results show that the learning accuracy and speed are greatly improved over the existing methods in dynamic and noisy environments.",
keywords = "Sliding window, Spatiotemporal event patterns",
author = "Junqi Zhang and Shanwen Zhu and Di Zang and Mengchu Zhou",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 9th International Conference on Internet and Distributed Computing Systems, IDCS 2016 ; Conference date: 28-09-2016 Through 30-09-2016",
year = "2016",
doi = "10.1007/978-3-319-45940-0_48",
language = "English (US)",
isbn = "9783319459394",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "513--524",
editor = "Wenfeng Li and Qiang Wang and Gabriel Lodewijks and Antonio Guerrieri and Mukaddim Pathan and Giancarlo Fortino and {Di Fatta}, Giuseppe and Shawkat Ali and Zhouping Yin",
booktitle = "Internet and Distributed Computing Systems - 9th International Conference, IDCS 2016, Proceedings",
address = "Germany",
}