McGuire, Michael P.Janeja, VandanaGangopadhyay, Aryya2020-11-122020-11-122008-01McGuire, Michael P.; Janeja, Vandana P.; Gangopadhyay, Aryya; Spatiotemporal Neighborhood Discovery for Sensor Data; Sensor-KDD 2008: Knowledge Discovery from Sensor Data, pp 203-225; https://link.springer.com/chapter/10.1007%2F978-3-642-12519-5_12https://doi.org/10.1007/978-3-642-12519-5_12http://hdl.handle.net/11603/20043Proceedings of the Second International Workshop on Knowledge Discovery from Sensor Data (Sensor-KDD 2008), August 24, 2008, Las Vegas, Nevada, USAThe focus of this paper is the discovery of spatiotemporal neighborhoods in sensor datasets where a time series of data is collected at many spatial locations. The purpose of the spatiotemporal neighborhoods is to provide regions in the data where knowledge discovery tasks such as outlier detection, can be focused. As building blocks for the spatiotemporal neighborhoods, we have developed a method to generate spatial neighborhoods and a method to discretize temporal intervals. These methods were tested on real life datasets including (a) sea surface temperature data from the Tropical Atmospheric Ocean Project (TAO) array in the Equatorial Pacific Ocean and (b)highway sensor network data archive. We have found encouraging results which are validated by real life phenomenon.14 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.© Springer-Verlag Berlin Heidelberg 2010Text