![]() He has published over 200 papers in refereed conferences and journals, and has applied for, or been granted, over 80 patents. He has since worked in the field of performance analysis, databases, and data mining. from Massachusetts Institute of Technology in 1996. Watson Research Center in Yorktown Heights, New York. Aggarwal is a Research Scientist at the IBM T. fellowship and is broadly interested in data and information analysis with a focus on information integration, ensemble methods, transfer learning, anomaly detection, and mining data streams. She is currently an assistant professor in the Computer Science and Engineering Department of the State University of New York at Buffalo. ![]() from University of Illinois at Urbana Champaign in 2011. ![]() His research interests are in the areas of data mining, information retrieval, and web mining. He worked for Yahoo! Bangalore from 2007 to 2009. in Computer Science from University of Illinois at Urbana Champaign in 2013. He received his Masters in Computer Science from IIT Bombay in 2007 and his Ph.D. He is also an adjunct faculty at the International Institute of Information Technology, Hyderabad (IIIT-H), India. Manish Gupta is an applied researcher at Microsoft Bing, India. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. Compared to general outlier detection, techniques for temporal outlier detection are very different. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. ![]() In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. A large number of applications generate temporal datasets. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. Initial research in outlier detection focused on time series-based outliers (in statistics). Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. ![]()
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