Temporal and Spatio-Temporal Data Mining

Temporal and Spatio-Temporal Data Mining

Wynne Hsu (National University of Singapore, Singapore), Mong Li Lee (National University of Singapore, Singapore) and Junmei Wang (National University of Singapore, Singapore)
Indexed In: SCOPUS
Release Date: July, 2007|Copyright: © 2008 |Pages: 292
ISBN13: 9781599043876|ISBN10: 1599043874|EISBN13: 9781599043890|DOI: 10.4018/978-1-59904-387-6

Description

The recent surge of interest in spatio-temporal databases has resulted in numerous advances, such as: modeling, indexing, and querying of moving objects and spatio-temporal data. Aside from this, rule mining in spatial databases and temporal databases has been studied extensively in data mining research. Temporal and Spatio-Temporal Data Mining: Association Patterns and Applications examines the problem of mining topological patterns in spatio-temporal databases by imposing the temporal constraints into the process of mining spatial collocation patterns.

Temporal and Spatio-Temporal Data Mining: Association Patterns and Applications presents probable solutions when discovering the spatial sequence patterns by incorporating the spatial information into the sequence of patterns, and introduces two new classes of spatial sequence patterns: flow patterns and generalized spatio-temporal patterns. This innovative book addresses different scenarios when finding complex relationships in spatio-temporal data by modeling them as graphs, giving readers a comprehensive synopsis on two successful partition-based algorithms designed by the authors.

Reviews and Testimonials

The book will be a useful companion for graduate students studying the issues of data mining in spatio-temporal databases, and for instructors who can use the book as a reference for advanced topics in spatio-temporal databases.

– Wynne Hsu, National University of Singapore, Singapore

Table of Contents and List of Contributors

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Author(s)/Editor(s) Biography

Wynne Hsu is an associate professor at the Department of Computer Science and vice dean (Graduate Studies), School of Computing, National University of Singapore (NUS). She received her BSc in computer science at National University of Singapore and her MSc and PhD in electrical engineering from Purdue University, West Lafayette, USA, in 1989 and 1994, respectively. She has published more than 100 technical research papers in various international journals, conference proceedings, and books. She has also served as a program committee member in numerous international conferences including VLDB, IEEE ICDE, SIGKDD, PAKDD, and DASFAA. Dr. Hsu is the principal investigator of a number of government-funded research projects. Her research interests include: knowledge discovery in databases with emphasis on data mining algorithms in relational databases, XML databases, image databases, and spatio-temporal databases.
Mong Li Lee is an associate professor and assistant dean (Undergraduate Studies) in the School of Computing at the National University of Singapore. She received her PhD, MSc, and BSc (Hons 1) degrees in computer science from the National University of Singapore in 1999, 1992, and 1989, respectively. Her research interests include data cleaning, data mining, data integration of heterogeneous and semistructured data, and performance database issues in dynamic environments. She has published more than 100 technical research papers in international journals and conferences such as ACM SIGMOD, VLDB, ICDE, EDBT, and ACM SIGKDD. She has also served as a program committee member in various international conferences and is the editor of a number of books.
Junmei Wang is currently a research engineer at Siemens, Singapore. She graduated from the Department of Computer Science, School of Computing, National University of Singapore (NUS) in 2005 with a PhD in computer science. Dr. Wang received her BEng and MEng in electrical engineering from Xi’an Jiaotong University, China, in 1998 and 2000, respectively. Her research interests are focused on knowledge discovery in spatio-temporal databases.

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