Continuously Changing Data in Spatiotemporal Databases

Applications that deal with large sets of Moving Objects (MOs) continue to grow, and so does the demand for efficient data management and query processing systems supporting MOs. Major challenges for developing such systems are due to the fact that the actual moving object reports its state that can continuously change over time, however, it is only possible to discretely record the object s states. All the missing or non-recorded states collectively form the uncertainty of the object s history. This book reviews existing uncertainty models and proposes a more efficient model called the Tornado model that reduces the size of uncertainty regions resulting in less false hits, thus improving the efficiency of the database management system. For indexing purpose, we propose Minimum Bounding Rectangle approximations for the uncertainty models. Finally, this book presents the Truncated Tornado in Tilted Minimum Bounding Boxes an uncertainty model as a significant advance in minimizing uncertainty regions associated with MOs. This work should be especially useful to professionals in Spatiotemporal Databases and Uncertainty Management fields.

Autorentext

Dr. Shayma Alkobaisi is an assistant professor at the College of Information Technology, United Arab Emirates University. Dr. Wan D. Bae is an assistant professor of computer science at the University of Wisconsin-Stout. Their primary research interests include spatial and spatiotemporal databases, GIS, and multidimensional data analysis.