We live in an age of rapid technological development. The Internet already affects our lives in many ways. Indeed, we continue to depend more, and more intrinsically, on the Internet, which is increasingly becoming a fundamental piece of societal infrastructure, just as water supply, electricity grids, and transportation networks have been for a long time. But while these other infrastructures are relatively static, the Internet is undergoing swift and fundamental change: Notably, the Internet is going mobile. The world has some 6.7 billion humans, 4 billion mobile phones, and 1.7 billion Internet users. The two most populous continents, Asia and Africa, have relatively low Internet penetration and hold the greatest potentials for growth. Their mobile phone users by far outnumber their Internet users, and the numbers are growing rapidly. China and India are each gaining about half a dozen million new phone users per month. Users across the globe as a whole increasingly embrace mobile Internet devices, with smart phone sales are starting to outnumber PC sales. Indeed, these and other facts suggest that the Internet stands to gain a substantial mobile component. This mega trend towards “mobile” is enabled by rapid and continuing advances in key technology areas such as mobile communication, consumer electronics, g- positioning, and computing. In short, this is the backdrop for this very timely book on moving objects by Xiaofeng Meng and Jidong Chen.

Applications, 2nd Edition focuses on moving object management, from the location management perspective to determining how constantly changing locations affect the traditional database and data mining technology. The book specifically describes the topics of moving objects modeling and location tracking, indexing and querying, clustering, location uncertainty, traffic-aware navigation and privacy issues, as well as the application to intelligent transportation systems. Through the book, the readers will be made familiar with the cutting-edge technologies in moving object management that can be effectively applied in LBS and transportation contexts. The second edition of this book significantly expands the coverage of the latest research on location privacy, traffic-aware navigation and uncertainty. The book has also been reorganized, with nearly all chapters rewritten, and several new chapters have been added to address the latest topics on moving objects management. Xiaofeng Meng is a professor at the School of Information, Renmin University of China; Zhiming Ding is a professor at the Institute of Software, Chinese Academy of Sciences (ISCAS); Jiajie Xu is an assistant professor at the ISCAS.

The continued advances in wireless communication and positioning technologies such as GPS have made new data management applications possible, such as location-based services (LBS) that store and manage the continuously changing positions of moving objects. "Moving Objects Management - Models, Techniques and Applications" focuses on moving objects management, from the location management perspective to the exploration of how the continually changing locations affect the traditional database and data mining technology. Specifically, the book describes the topics of moving objects modeling and location updating, indexing and querying, clustering, location uncertainty and privacy issues, as well as their application to intelligent transportation systems. This book is intended for developers of database management systems and mobile applications, research scientists and advanced-level students in computer science and geography. Dr. Xiaofeng Meng is a professor at the School of Information, Renmin University of China; Dr. Jidong Chen is a senior research scientist at EMC Research China, one of the research groups of the EMC Corporation.

Moving Objects Databases is the first uniform treatment of moving objects databases, the technology that supports GPS and RFID. It focuses on the modeling and design of data from moving objects — such as people, animals, vehicles, hurricanes, forest fires, oil spills, armies, or other objects — as well as the storage, retrieval, and querying of that very voluminous data. It includes homework assignments at the end of each chapter, exercises throughout the text that students can complete as they read, and a solutions manual in the back of the book. This book is intended for graduate or advanced undergraduate students. It is also recommended for computer scientists and database systems engineers and programmers in government, industry and academia; professionals from other disciplines, e.g., geography, geology, soil science, hydrology, urban and regional planning, mobile computing, bioterrorism and homeland security, etc. Focuses on the modeling and design of data from moving objects--such as people, animals, vehicles, hurricanes, forest fires, oil spills, armies, or other objects--as well as the storage, retrieval, and querying of that very voluminous data. Demonstrates through many practical examples and illustrations how new concepts and techniques are used to integrate time and space in database applications. Provides exercises and solutions in each chapter to enable the reader to explore recent research results in practice.

In spatio-temporal systems, precise location data is desirable but often not available due to obfuscation, privacy, hardware inaccuracies, and other factors. Progress has been made in research which deals with the uncertainty of moving objects' location data. However, much of the existing work does not always consider factors such as constraints imposed by the topology of road networks, and harmonic integration between past movements, current, and prospective imprecise positions. In this thesis, we propose an approach that utilizes time, distance, and connectivity constraints of a road network to infer a moving object's past, present, and future locations more precisely when its exact location data is not available. The experimental results using real GPS trajectories confirm the efficiency of our proposed solution for reducing uncertainty and inferring historical, and future locations.