This book is an introduction and source book for practitioners, graduate s- dents, and researchers interested in the state of the art and practice in spatiot- poral databases. It collects the most important and representative research c- ried out in the project CHOROCHRONOS and presents it in a uni?ed fashion. CHOROCHRONOS was a Training and Mobility Research Network funded by the European Commission with the objective to study the design, implemen- tion, and application of spatiotemporal database management systems. This book would never have been possible if it was not for the devoted work of many people. First and foremost, we would like to thank the authors of the nine chapters of this book for their hard work. We would also like to acknowledge the help of Christiane Bernard, our o?cer from the European Commission, who saw the project to its conclusion, working as hard as we did to make it a thorough success. The constructive comments and feedback of our reviewer Colette Roland (University of Paris-1) are also very much appreciated. Last, but not least, we would like to thank all the students and postdoctoral fellows who were trained during CHOROCHRONOS. We hope the time they spent at CHOROCHRONOS node institutions was rewarding and lots of fun! March 2003 Timos Sellis Manolis Koubarakis Andrew Frank, Vienna St ́ ephane Grumbach Ralf Hartmut Guting ̈ Christian Jensen Nikos Lorentzos Yannis Manolopoulos Enrico Nardelli Barbara Pernici Babis Theodoulidis Nectaria Tryfona Hans-J ̈ org Schek Michel Scholl Table of Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Spatio-Temporal Databases explores recent trends in flexible querying and reasoning about time- and space-related information in databases. It shows how flexible querying enhances standard querying expressiveness in many different ways, with the aim of facilitating extraction of relevant data and information. Flexible spatial and temporal reasoning denotes qualitative reasoning about dynamic changes in the spatial domain, characterized by imprecision or uncertainty (or both). Many of the contributions focus on GIS, while some others are more general, or focus on related application fields, presenting theoretical viewpoints and techniques that are inspiring or can be adapted for GIS. The first part bundles the contributions on advances at the theoretical level, also discussing examples and opening further perspectives. The second part presents contributions on well-developed applications. The authors explain how to handle imprecision and uncertainty, demonstrating how advanced techniques can help to solve diverse problems related to GIS.

This brief presents several new query processing techniques, called complex motion pattern queries, specifically designed for very large spatio-temporal databases of moving objects. The brief begins with the definition of flexible pattern queries, which are powerful because of the integration of variables and motion patterns. This is followed by a summary of the expressive power of patterns and flexibility of pattern queries. The brief then present the Spatio-Temporal Pattern System (STPS) and density-based pattern queries. STPS databases contain millions of records with information about mobile phone calls and are designed around cellular towers and places of interest. Density-based pattern queries capture the aggregate behavior of trajectories as groups. Several evaluation algorithms are presented for finding groups of trajectories that move together in space and time, i.e. within a predefined distance to each other. Finally, the brief describes a generic framework, called DivDB, for diversifying query results. Two new evaluation methods, as well as several existing ones, are described and tested in the proposed DivDB framework. The efficiency and effectiveness of all the proposed complex motion pattern queries are demonstrated through an extensive experimental evaluation using real and synthetic spatio-temporal databases. This clear evaluation of new query processing techniques makes Spatio-Temporal Database a valuable resource for professionals and researchers studying databases, data mining, and pattern recognition.

This book constitutes the refereed proceedings of the 6th International Symposium on Spatial Databases, SSD'99, held in Hong Kong, China in July 1999. The 17 revised full papers presented were carefully selected from 55 submissions. Also included are short papers corresponding to three invited talks and industrial applications presentations. The papers are organized in chapters on multi-resolution and scale, indexing, moving objects and spatio-temporal data, spatial mining and classification, spatial join, uncertainty and geological hypermaps, and industrial and visionary application track.

"This book presents probable solutions when discovering the spatial sequence patterns by incorporating the information into the sequence of patterns, and introduces new classes of spatial sequence patterns, called flow and generalized spatio-temporal patterns, addressing different scenarios in spatio-temporal data by modeling them as graphs, providing a comprehensive synopsis on two successful partition-based algorithms designed by the authors"--Provided by publisher.

This book constitutes the refereed proceedings of the International Workshop on Spatio-Temporal Database Management Systems, STDBM'99, held in Edinburgh, UK, in September 1999 as a satelite event of VLDB'99. The 13 revised full papers presented were carefully selected from 30 papers submitted. The book offers topical sections on understanding and manipulating spatio-temporal data; integration, exchange, and visualization; query processing; index evaluation; and constraints and dependencies.

This volume contains updated versions of the ten papers presented at the First International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining (TSDM 2000) held in conjunction with the 4th European Conference on Prin- ples and Practice of Knowledge Discovery in Databases (PKDD 2000) in Lyons, France in September, 2000. The aim of the workshop was to bring together experts in the analysis of temporal and spatial data mining and knowledge discovery in temporal, spatial or spatio-temporal database systems as well as knowledge engineers and domain experts from allied disciplines. The workshop focused on research and practice of knowledge discovery from datasets containing explicit or implicit temporal, spatial or spatio-temporal information. The ten original papers in this volume represent those accepted by peer review following an international call for papers. All papers submitted were refereed by an international team of data mining researchers listed below. We would like to thank the team for their expert and useful help with this process. Following the workshop, authors were invited to amend their papers to enable the feedback received from the conference to be included in the ?nal papers appearing in this volume. A workshop report was compiled by Kathleen Hornsby which also discusses the panel session that was held.

This book constitutes the refereed proceedings of the 15th International Symposium on Spatial and Temporal Databases, SSTD 2017, held in Arlington, VA, USA, in August 2017.The 19 full papers presented together with 8 demo papers and 5 vision papers were carefully reviewed and selected from 90 submissions. The papers are organized around the current research on concepts, tools, and techniques related to spatial and temporal databases.

This SpringerBrief presents the fundamental concepts of a specialized class of data stream, spatio-temporal data streams, and demonstrates their distributed processing using Big Data frameworks and platforms. It explores a consistent framework which facilitates a thorough understanding of all different facets of the technology, from basic definitions to state-of-the-art techniques. Key topics include spatio-temporal continuous queries, distributed stream processing, SQL-like language embedding, and trajectory stream clustering. Over the course of the book, the reader will become familiar with spatio-temporal data streams management and data flow processing, which enables the analysis of huge volumes of location-aware continuous data streams. Applications range from mobile object tracking and real-time intelligent transportation systems to traffic monitoring and complex event processing. Spatio-Temporal Data Streams is a valuable resource for researchers studying spatio-temporal data streams and Big Data analytics, as well as data engineers and data scientists solving data management and analytics problems associated with this class of data.