Spatial data management has been an area of intensive research for more than two decades. The complex nature of data required by many applications that we use today—such as geographic information systems (GIS), computer-aided design/computer-aided manufacturing (CAD/CAM), multimedia applications, and meteorological and weather information systems—has compelled database vendors to develop spatial data management systems. Spatial data management systems are designed to make the storage, retrieval, and manipulation of spatial data easier and more natural to users of applications like GIS, CAD/CAM, and weather forecasting. The popularity and use of these systems is growing and will continue to grow in the coming years.

To achieve their true potential, spatial data management systems need an efficient query processing mechanism. One of the most influential access methods is the R-tree structure. The demanding modern data-intensive applications and their complex data manipulation requirements have forced the adoption of R-trees by the database industry.

This book consolidates and presents the research done in this field in the past two decades, gives an overview of R-trees and their variants, familiarizes users with the fundamental concepts of R-trees, and goes on to explain advanced topics like query processing, modern applications, optimization, and implementation issues. The book contains nine chapters (divided into four parts), an epilogue, a reference section, and an index, in addition to the preface and list of figures and tables.

Part 1 comprises three chapters and deals with the fundamental concepts of R-trees. Chapter 1 provides an introduction, the basic notations and the corresponding descriptions, and the original R-tree access method. The second chapter describes the popular dynamic versions of R-trees. Chapter 3 explains the various static variations of R-trees.

Part 2 concentrates on query processing techniques and contains two chapters. Chapter 4 deals with the fundamental query processing techniques, like range queries, nearest-neighbor queries, and spatial join queries, and chapter 5 explores complex queries, like categorical range queries, multiway spatial joins, closest-pair queries, and incremental processing. In both chapters, the different query processing techniques are explained in detail with figures, examples, and algorithms.

Part 3 is about the use of R-trees in modern applications like spatiotemporal databases (chapter 6), multimedia databases, data warehousing, and data mining (chapter 7). Part 4 deals with query optimization and implementation issues. Chapter 8 explains the various query optimization issues for R-tree-based query processing. Chapter 9 discusses implementation issues like parallel systems, concurrency control, and issues in relational implementations.

The epilogue summarizes the issues covered in the book and gives a preview of the future trends. The references section contains an exhaustive list of books, articles, and Web sites that provide information on R-trees and spatial data management. This is a treasure trove for those who want to learn more.

The book is written in a very informal and user-friendly manner with lots of examples, illustrations, and tables. The authors have succeeded in making this book very accessible, understandable, and readable. This book will be invaluable to professionals, researchers, teachers, and students who deal with spatial data management systems and their access methods.