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Spatial Databases: A Tourby Shashi Shekhar
Over the years it has become evident in many areas of computer applications that the functionality of database management systems has to be enlarged to include spatially referenced data. The study of spatial database management systems (SDBMS) is a step in the direction of providing models and algorithms for the efficient handling of data related to space.
Spatial databases have now been an active area of research for over two decades. Their results, example, spatial multidimensional indexing, are being used in many different areas. The principle impetus for research in SDBMs comes from the needs of existing applications such as geographical information systems (GIS) and computer-aided design (CAD), as well as potential applications such as multimedia information systems, data warehousing, and NASA's earth observation system. These spatial applications have over one million existing users.
Major players in the commercial database industry have products specifically designed to handle spatial data. These products include the spatial data engine (SDE), by Environment Systems Research Institute (ESRI); as well as spatial datablades for object-relational database servers from many vendors including Intergraph, Autodesk, Oracle, IBM and Informix. Research prototypes include Postgres, Geo2, and Paradise. The functionality provided by these systems includes a set of spatial data types such as the point, line, and polygon, and a set of spatial operations, including intersection, enclosure, and distance. An industry-wide standard set of spatial data types and operations has been developed by the Open Geographic Information Systems (OGIS) consortium. The spatial types and operations can be made a part of an object-relational query language such as SQL3. The performance enhancement provided by these systems includes a multidimensional spatial index and algorithms for spatial access methods, spatial range queries, and spatial joins.
The integration of spatial data into traditional databases amounts to resolving many nontrivial issues at various levels. They range from deep ontological questions about the modeling of space, for example, whether it should be field based or object based, thus paralleling the wave-particle duality in physics to more mundane but important issues about file management. These diverse topics make research in SDBMS truly interdisciplinary.
Let us use the example of a country dataset to highlight the special needs of spatial databases. A country has at least one nonspatial datum, its name, and one spatial datum, its boundary. There is no ambiguity about storing or representing its name, but unfortunately it is not true for its boundary. Assuming that the boundary is represented as a collection of straight lines, we need to include a spatial data type line and the companion types point and region in the database system to facilitate spatial queries on the object country. These new data types need to be manipulated and composed according to some fixed rules leading to the creation of a spatial algebra. Because spatial data is inherently visual and usually voluminous, database systems have to be augmented to provide visual query processing and special spatial indexing . Other important database issues such as concurrency control, bulk loading, storage, and security have to be revisited and fine-tuned to build an effective spatial database management system.
This book evolved from the class notes of a graduate course on Scientific Databases (Csci 8705) in University of Minnesota. Researchers and students both within and outside the Computer Science Department found the course very useful and applicable to their work. Despite the good response and high level of interest in the topic, no textbook available in the market was able to meet the interdisciplinary needs of the audience. A recent book by Scholl et al., 2001 focuses on traditional topics related to query languages and access methods while leaving out current topics such as spatial networks (e.g., road maps) and data mining for spatial patterns. A monograph by Adam and Gangopadhyay, 1997 catalogs research papers on database issues in GIS, with little reference to the industrial state-of-the-art. Another Worboys, 1995 also focuses on GIS and has only two chapters devoted to database issues. Many of these books neither use industry standards, e.g., OGIS, nor provide adequate instructional support, example, questions and problems at the end of each chapter to allow students to assess their understanding of the main concepts. Not surprisingly, our colleagues in academia working in databases, parallel computing, multimedia information, civil and mechanical engineering, and forestry have expressed a strong desire for a comprehensive text on spatial databases. Industry professionals involved in software development for GIS and CAD/CAM have also made several requests for information on spatial databases in a collected form.
As a first step toward developing this book, we completed a survey paper, "Spatial Databases: Accomplishments and Research Needs," for IEEE Transactions on Knowledge and Data Engineering (Jan. 1999). We noticed that the research literature in computer science was skewed with numerous publications on some topics (e.g., spatial indexes, spatial join algorithms) and relatively scarce publications on many other important topics (e.g., conceptual modeling of spatial data). We looked to GIS industry as well as GIS researchers outside computer science for ideas in these areas for the relevant chapters in the book.
Being on sabbatical for the academic year 1997-1998 facilitated initial work on the book. We completed a draft of many chapters by expanding on the course notes of CSci 8705. The first draft of the book was used in database courses at the University of Minnesota. Subsequently the book was revised using feedback from reviewers, colleagues, and students.
We believe the following features are unique aspects of this book:
The book is divided into eight chapters, each one an important subarea within spatial databases. We introduce the field of spatial databases in Chapter 1. In Chapter 2, we focus on spatial data models and introduce the field versus object dichotomy and its implications for database design. Chapter 3 discusses the necessary enhancements required to make traditional query languages compatible with spatial databases. We provide an extensive discussion of various proposals to extend SQL with spatial capabilities. Spatial databases deal with extraordinarily large amounts of data, and it is essential for DBMS to provide sophisticated storage, compression, and indexing methods to enhance the performance of query processing. Spatial data storage and indexing schemes are covered in Chapter 4. From query languages and indexing, we move on to query processing and optimization in Chapter 5: Here we discover that many standard techniques from traditional databases have to be abandoned or drastically modified in order to be applicable in a spatial context. We also introduce the filter-refine paradigm for spatial query processing. In Chapter 6, we show how spatial database technology is being applied to spatial networks. In this chapter we also cover network data models and query languages. Chapter 7 covers the emerging field of spatial data mining. In this chapter we expose the readers to the concept of spatial dependency that is prevalent in spatial data sets, and show how this can be modeled and incorporated into data mining process. Finally in Chapter 8, we discuss emerging trends in spatial databases.
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