Welcome to the InfoSci Platform
Soft Computing Techniques in Spatial Databases

Soft Computing Techniques in Spatial Databases

Markus Schneider
Copyright: © 2010 |Pages: 23
ISBN13: 9781605668147|ISBN10: 1605668141|ISBN13 Softcover: 9781616923068|EISBN13: 9781605668154
DOI: 10.4018/978-1-60566-814-7.ch004
Cite Chapter Cite Chapter

MLA

Schneider, Markus. "Soft Computing Techniques in Spatial Databases." Soft Computing Applications for Database Technologies: Techniques and Issues, edited by K. Anbumani and R. Nedunchezhian, IGI Global, 2010, pp. 49-71. https://doi.org/10.4018/978-1-60566-814-7.ch004

APA

Schneider, M. (2010). Soft Computing Techniques in Spatial Databases. In K. Anbumani & R. Nedunchezhian (Eds.), Soft Computing Applications for Database Technologies: Techniques and Issues (pp. 49-71). IGI Global. https://doi.org/10.4018/978-1-60566-814-7.ch004

Chicago

Schneider, Markus. "Soft Computing Techniques in Spatial Databases." In Soft Computing Applications for Database Technologies: Techniques and Issues, edited by K. Anbumani and R. Nedunchezhian, 49-71. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-814-7.ch004

Export Reference

Mendeley
Favorite

Abstract

Spatial database systems and geographical information systems are currently only able to support geographical applications that deal with only crisp spatial objects, that is, objects whose extent, shape, and boundary are precisely determined. Examples are land parcels, school districts, and state territories. However, many new, emerging applications are interested in modeling and processing geographic data that are inherently characterized by spatial vagueness or spatial indeterminacy. Examples are air polluted areas, temperature zones, and lakes. These applications require novel concepts due to the lack of adequate approaches and systems. In this chapter, the authors show how soft computing techniques can provide a solution to this problem. They give an overview of two type systems or algebras that can be integrated into database systems and utilized for the modeling and handling of spatial vagueness. The first type system, called Vague Spatial Algebra (VASA), is based on well known, general, and exact models of crisp spatial data types and introduces vague points, vague lines, and vague regions. This enables an exact definition of the vague spatial data model since we can build it upon an already existing theory of spatial data types. The second type system, called Fuzzy Spatial Algebra (FUSA), leverages fuzzy set theory and fuzzy topology and introduces novel fuzzy spatial data types for fuzzy points, fuzzy lines, and fuzzy regions. This enables an even more fine-grained modeling of spatial objects that do not have sharp boundaries and interiors or whose boundaries and interiors cannot be precisely determined. This chapter provides a formal definition of the structure and semantics of both type systems. Further, the authors introduce spatial set operations for both algebras and obtain vague and fuzzy versions of geometric intersection, union, and difference. Finally, they describe how these data types can be embedded into extensible databases and show some example queries.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.