ABSTRACT
The representation of multidimensional data is an important issue in applications in diverse fields that include database management systems (see Chapter 62), computer graphics (see Chapter 55), computer vision, computational geometry (see Chapters 65, 66 and 67), image processing (see Chapter 58), geographic information systems (GIS) (see Chapter 56), pattern recognition, VLSI design (Chapter 53), and others. The most common definition of multidimensional data is a collection of points in a higher dimensional space. These points can represent locations and objects in space as well as more general records where only some, or even none, of the attributes are locational. As an example of nonlocational point data, consider an employee record which has attributes corresponding to the employee's name, address, sex, age, height, weight, and social security number. Such records arise in database management systems and can be treated as points in, for this example, a seven-dimensional space (i.e., there is one dimension for each attribute) albeit the different dimensions have different type units (i.e., name and address are strings of characters, sex is binary; while age, height, weight, and social security number are numbers).
