Approximate Computation of Distance-Based Queries

In spatial database applications the similarity or dissimilarity of complex objects is examined by performing distance-based queries (DBQs) on data of high dimensionality (a generalization of spatial data). The R-tree and its variations are commonly cited as multidimensional access methods that can be used for answering such queries. Although the related algorithms work well for low-dimensional data spaces, their performance degrades as the number of dimensions increases (dimensionality curse). To obtain acceptable response time in high-dimensional data spaces, algorithms that obtain approximate solutions can be used. In this chapter, we review the most important approximation techniques for reporting sufficiently good results quickly. We focus on the design choices of efficient approximate DBQ algorithms that minimize the response time and the number of I/O operations over tree-like structures. The chapter concludes with possible future research trends in the approximate computation of DBQs.