Optimal Multi-Step k-Nearest Neighbor Search

For an increasing number of modern database applications, efficient support of similarity search becomes an important task. Along with the complexity of the objects such as images, molecules and mechanical parts, also the complexity of the similarity models increases more and more. Whereas algorithms that are directly based on indexes work well for simple medium-dimensional similarity distance functions, they do not meet the efficiency requirements of complex high-dimensional and adaptable distance functions. The use of a multi-step query processing strategy is recommended in these cases, and our investigations substantiate that the number of candidates which are produced in the filter step and exactly evaluated in the refinement step is a fundamental efficiency parameter. After revealing the strong performance shortcomings of the state-of-the-art algorithm for k-nearest neighbor search [Korn et al. 1996], we present a novel multi-step algorithm which is guaranteed to produce the mini...

• Type: Conference
• Year: 1998
• Where: SIGMOD
• Authors: Thomas Seidl, Hans-Peter Kriegel
• Keywords: Adaptable Distance Functions, Database, Multi-step Query Processing, SIGMOD 1998, Similarity Distance Functions