Anytime K-Nearest Neighbor Search for Database Applications

Weijia Xu, Daniel P. Miranker, Rui Mao, Smriti Ramakrishnan
2008 First International Workshop on Similarity Search and Applications (sisap 2008)  
Many contemporary database applications require similarity-based retrieval of complex objects where the only usable knowledge of its domain is determined by a metric distance function. In support of these applications, we explored a search strategy for knearest neighbor searches with MVP-trees that greedily identifies k answers and then improves the answer set monotonically. The algorithm returns an approximate solution when terminated early, as determined by a limiting radius or an internal
more » ... sure of progress. Given unbounded time the algorithm terminates with an exact solution. Approximate solutions to k-nearest neighbor search provide much needed speed improvement to hard nearest-neighbor problems. Our anytime approximate formulation is well suited for interactive search applications as well as applications where the distance function itself is an approximation. We evaluate the algorithm over a suite of workloads, including image retrieval, biological data and high-dimensional vector data. Experimental results demonstrate the practical applicability of our approach. First International Workshop on Similarity Search and Applications 0-7695-3101-6/08 $25.00
doi:10.1109/sisap.2008.11 dblp:conf/sisap/XuMMR08 fatcat:nxr4l7iv7bbupgj5gw6olk6xyu