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Anytime k-nearest neighbor search for database applications
<span title="">2008</span>
<i title="IEEE">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vuw5ktdyknehrehttg5qm4rfqm" style="color: black;">2008 IEEE 24th International Conference on Data Engineering Workshop</a>
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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
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icdew.2008.4498354">doi:10.1109/icdew.2008.4498354</a>
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... 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
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