Abstract
High-dimensional indexing has been very popularly used for performing similarity search over various data types such as multimedia (audio/image/video) databases, document collections, time-series data, sensor data and scientific databases. Because of the curse of dimensionality, it is already known that well-known data structures like kd-tree, R-tree, and M-tree suffer in their performance over high-dimensional data space which is inferior to a brute-force approach linear scan. In this paper, we focus on an approximate nearest neighbor search for two different types of queries: r-Range search and k-NN search. Adapting a novel concept of a ring structure, we define a new index structure MLR-Index (Multi-Layer Ring-based Index) in a metric space and propose time and space efficient algorithms with high accuracy. Evaluations through comprehensive experiments comparing with the best-known high-dimensional indexing method LSH show that our approach is faster for a similar accuracy, and shows higher accuracy for a similar response time than LSH.
The work was supported in part by the U.S. NSF grants CNS-0520182, IIS-0513678, IIS-0842769, CAREER CNS-0448246, ITR CMS-0427089 and Air Force Office of Scientific Research MURI award FA9550-08-1-0265. Any opinions, findings, and conclusions expressed here are those of the authors and do not necessarily reflect the views of the funding agencies.
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Malik, R. et al. (2009). MLR-Index: An Index Structure for Fast and Scalable Similarity Search in High Dimensions. In: Winslett, M. (eds) Scientific and Statistical Database Management. SSDBM 2009. Lecture Notes in Computer Science, vol 5566. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02279-1_12
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DOI: https://doi.org/10.1007/978-3-642-02279-1_12
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