@inproceedings{de566a0f15054460bdf47fe4320476c1,
title = "An approximate oracle for distance in metric spaces",
abstract = "In this paper we present a new data structure for estimating distances in a pseudo-metric space. Given are a database of objects and a distance function for the objects, which is a pseudo-metric. We map the objects to vectors in a pseudo-Euclidean space with a reasonably low dimension while preserving the distance between two objects approximately. Such a data structure can be used as an approximate oracle to process a broad class of pattern-matching based queries. Experimental results on both synthetic and real data show the good performance of the oracle in distance estimation.",
author = "Yanling Yang and Kaizhong Zhang and Xiong Wang and Wang, {Jason T.L.} and Dennis Shasha",
note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 9th Annual Symposium on Combinatorial Pattern Matching, CPM 1998 ; Conference date: 20-07-1998 Through 22-07-1998",
year = "1998",
doi = "10.1007/bfb0030784",
language = "English (US)",
isbn = "3540647392",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "104--117",
booktitle = "Combinatorial Pattern Matching - 9th Annual Symposium, CPM 1998, Proceedings",
address = "Germany",
}