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Compact and Efficient Permutations for Proximity Searching

机译：紧凑高效的置换搜索

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摘要

Proximity searching consists in retrieving the most similar objects to a given query. This kind of searching is a basic tool in many fields of artificial intelligence, because it can be used as a search engine to solve problems like kNN searching. A common technique to solve proximity queries is to use an index. In this paper, we show a variant of the permutation based index, which, in his original version, has a great predicting power about which are the objects worth to compare with the query (avoiding the exhaustive comparison). We have noted that when two permutants are close, they can produce small differences in the order in which objects are revised, which could be responsible of finding the true answer or missing it. In this paper we pretend to mitigate this effect. As a matter of fact, our technique allows us both to reduce the index size and to improve the query cost up to 30%.
机译：邻近搜索在于检索与给定查询最相似的对象。这种搜索是人工智能许多领域的基本工具，因为它可以用作搜索引擎来解决诸如kNN搜索之类的问题。解决邻近查询的一种常用技术是使用索引。在本文中，我们展示了基于置换的索引的变体，在他的原始版本中，该变体具有关于哪些对象值得与查询进行比较的强大预测能力（避免进行详尽的比较）。我们已经注意到，当两个置换接近时，它们可以在修改对象的顺序上产生很小的差异，这可能是寻找真实答案或错过真实答案的原因。在本文中，我们假装减轻了这种影响。实际上，我们的技术使我们既可以减小索引大小，又可以将查询成本提高多达30％。

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