一种基于局部敏感哈希的智能数据搜索算法

Haiying Shen, F. Ching, Ting Li, Ze Li
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引用次数: 3

摘要

在信息飞速增长的今天,对于一个信息量巨大的数据库来说,高效的信息搜索变得越来越重要。局部敏感散列(LSH)是搜索相似记录的有效方法。本文分析了LSH在海量数据库中的优缺点以及Smith-Waterman算法。揭示了LSH和Smith-Waterman算法在数据库搜索和查询领域的优势。更重要的是,本文提出了一种名为LSH- smith waterman的智能搜索算法,该算法将LSH和Smith-Waterman算法智能地结合在一起,充分发挥两者的优势和潜力。仿真结果表明,LSH- smith - waterman算法在信息搜索方面优于LSH算法。它大大减少了内存和时间消耗,并执行准确的搜索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An intelligent Locally Sensitive Hashing based algorithm for data searching
The rapid growth of information nowadays makes efficient information searching increasingly important for a massive database with tremendous volume of information. locally sensitive hashing (LSH) is an efficient method for searching similar records. This paper analyzes the strengths and weaknesses of LSH in a massive database and Smith-Waterman algorithm. It reveals the strengths of LSH and Smith-Waterman algorithm in the field of database searching and querying. More importantly, this paper presents an intelligent searching algorithm called LSH-SmithWaterman that intelligently integrates LSH and Smith-Waterman algorithm to utilize their strengths and exploit their fullest capacities. Simulation results show the superiority of LSH-Smith-Waterman algorithm compared to LSH in information searching. It dramatically reduces the memory and time consumption and performs accurate searching.
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