{"title":"An intelligent Locally Sensitive Hashing based algorithm for data searching","authors":"Haiying Shen, F. Ching, Ting Li, Ze Li","doi":"10.1109/SECON.2008.4494284","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":188817,"journal":{"name":"IEEE SoutheastCon 2008","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE SoutheastCon 2008","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SECON.2008.4494284","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
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.
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。