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Keyword search in information retrieval and relational database system: Two class view

机译:信息检索与关系数据库系统中的关键词搜索:二级视图

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Information Retrieval and web search domain revolves around search and retrieve Methodologies. keyword search has been most popular and easy to used technique. Popular search Engine like Google Bing at core have this methodology in operation. Keywords are terms Extracted from document or single sentence generating sense when clustered in context. Same Keywords might be present in different document but structure and position of keywords build different meaning, this highlights keyword importance. Keywords are daily used atomic terms which when used in two or more group to represent information form phrases or short sentences and require concept generated by keywords. Success of keyword technology is simple match of documents consisting Question Keyword. Even though irrelevant results are retrieved due to word sense Ambiguity (WSD) necessitate concept extraction, web information is multiplying in petabytes and daily 20 petabytes of information is processed by Google still core technique of search remains on keyword as best even today. A lot effort has been taken to extend this keyword search patterns to relational database systems from last four decades. A large volume of information is stored in data which is as large as size of static web. Extending keyword search to relational data base systems would eliminate requirement of specialized data processing and manipulation language like sql. Database would be handled by nontechnical person simply with assistance of word. Relational database system are operated with precise query retrieving all matched records restricted query dimension on other hand IR System rank and present information documents containing cluster and focusing on user experience and precision. Extending keyword pattern search on relational database management system also requires extending Index, ranking, clustering to database management system with specially constructed components performing above task. This research work is dedicated to keyword search and gives two viewpoints of its application in IR and database system. Article present prototype of Machine A and B, where A presents Innovative IR system and B presents Discover approach relational database management system. Article focus more on extending keyword search to database management system as it less addressed topic and more challenging. Analysis of Machine B show that performance evaluation need to address with effective evaluation like query workload memory utilization for flexible and scalable optimized machine development. Rather than evaluation parameters like time delay etc. hybrid scalable document retrieval system is build and evaluated on memory utilization and search space is reduced by half with two layer algorithm. Further scope of system is developing hybridization at machine level and working with images as input query.
机译:信息检索和网络搜索领域围绕搜索和检索方法论。关键字搜索是最流行且易于使用的技术。诸如Google Bing之类的流行搜索引擎在核心上具有这种方法。关键字是从上下文中聚类时从文档或单句产生意义中提取的术语。相同的关键字可能出现在不同的文档中,但是关键字的结构和位置具有不同的含义,这突出了关键字的重要性。关键字是日常使用的原子术语,当在两个或多个组中使用时,它们表示信息形式的短语或简短的句子,并要求由关键字生成概念。关键字技术的成功在于组成“问题关键字”的文档的简单匹配。尽管由于词义歧义(WSD)要求提取概念而获得了不相关的结果,但Web信息却以PB的形式成倍增长,每天Google会处理20 PB的信息,即使在今天,搜索关键词的核心技术仍然是最好的。从过去的四十年开始,已经付出了很大的努力来将该关键字搜索模式扩展到关系数据库系统。大量信息存储在与静态Web大小一样大的数据中。将关键字搜索扩展到关系数据库系统将消除对专用数据处理和操作语言(如sql)的需求。数据库将由非技术人员简单地借助单词来处理。关系数据库系统通过精确查询来操作,而另一方面检索所有匹配记录的受限查询维度IR System排名并提供包含集群的信息文档,并着重于用户体验和精度。在关系数据库管理系统上扩展关键字模式搜索还需要将索引,排名,聚类扩展到具有专门执行上述任务的组件的数据库管理系统。这项研究工作致力于关键字搜索,并给出了其在IR和数据库系统中的应用的两个观点。本文介绍了机器A和B的原型,其中A提出了创新的IR系统,B提出了Discover方法关系数据库管理系统。文章着重于将关键词搜索扩展到数据库管理系统,因为它解决的主题较少且更具挑战性。机器B的分析表明,性能评估需要通过有效的评估来解决,例如查询工作负载内存利用率,以实现灵活,可扩展的优化机器开发。构建混合可伸缩文档检索系统并对其进行评估,而不是使用诸如时延等评估参数,并通过两层算法将搜索空间减少了一半。系统的进一步范围是在机器级别开发杂交,并使用图像作为输入查询。

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