SearchAutomaton:通过结合索引工具和技术来搜索多格式数据的机制

Hiren D. Pandya, Girish Mulchandani, T. Patalia
{"title":"SearchAutomaton:通过结合索引工具和技术来搜索多格式数据的机制","authors":"Hiren D. Pandya, Girish Mulchandani, T. Patalia","doi":"10.1145/2979779.2979846","DOIUrl":null,"url":null,"abstract":"In present days, I.T. Industry is rapidly growing there for its operational data increase significantly. Any large organization review its data effectively for performance evaluation and day to day monitoring. To provide such functionality Search Engine is required. Sometimes requirement of multi-format data retrieval is also important with compare to text retrieval. Oracle, Lucene and Terrier all having its own indexing mechanism to deal with such kind of retrieval. All these have its own limitations too. By combining oracle and Lucene or terrier indexing mechanism formulation of multi-format searching mechanism can be built and these is the main gist of these research. In SearchAutomaton master index is built by combining Oracle 11g index data and Terrier index data. With the help of Top-k algorithm and modified ranking model different indexing structures were analyzed as a master index entries. Effective retrieval is being possible through trained ranking model which give priority ranking for database item and file system item.","PeriodicalId":298730,"journal":{"name":"Proceedings of the International Conference on Advances in Information Communication Technology & Computing","volume":"161 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"SearchAutomaton: Searching mechanism for multi-format data by combining indexing tools and techniques\",\"authors\":\"Hiren D. Pandya, Girish Mulchandani, T. Patalia\",\"doi\":\"10.1145/2979779.2979846\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In present days, I.T. Industry is rapidly growing there for its operational data increase significantly. Any large organization review its data effectively for performance evaluation and day to day monitoring. To provide such functionality Search Engine is required. Sometimes requirement of multi-format data retrieval is also important with compare to text retrieval. Oracle, Lucene and Terrier all having its own indexing mechanism to deal with such kind of retrieval. All these have its own limitations too. By combining oracle and Lucene or terrier indexing mechanism formulation of multi-format searching mechanism can be built and these is the main gist of these research. In SearchAutomaton master index is built by combining Oracle 11g index data and Terrier index data. With the help of Top-k algorithm and modified ranking model different indexing structures were analyzed as a master index entries. Effective retrieval is being possible through trained ranking model which give priority ranking for database item and file system item.\",\"PeriodicalId\":298730,\"journal\":{\"name\":\"Proceedings of the International Conference on Advances in Information Communication Technology & Computing\",\"volume\":\"161 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the International Conference on Advances in Information Communication Technology & Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2979779.2979846\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the International Conference on Advances in Information Communication Technology & Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2979779.2979846","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

如今,it行业正在迅速发展,其运营数据显着增加。任何大型组织都会有效地审查其数据,以进行绩效评估和日常监控。要提供这样的功能,搜索引擎是必需的。与文本检索相比,有时对多格式数据检索的要求也很重要。Oracle、Lucene和Terrier都有自己的索引机制来处理这种检索。所有这些都有其自身的局限性。结合oracle和Lucene或terrier的索引机制,可以构建多格式搜索机制,这是本研究的要点。在SearchAutomaton中,主索引由Oracle 11g索引数据和Terrier索引数据组合而成。利用Top-k算法和改进的排序模型,分析了不同索引结构的主索引条目。通过训练后的排序模型对数据库项和文件系统项进行优先级排序,实现了有效的检索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SearchAutomaton: Searching mechanism for multi-format data by combining indexing tools and techniques
In present days, I.T. Industry is rapidly growing there for its operational data increase significantly. Any large organization review its data effectively for performance evaluation and day to day monitoring. To provide such functionality Search Engine is required. Sometimes requirement of multi-format data retrieval is also important with compare to text retrieval. Oracle, Lucene and Terrier all having its own indexing mechanism to deal with such kind of retrieval. All these have its own limitations too. By combining oracle and Lucene or terrier indexing mechanism formulation of multi-format searching mechanism can be built and these is the main gist of these research. In SearchAutomaton master index is built by combining Oracle 11g index data and Terrier index data. With the help of Top-k algorithm and modified ranking model different indexing structures were analyzed as a master index entries. Effective retrieval is being possible through trained ranking model which give priority ranking for database item and file system item.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信