• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国政府科技报告 >Content Based Retrieval Database Management System with Support for Similarity Searching and Query Refinement
【24h】

Content Based Retrieval Database Management System with Support for Similarity Searching and Query Refinement

机译：基于内容的检索数据库管理系统，支持相似性搜索和查询优化

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

With the emergence of many application domains that require imprecise similarity based access to information, techniques to support such a retrieval paradigm over database systems have emerged as a critical area of research. This thesis explores how to enhance database systems with content based search over arbitrary abstract data types in a similarity based framework with query refinement. This scope opens a number of challenges previously not faced by databases, among them: * Extension of abstract data types to support arbitrary similarity functions and support for query refinement. (Intra type similarity and feedback) * Extension of the already developed query refinement models under the MARS system to a general multi table relational model. (Inter Type similarity and feedback) * Extension of query processing models from a set based model where tuples either satisfy or not the query predicate to a result where the degree to which tuples satisfy a predicate is represented by their similarity values. (Similarity predicates) * Based on the similarity values, return only the best k matches. This implies a sorting on the similarity values and ample optimizations are possible to use lazy evaluation and only compute those answers that the user will see. (Ranked Retrieval) * Optimization of query execution under the similarity conditions which requires access to specialized indices. Optimized composite predicate merging is possible based on earlier work on the MARS project to compute the similarity value for a predicate based on independent streams rather than using the value directly. (Incremental top-k merging) We are building a prototype system that implements the proposed functionality in an efficient way and we evaluate the quality of the answers returned to the user.

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号