关系数据库中的关键词查询结果动态优化

关键词查询可以帮助用户从数据库中快速获取感兴趣的内容，它不需要用户掌握专业的数据库结构化查询语言，降低了使用门槛.针对基于关键词的数据库查询，基于数据图的方法是一种比较常见的方法，它把数据库转换成数据图，然后从数据图中计算最小Steiner树.但是，已有的方法无法根据不断变化的用户查询兴趣而动态优化查询结果.提出采用蚁群优化算法解决数据库中的关键词查询问题，并提出了基于概念漂移理论的用户查询兴趣突变探查方法，可以及时发现用户兴趣的突变.在此基础上，提出了基于概念漂移理论和蚁群优化算法的查询结果动态优化算法ACOKS^*，可以根据突变的用户兴趣，动态地优化查询结果，使其更加符合用户查询预期.在原型系统上得到的大量实验结果表明，该方法具有很好的可扩展性，并且可以比已有的方法取得更好的性能.

Dynamic Result Optimization for Keyword Search over Relational Databases

Keyword search helps users to efficiently get interested information from relational databases, and users are exempted from learning the professional structural query language for relational databases, which greatly reduces the usabilitye threshold. Keyword search over relational databases commonly employs data-graph-based methods which first models a database into a graph and then uses it to identify the minimum Steiner tree. However, the available methods are not able to dynamically optimize query results according to the dynamically changing user interest. In this paper, an ant-colony-optimization-based algorithm is proposed to achieve the task of keyword search over relational databases. Furthermore, a novel approach based on the theory of concept drift is presented to capture the mutation of user interest. In addition, based on concept drift theory and ant colony optimization algorithm, a new algorithm called ACOKS* is proposed to dynamically optimize the search results according to the time-changing user interest, so as to achieve the results in more accordance with user interest. Finally, a prototype is developed to carry out extensive experiments, and the results show that our method can achieve high scalability and perform better than other state-of-the-art methods.