Research On Flexible Query Results Automated Ranking Over Web Database

The rapid expansion of the Internet has made a variety of online databases accessible to a large number of users. Users can access the database through the query interfaces provided by the Web site and the database available on the website is called Web database. However, for most ordinary users, they often have insufficient knowledge about the structure and contents of database, and often have vague or imprecise ideas when searching the Web databases, so they may not be able to formulate queries that accurately express their query intentions. Therefore, the query user submits should not act as rigid constraints for the query results, i.e. the query is a flexible query. However, the flexible query can result in too many answers over large database, and the users are only interested in the results which meet their intention most closely. So it is important to rank the query results of the flexible queries.This article proposes an automated ranking approach for the flexible answers for database queries. Our solution is domain independent and leverages data and workload statistics and correlations, and so it can return the ranked flexible query results independent of users and domains. The ranking function this article proposes measures the importance of the tuple by regarding its satisfaction degree of specified attribute values to the query, and its relevance degree of unspecified attribute values to the user's preferences. The satisfaction degree of specified attribute values is determined by the attribute weights of the specified attributes, the user's preferences to the specified attributes and the similarity between the values of the tuple and the specified values. Firstly, the attribute weights are assigned according to the data distribution of the database and then the user's preferences to the specified attributes are given according to the workload and the queries. Secondly, to evaluate the user's preference to the unspecified attributes, we look beyond the attributes specified in the query and find the association degrees between the specified attributes and the unspecified attributes by adapting and applying principles of probabilistic models from information retrieval for structured data. And finally, we rank the query results according to the satisfaction degree of the specified attributes and the association degree of the unspecified attributes.We evaluate the quality of our approach with an experiment on a real database, and experimental results demonstrate that the approach for the many flexible answers problem for the web database proposed in the article can capture the user's preferences effectively and have a high ranking quality as well...