RETRIEVING DEEP WEB DATA THROUGH MULTI-ATTRIBUTES INTERFACES WITH STRUCTURED QUERIES
A great deal of data on the Web lies in the hidden databases, or the deep Web. Most of the deep Web data is not directly available and can only be accessed through the query interfaces. Current research on deep Web search has focused on crawling the deep Web data via Web interfaces with keywords queries. However, these keywords-based methods have inherent limitations because of the multi-attributes and top-k features of the deep Web. In this paper we propose a novel approach for siphoning structured data with structured queries. Firstly, in order to retrieve all the data non-repeatedly in hidden databases, we model the hidden database as a hierarchy tree. Under this theoretical framework, data retrieving is transformed into the traversing problem in a tree. We also propose techniques to narrow the query space by using heuristic rule, based on mutual information, to guide the traversal process. We conduct extensive experiments over real deep Web sites and controlled databases to illustrate the coverage and efficiency of our techniques.
multi-attribute interfaces hidden databases data retrieval top-k tuples