We propose to mine structured query templates from search logs, for enabling rich query interpretation that recognizes both query intents and associated attributes. We formalize the notion of template as a sequence of keywords and domain attributes, and our objective is to discover templates with high precision and recall for matching queries in a domain of interest. Our solution bootstraps from small seed input knowledge to discover relevant query templates, by harnessing the wealth of information available in search logs. We model this information in a tri-partite QueST network of queries, sites, and templates. We propose a probabilistic inferencing framework based on the dual metrics of precision and recall- and we show that the dual inferencing correspond respectively to the random walks in backward and forward directions. We deployed and tested our algorithm over a real-world search log of 15 million queries. The algorithm achieved accuracy of as high as 90% (on F-measure), with little seed knowledge and even with incomplete domain schema.

In this demo, we present Dewex, an exploration facility for answering questions like T1-TA, running on repository of over 30,000 real sources. The main technical contributions of this demo are: as our system, we present a novel facility for exploring the deep Web; as our solution, we propose a schematic metadata based source modeling, and a generalized search mechanism to compute associativity in metadata graph; and in our realization, to enable online exploration, we propose to speed up computation using matrix optimization.

The current SQL standard for access control is coarse grained, in that it grants access to all rows of a table or none. Fine-grained access control, which allows control of access at the granularity of individual rows, and to specific columns within those rows, is required in practically all database applications. There are several models for fine grained access control, but the majority of them follow a view replacement strategy. There are two significant problems with most implementations of the view replacement model, namely (a) the unnecessary overhead of the access control predicates when they are redundant and (b) the potential of information leakage through channels such as user-defined functions, and operations that cause exceptions and error messages. We first propose techniques for redundancy removal. We then define when a query plan is safe with respect to UDFs and other unsafe functions, and propose techniques to generate safe query plans. We have prototyped redundancy removal and safe UDF pushdown on the Microsoft SQL Server query optimizer, and present a preliminary performance study.