Grant: Discovery Grants Program - Individual

This research aims at development of advanced data management techniques, which uses and/or extends existing database theory and technology for modeling data and processing queries required in numerous emerging applications. Examples of such data includes logical data, uncertainty data, web logs, sequence data, etc. Our focus is on logical data and sequence data. We consider logical data in information integration and study query rewriting and evaluation of conjunctive queries using views, where the queries and views are logical rules possibly extended with constraints which are linear arithmetic comparisons. Literature discusses attribute comparisons and for certain classes of connstraints practical solutions are developed to find maximally contained rewritings, when exists, for queries with all but inequality comparisons. We will develop constraints resolution and optimization modules for the rewriting algoritms in our context above. This extends our results in CoopIS 2005, where we considered constraints which are linear arithmetic expressions with equality. In another research theme in this proposal, we investigate modeling and processing sequence data, which do not have any structure. Emerging applications generate and use enormous amounts of such data. We focus more on biological sequences: DNA and proteins. The main issues are the required uge storage space and lengthy process of index construction. Further, efficient support for exact match and approximate searching is central to such applications. Hence a desired solution should be space efficient, scalable, with resonable index construction time and efficient various search tecniques. While string searching is not new, the enormous size of ever-growing biological sequences, and the variety and volume of queries to be supported pose new research challenges. There is a growing need for more sophisicated indexing structures and techniques. Based on our results in CIKM 2005, the expected contributions of this work include both theoretical and practical advances in the management of biological sequence data.