The goal of this project is to develop techniques and scalable infrastructure that cater to the diverse needs of emerging applications, which use XML to represent their data. This project addresses two key problems in XML data management. First, it develops a framework for automatically deriving information-preserving XML-to-relational mappings, which ensures document integrity is correctly (and efficiently) maintained as the mapped data is updated. Second, it develops new statistical summary structures that concisely capture both structural and order information in a document, and algorithms that use these structures to accurately estimate the cardinality of XML queries including order-sensitive queries. To ensure this framework is scalable and able to support large volumes of dynamic data, techniques are also developed that incrementally maintain precise statistical summaries in the presence of updates to the base data.
The research results of this project will have the potential to influence commercial database systems as they strive to provide better support for XML data. Through our interdisciplinary collaborations, in particular in the area of scientific data management, this project will have immediate impact in helping improve the scientific discovery process. The project will be instrumental in establishing Information Management as a key research and education area in the School of Computing at the University of Utah. The involvement of graduate and undergraduate students in the project will provide mentoring opportunities. The PI is committed to recruiting minority students. The results of this project will be disseminated as research papers and as freely available tools at the project website: www.cs.utah.edu/~juliana/projects/NSF-IIS-0534628/
