Integrating heterogeneous data is a major challenge that manifests itself across a wide spectrum, from government and business to science and health care. A critical task underlying this challenge is deriving the relationship between different database schemas. During the past decade, schema mappings have emerged as the right tool for this task. Schema mappings are high-level, declarative specifications of the relationships between two database schemas that provide the appropriate level of abstraction and, at the same time, can be compiled into executable code.
The first main aim of this project is to develop a framework and tools for designing schema mappings. This framework is based on the systematic use of data examples. The project investigates fundamental algorithmic tasks in using data examples as a device to illustrate and understand the behavior of already derived schema mappings, and also as inputs to a schema-mapping design system that will derive a suitable schema mapping based on the given data examples. The second main aim is to investigate the uses of schema mappings in integrating and exchanging inconsistent data that arise when bringing together data from heterogeneous sources. Since the current framework of data exchange does not handle inconsistencies well, this project re-examines data exchange and extends it to gracefully handle inconsistencies.
This project will advance the state of the art in designing schema mappings and managing inconsistent databases. Furthermore, it will contribute to the development of human resources in science and engineering through the teaching and research training of graduate students.
