Preserving mapping consistency under schema changes
The VLDB Journal — The International Journal on Very Large Data Bases
Clio: Schema Mapping Creation and Data Exchange
Conceptual Modeling: Foundations and Applications
| Hi-index | 0.00 |
Integration, transformation, and translation of data is increasingly important for modern information systems and e-commerce applications. Views, and more generally, transformation specifications, or mappings, provide the foundation for many data transformation applications. Mappings are usually specified manually by data administrators that are familiar with the semantics of the data and have a good knowledge of the transformation language. The task of generating and managing mappings is laborious, time consuming and error-prone since data administrators are called on to write complex mappings in which they specify in tedious detail how the data is to be transformed. Even once deployed, mappings must remain under constant supervision since changes in the structure of the data may require changes in the mappings. In this dissertation, we elaborate on the development of mapping management tools that are intended to shield administrators from the laborious task of mapping management. In particular, we present a novel framework for generating mappings between any combination of XML and relational schemas. A set of high-level binary relationships between the elements of the two schemas, which are specified by a user or generated by a tool, are combined together to form semantically meaningful mappings. These mappings are guaranteed to be consistent with the constraints of the schemas. To handle schemas that are dynamically modified, we describe a methodology for automatically detecting the mappings that have become invalid as a result of a schema change and rewriting them to become consistent with the modified schema. Each rewriting is generated in a way that preserves, as much as possible, the semantics of the initial mapping. Finally, we show how collections of schemas and mappings can be used in queries to provide a better understanding of how data has been integrated and transformed.