Mapping Adaptation under Evolving Schemas

Abstract

To achieve interoperability, modern information systems and e-commerce applications use mappings to translate data from one representation to another. In dynamic environments like the Web, data sources may change not only their data but also their schemas, their semantics, and their query capabilities. Such changes must be reflected in the mappings. Mappings left inconsistent by a schema change have to be detected and updated. As large, complicated schemas become more prevalent, and as data is reused in more applications, manually maintaining mappings (even simple mappings like view definitions) is becoming impractical. We present a novel framework and a tool (ToMAS) for automatically adapting mappings as schemas evolve. Our approach considers not only local changes to a schema, but also changes that may affect and transform many components of a schema. We consider a comprehensive class of mappings for relational and XML schemas with choice types and (nested) constraints. Our algorithm detects mappings affected by a structural or constraint change and generates all the rewritings that are consistent with the semantics of the mapped schemas. Our approach explicitly models mapping choices made by a user and maintains these choices, whenever possible, as the schemas and mappings evolve. We describe an implementation of a mapping management and adaptation tool based on these ideas and compare it with a mapping generation tool.

Schema evolution analysis for embedded databases

“…For large, enterprise-class databases, researchers and practitioners have proposed many solutions to reconcile applications and schema updates, such as query rewriting [10], schema versioning and temporal querying [11], [12], schema mapping [13], [14], schema matching [15], or editioning views [16]. However, these approaches might not be suitable for ED schema evolution for several reasons; for example, they require SMOs (Schema Modification Operators [17], [10]) to be specified by the developers; also, schema matching/mapping/versioning implementations might impose significant overhead which is problematic for mobile and resourceconstrained systems.…”

Schema Evolution in Data Warehousing Environments – A Schema Transformation-Based Approach

“…[20,14], in that in our case the new mappings are a composition of the original transformation pathway and the transformation pathway which expresses the schema evolution. Thus, the new mappings are, by definition, correct.…”

Semi-automatic Adaptation of Mappings between Life Science Ontologies

“…In the context of schema evolution and model management [19,20], it has been proposed to evolve a previously determined mapping by composing it with a match mapping between the old and the new version of an updated schema or model. This composition approach has been explored in [21] for schema mappings and was shown to avoid the full re-calculation of existing mappings.…”