In this paper we describe a new approach to data integration which subsumes the previous approaches of local as view (LAV) and global as view (GAV). Our method, which we term both as view (BAV), is based on the use of reversible schema transformation sequences. We show how LAV and GAV view definitions can be fully derived from BA V schema transformation sequences, and how BA V transformation sequences may be partially derived from LAV or GAV view definitions. We also show how BAV supports the evolution of both global and local schemas, and we discuss ongoing implementation of the BA V approach within the AutoMed project.

Several methodologies for integrating database schemas have been proposed in the literature, using various common data models (CDMs). As part of these methodologies transformations have been defined that map between schemas which are in some sense equivalent. This paper describes a general framework for formally underpinning the schema transformation process. Our formalism clearly identifies which transformations apply for any instance of the schema and which only for certain instances. We illustrate the applicability of the framework by showing how to define a set of primitive transformations for an extended ER model and by defining some of the common schema transformations as sequences of these primitive transformations. The same approach could be used to formally define transformations on other CDMs.

Whilst it is a common task in systems integration to have to transform between different semantic data models, such inter-model transformations are often specified in an ad hoc manner. Further, they are usually based on transforming all data into one common data model, which may not contain suitable data constructs to model directly all of the constructs of the data models being integrated. Our approach is to define each of the data models to be integrated in terms of a more `elemental', low-level data model-- a hypergraph-based one. We show how these definitions can be used to automatically derive schema transformation operators for the higher-level data models. These higher-level transformations can be used to perform inter-model transformations, and in turn allow data and queries to be automatically translated from one model to another. Finally, we show how to use the hypergraph data model in order to define inter-model links, and hence allow queries which span more than one model.

This paper presents a new approach to schema evolution, which combines the activities of schema integration and schema evolution into one framework. In previous work we have developed a general framework to support schema transformation and integration in heterogeneous database architectures. Here we show how this framework also readily supports evolution of source schemas, allowing the global schema and the query translation pathways to be easily repaired, as opposed to having to be regenerated, after changes to source schemas.

Integration of heterogeneous databases requires that semantic differences between schemas are resolved by a process of schema transformation. Previously, we have developed a general framework to support the schema transformation process, consisting of a hypergraph-based common data model and a set of primitive schema transformations de ned for this model. Higher-level common data models and primitive schema transformations for them can be defined in terms of this lower-level model. In this paper, we show that a key feature of our framework is that both primitive and composite schema transformations are automatically reversible. We show how these transformations can be used to automatically migrate or wrap data, queries and updates between semantically equivalent schemas....

XML is fast becoming the standard for information exchange on the Internet. As such, information expressed in XML will need to be integrated with existing information systems, which are mostly based on structured data models such as relational, object-oriented or object /relational data models. This paper shows how our previous framework for integrating heterogeneous structured data sources can also be used for integrating XML data sources with each other and/or with other structured data sources. In our approach, the constructs and transformations of modelling languages such as ER, XML etc. are defined in terms of the constructs and transformations of a lower-level graphbased data model. This allows constructs from multiple modelling languages to co-exist within the same intermediate schema, thus avoiding the need for a high-level common data model and the semantic mismatches that this can bring about. Transformations between schemas are expressed as sequences of primitive transformations and a key feature of them is that they are automatically reversible. This allows automatic translation of data, queries and updates between semantically equivalent or overlapping heterogenous schemas.

Abstract. This paper describes the AutoMed repository and some associated tools, which provide the first implementation of the both as view (BAV) approach to data integration. Apart from being a highly expressive data integration approach, BAV in additional provides a method to support a wide range of data modelling languages, and describes transformations between those data modelling languages. This paper documents how BAV has been implemented in the AutoMed repository, and how several practical problems in data integration between heterogeneous data sources have been solved. We illustrate the implementation with examples in the relational, ER, and semi-structured data models. 1

Several methodologies for semantic schema integration have been proposed in the literature, often using some variant of the ER model as the common data model. As part of these methodologies, various transformations have been defined that map between ER schemas which are in some sense equivalent. This paper gives a unifying formalisation of the ER schema transformation process and shows how some common schema transformations can be expressed within this single framework. Our formalism clearly identifies which transformations apply for any instance of the schema and which only for certain instances.

Abstract. Data integration is frequently performed between heterogeneous data sources, requiring that not only a schema, but also the data modelling language in which that schema is represented must be transformed between one data source and another. This paper describes an extension to the hypergraph data model (HDM), used in the AutoMed data integration approach, that allows constraint constructs found in static data modelling languages to be represented by a small set of primitive constraint operators in the HDM. In addition, a set of five equivalence preserving transformation rules are defined that operate over this extended HDM. These transformation rules are shown to allow a bidirectional mapping to be defined between equivalent relational, ER, UML and ORM schemas. The approach we propose provides a precise framework in which to compare data modelling languages, and precisely identifies what semantics of a particular domain one data model may express that another data model may not express. The approach also forms the platform for further work in automating the process of transforming between different data modelling languages. The use of the both-as-view approach to data integration means that a bidirectional association is produced between schemas in the data modelling language. Hence a further advantage of the approach is that composition of data mappings may be performed such that mapping two schemas to one common schema will produce a bidirectional mapping between the original two data sources.

Abstract. This paper presents an extensible architecture that can be used to support the integration of heterogeneous biological data sets. In our architecture, a clustering approach has been developed to support distributed biological data sources with inconsistent identi cation of biological objects. The architecture uses the AutoMed data integration toolkit to store the schemas of the data sources and the semiautomatically generated transformations from the source data into the data of an integrated warehouse. AutoMed supports bi-directional, extensible transformations which can be used to update the warehouse data as entities change, are added, or are deleted in the data sources. The transformations can also be used to support the addition or removal of entire data sources, or evolutions in the schemas of the data sources or of the warehouse itself. The results of using the architecture for the integration of existing genomic data sets are discussed. 1