Negotiation in Database Schema Integration

Databases are playing an increasingly important role in organizations. Timely, accurate access to information has become a critical component of gaining competitive advantage. Data availability is commonly perceived as a critical success factor for an organizationÕs long-term survival, and day-to-day operations can be crippled by failure of the database system to satisfy user requirements. However, a number of emerging issues complicate organizationsÕ ability to provide comprehensive and reliable access to disparate information resources. Further, data accessibility is often compromised due to the typically high cost associated with addressing these issues in practice. Examples of such issues which have emerged in the past decade include the proliferation and investment in autonomous databases within organizations, heterogeneity among data models and database management systems employed, the increasingly important role of distributed systems, and the increasing complexity and knowledge-intensive nature of integrating database schemas. All these factors contribute to the increasing importance of developing feasible options for providing interoperability among existing databases, and therefore, of pursuing research in the area of database schema integration. Indeed, this research focuses specifically on knowledge requirement problems involved in integrating the schema of existing databases in order to provide interoperability and transparent access to disparate information resources without the investment involved in complete systems redesig

Schema Management for Data Integration: A Short Survey

Schema management is a basic problem in many database application domains such as data integration systems. Users need to access and manipulate data from several databases. In this context, in order to integrate data from distributed heterogeneous database sources, data integration systems demand the resolution of several issues that arise in managing schemas. In this paper, we present a brief survey of the problem of schema matching which is used for solving problems of schema integration processing. Moreover, we propose a technique for integrating and querying distributed heterogeneous XML schemas.

The OBO Foundry: Coordinated Evolution of Ontologies to Support Biomedical Data Integration

The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or ‘ontologies’. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium has set in train a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing a process of coordinated reform, and new ontologies being created, on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable, logically well-formed, and to incorporate accurate representations of biological reality. We describe the OBO Foundry initiative, and provide guidelines for those who might wish to become involved in the future

Database Integration: the Key to Data Interoperability

Most of new databases are no more built from scratch, but re-use existing data from several autonomous data stores. To facilitate application development, the data to be re-used should preferably be redefined as a virtual database, providing for the logical unification of the underlying data sets. This unification process is called database integration. This chapter provides a global picture of the issues raised and the approaches that have been proposed to tackle the problem

Database Integration: an Overview of Issues and Approaches

In many large companies the widespread usage of computers has led a number of different application-specific databases to be installed. As company structures evolve, boundaries between departments move, creating new business units. Their new applications will use existing data from various data stores, rather than new data entering the organization. Henceforth, the ability to make data stores interoperable becomes a crucial factor for the development of new information systems. Data interoperability may come in various degrees. At the lowest level, commercial gateways connect specific pairs of database management systems (DBMSs). Software providing facilities for defining persistent views over different databases [6] simplifies access to distant data but does not support automatic enforcement of consistency constraints among different databases. Full interoperability is achieved by distributed or federated database systems, which support integration of existing data into virtual databases (i.e. databases which are logically defined but not physically materialized). The latter allow existing databases to remain under control of their respective owners, thus supporting a harmonious coexistence of scalable data integration and site autonomy requirements [9]. Federated systems are very popular today. However, before they become marketable, many issues remain to be solved. Design issues focus on either human-centered aspects (cooperative work, including autonomy issues and negotiation procedures) or database-centered aspects (data integration, schema/database evolution). Operational issues investigate system interoperability mainly in terms of support of new transaction types, new query processing algorithms, security concerns, etc. General overviews may be found elsewhere [4, 9]. This paper is devoted to database integration, possibly the most critical issue. Simply stated, database integration is the process which takes as input a set of databases, and produces as output a single unified description of the input schemas (the integrated schema) and the associated mapping information supporting integrated access to existing data through the integrated schema. As such, database integration is also used in the process of re-engineering an exist i ng l egacy system. Database integration has attracted many diverse and diverging contributions. The purpose, and the main intended contribution of this article is to provide a clear picture of what are the approaches and the current solutions and what remains to be achieved

Integration operators for generating RDF/OWL-based user defined mediator views in a grid environment

Research and development activities relating to the grid have generally focused on applications where data is stored in files. However, many scientific and commercial applications are highly dependent on Information Servers (ISs) for storage and organization of their data. A data-information system that supports operations on multiple information servers in a grid environment is referred to as an interoperable grid system. Different perceptions by end-users of interoperable systems in a grid environment may lead to different reasons for integrating data. Even the same user might want to integrate the same distributed data in various ways to suit different needs, roles or tasks. Therefore multiple mediator views are needed to support this diversity. This paper describes our approach to supporting semantic interoperability in a heterogeneous multi-information server grid environment. It is based on using Integration Operators for generating multiple semantically rich RDF/OWL-based user defined mediator views above the grid participating ISs. These views support different perceptions of the distributed and heterogeneous data available. A set of grid services are developed for the implementation of the mediator views

Documenting Data Integration Using Knowledge Graphs

With the increasing volume of data on the Web and the proliferation of published knowledge graphs, there is a growing need for improved data management and information extraction. However, heterogeneity issues across the data sources, i.e., various formats and systems, negatively impact efficient access, manage, reuse, and analyze the data. A data integration system (DIS) provides uniform access to heterogeneous data sources and their relationships; it offers a unified and comprehensive view of the data. DISs resort to mapping rules, expressed in declarative languages like RML, to align data from various sources to classes and properties defined in an ontology. This work defines a knowledge graph where data integration systems are represented as factual statements. The aim of this work is to provide the basis for integrated analysis of data collected from heterogeneous data silos. The proposed knowledge graph is also specified as a data integration system, that integrates all data integration systems. The proposed solution includes a unified schema, which defines and explains the relationships between all elements in the data integration system DIS=⟨G, S, M, F⟩. The results suggest that factual statements from the proposed knowledge graph, improve the understanding of the features that characterize knowledge graphs declaratively defined like data integration systems

Multi-representation Ontology in the Context of Enterprise Information Systems

International audienceIn the last decade, ontologies as shared common vocabulary played a major role in many AI applications and informationintegration for heterogeneous, distributed systems. The problems of integrating and developing information systems anddatabases in heterogeneous, distributed environment have been translated in the technical perspectives as system’sinteroperability. Ontologies, however, are foreseen to play a key role in resolving partially the semantic conflicts anddifferences that exist among systems. Domain ontologies, however, are constructed by capturing a set of concepts and theirlinks according to various criteria such as the abstraction paradigm, the granularity scale, interest of user communities, andthe perception of the ontology developer. Thus, different applications of the same domain end up having severalrepresentations of the same real world phenomenon. Multi-representation ontology is an ontology (or ontologies) thatcharacterizes ontological concept by a variable set of properties (static and dynamic) or attributes in several contexts and/ orin several scales of granularity. This paper introduces the formalism used for defining the paradigm of multi-representationontology and shows the manifestation of this paradigm with Enterprise Information Systems