Data integration through service-based mediation for web-enabled information systems

The Web and its underlying platform technologies have often been used to integrate existing software and information systems. Traditional techniques for data representation and transformations between documents are not sufficient to support a flexible and maintainable data integration solution that meets the requirements of modern complex Web-enabled software and information systems. The difficulty arises from the high degree of complexity of data structures, for example in business and technology applications, and from the constant change of data and its representation. In the Web context, where the Web platform is used to integrate different organisations or software systems, additionally the problem of heterogeneity arises. We introduce a specific data integration solution for Web applications such as Web-enabled information systems. Our contribution is an integration technology framework for Web-enabled information systems comprising, firstly, a data integration technique based on the declarative specification of transformation rules and the construction of connectors that handle the integration and, secondly, a mediator architecture based on information services and the constructed connectors to handle the integration process

Mediated data integration and transformation for web service-based software architectures

Service-oriented architecture using XML-based web services has been widely accepted by many organisations as the standard infrastructure to integrate heterogeneous and autonomous data sources. As a result, many Web service providers are built up on top of the data sources to share the data by supporting provided and required interfaces and methods of data access in a unified manner. In the context of data integration, problems arise when Web services are assembled to deliver an integrated view of data, adaptable to the specific needs of individual clients and providers. Traditional approaches of data integration and transformation are not suitable to automate the construction of connectors dedicated to connect selected Web services to render integrated and tailored views of data. We propose a declarative approach that addresses the oftenneglected data integration and adaptivity aspects of serviceoriented architecture

Competence Discovery and Composition

International audienceThe capture, the structuring and the exploitation of competences of an "object" (like a business partner, an employee, a software component, a Web service, etc.) are crucial problems in various applications, like cooperative and distributed applications or e\_business applications. The work we describe here concerns competence advertising, organization, discovery and composition. Indeed, one of the originality of the proposal is in the nature of the answers the intended system can return when seeking for individuals fitted with given competences: answers may be composite ones in that sense that when no single object meets the search criteria, we attempt to find out what a set of objects, when pooled together, do satisfy the whole search criteria. Conceptual Graphs (CGs) are used as a knowledge representation formalism and operations on graphs are used as a search mechanism. A client/server prototype, viewed as a federation of mediators, has been developed as a proof of concept

Conceptual Graphs for Fomally Managing and Discovering Complementary Competences

International audienceThe capture, the structuring of the expertise or the competences of an "object" (lie a business partner, an employee and even a software component or a Web service) are of very crucial interest in many application domains, like cooperative and distributed applications as well as in cooperative e\_business applications and in human resource managment. The work that is described in this paper concerns the advertising , the classification and the discovry of competences. The foundings of the proposals that are described here after are a formal representation of competences using conceptual graphs and the use of operations on conceptual graphs for competence discovery and their possible composition

Ontology mapping: the state of the art

Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mapping has beeb the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping

Dynamic Integration of Evolving Distributed Databases using Services

This thesis investigates the integration of many separate existing heterogeneous and distributed databases which, due to organizational changes, must be merged and appear as one database. A solution to some database evolution problems is presented. It presents an Evolution Adaptive Service-Oriented Data Integration Architecture (EA-SODIA) to dynamically integrate heterogeneous and distributed source databases, aiming to minimize the cost of the maintenance caused by database evolution. An algorithm, named Relational Schema Mapping by Views (RSMV), is designed to integrate source databases that are exposed as services into a pre-designed global schema that is in a data integrator service. Instead of producing hard-coded programs, views are built using relational algebra operations to eliminate the heterogeneities among the source databases. More importantly, the definitions of those views are represented and stored in the meta-database with some constraints to test their validity. Consequently, the method, called Evolution Detection, is then able to identify in the meta-database the views affected by evolutions and then modify them automatically. An evaluation is presented using case study. Firstly, it is shown that most types of heterogeneity defined in this thesis can be eliminated by RSMV, except semantic conflict. Secondly, it presents that few manual modification on the system is required as long as the evolutions follow the rules. For only three types of database evolutions, human intervention is required and some existing views are discarded. Thirdly, the computational cost of the automatic modification shows a slow linear growth in the number of source database. Other characteristics addressed include EA-SODIA’ scalability, domain independence, autonomy of source databases, and potential of involving other data sources (e.g.XML). Finally, the descriptive comparison with other data integration approaches is presented. It shows that although other approaches may provide better performance of query processing in some circumstances, the service-oriented architecture provide better autonomy, flexibility and capability of evolution

Intelligent Agents and Their Potential for Future Design and Synthesis Environment

This document contains the proceedings of the Workshop on Intelligent Agents and Their Potential for Future Design and Synthesis Environment, held at NASA Langley Research Center, Hampton, VA, September 16-17, 1998. The workshop was jointly sponsored by the University of Virginia's Center for Advanced Computational Technology and NASA. Workshop attendees came from NASA, industry and universities. The objectives of the workshop were to assess the status of intelligent agents technology and to identify the potential of software agents for use in future design and synthesis environment. The presentations covered the current status of agent technology and several applications of intelligent software agents. Certain materials and products are identified in this publication in order to specify adequately the materials and products that were investigated in the research effort. In no case does such identification imply recommendation or endorsement of products by NASA, nor does it imply that the materials and products are the only ones or the best ones available for this purpose. In many cases equivalent materials and products are available and would probably produce equivalent results

Query Optimization Techniques For Scaling Up To Data Variety

Even though Data Lakes are efficient in terms of data storage, they increase the complexity of query processing; this can lead to expensive query execution. Hence, novel techniques for generating query execution plans are demanded. Those techniques have to be able to exploit the main characteristics of Data Lakes. Ontario is a federated query engine capable of processing queries over heterogeneous data sources. Ontario uses source descriptions based on RDF Molecule Templates, i.e., an abstract description of the properties belonging to the entities in the unified schema of the data in the Data Lake. This thesis proposes new heuristics tailored to the problem of query processing over heterogeneous data sources including heuristics specifically designed for certain data models. The proposed heuristics are integrated into the Ontario query optimizer. Ontario is compared to state-of-the-art RDF query engines in order to study the overhead introduced by considering heterogeneity during query processing. The results of the empirical evaluation suggest that there is no significant overhead when considering heterogeneity. Furthermore, the baseline version of Ontario is compared to two different sets of additional heuristics, i.e., heuristics specifically designed for certain data models and heuristics that do not consider the data model. The analysis of the obtained experimental results shows that source-specific heuristics are able to improve query performance. Ontario optimization techniques are able to generate effective and efficient query plans that can be executed over heterogeneous data sources in a Data Lake