Systems and infrastructures are currently being developed to support Web services. The main idea is to encapsulate an organization's functionality within an appropriate interface and advertise it as Web services. While in some cases Web services may be utilized in an isolated form, it is normal to expect Web services to be integrated as part of workflow processes. The composition of workflow processes that model e-service applications differs from the design of traditional workflows, in terms of the number of tasks (Web services) available to the composition process, in their heterogeneity, and in their autonomy. Therefore, two problems need to be solved: how to efficiently discover Web services—based on functional and operational requirements—and how to facilitate the interoperability of heterogeneous Web services. In this paper, we present a solution within the context of the emerging Semantic Web that includes use of ontologies to overcome some of the problem. We describe a prototype that has been implemented to illustrate how discovery and interoperability functions are achieved more efficiently.

/pdf/semantic-e-workflow-composition-w26tlvrbrv.pdf

Semantic E-Workflow Composition

http://www.inf.udec.cl/%7eandrea/papers/master.pdf

Assessing semantic similarity among spatial entity classes

A Cooperative Federated Database System (CFDBS) is an information sharing environment in which units of information to be shared may be substantially structured, and participants are actively involved in sharing activities. We focus on the problem of shared ontology for the purpose of discovery in the CFDBS context. We introduce the concept and mechanism of the Dynamic Classificational Ontology (DCO), which is a mediator to help participants identify and resolve ontological similarities and differences. A DCO contains top level knowledge about information units exported by information providers, along with classificational knowledge. By contrast with fixed hierarchical classifications, the DCO builds domain specific, dynamically changing classification schemes; it specifically contains knowledge about overlap among information units. Information providers contribute to the DCO when information units are exported, and the current knowledge in the DCO is in turn utilized to guide export and discovery of information. At the cost of information providers' cooperative efforts, this approach supports much more systematic discovery than that provided by keyword based search, with substantially greater precision and recall. An experimental prototype of the DCO has been developed, and applied and tested to improve the precision and recall of Medline document searches for biomedical information sharing.

Dynamic classificational ontologies for discovery in cooperative federated databases

There is a large and increasing volume of documents, data sources and data base management systems available in the world, and many autonomous and heterogeneous sources speak of a same reality while using different words and conceptual structures Many organizations need to dispose of a system that handles such data in a homogeneous way, which necessitates the integration of these data sources The goal of a data integration system is to develop a homogeneous interface for the end users to query several heterogeneous and autonomous sources Building such a homogeneous interface raises many challenges among which the heterogeneity of data sources, the fragmentation of data, the processing and optimization of queries appear to be the most important There are many research projects that present different approaches and each of them proposes a solution to each of these problems Depending on the integrated view, these approaches can be categorized into two main categories: materialized and virtual approaches; there are also some hybrid approaches when there is a composition of materialized and virtual views The main advantage of a hybrid approach is to offer a trade-off between the query response time and data freshness in a data integration system In the existing approaches, query optimization is often privileged for the materialized part of the system In this thesis, we develop a hybrid approach which aims to extend query optimization to all the queries of the integration system It also provides a flexible data refreshing mechanism in order to tolerate different characteristics of sources and their data This approach is based on the Osiris object indexing system Osiris is a database and knowledge base platform with a specific object data model based on a hierarchy of views Its indexation system relies on the partitioning of the object space using the view constraints IXIA, the hybrid approach presented in this thesis, materializes the indexation structure of the underlying objects at the mediator level The Oids of objects, their correspondence with the source objects and the needed data to refresh the indexation data are also materialized Our index-based data integration approach offers more flexibility in data refreshing than a fully materialized approach and a better query response time in comparison with a fully virtual data integration system

https://tel.archives-ouvertes.fr/tel-00407575/document

IXIA (IndeX-based Integration Approach) A Hybrid Approach to Data Integration

Data migration between relational and object-oriented database systems is useful whenever relational data is needed in new object-oriented environments. However, implementing migration programs, i.e. converting and loading legacy data into the new database system, is cumbersome and must be done for each pair of databases manually. This paper presents a flexible approach to support data exchange using a federation approach. Given any relational database, a migration program for filling an object-oriented database is generated. The other direction can be automated in the same way.

Supporting data migration between relational and object-oriented databases using a federation approach

Support for interoperability among autonomous, heterogeneous database systems is emerging as a key information management problem for the 1990s. A key challenge for achieving interoperability among multiple database systems is to provide capabilities to allow information units and resources to be flexibly and dynamically combined and interconnected while at the same time, preserve the investment in and autonomy of each existing system. This research specifically focuses on two key aspects of this: (1) how to discover the location and content of relevant, non-local information units, and (2) how to identify and resolve the semantic heterogeneity that exists between related information in different database components. We demonstrate and evaluate our approach using the remote-exchange experimental prototype system, which supports information sharing and exchange from the above perspective. >

Si

Papers

An intelligent system for identifying and integrating non-local objects in federated database systems