Resource Discovery [electronic resource] : Second International Workshop, RED 2009, Lyon, France, August 28, 2009, Revised Papers /
RED 2009Corporate Author
RED 2009(2009 :Lyon, France)
Lacroix, Zoé.editor.
Resource discovery is the process of identifying and locating existing resources thathavea particularproperty. Aresourcecorrespondsto aninformationsource such as a data repositoryor databasemanagement system (e. g. , a query form or a textual search engine), a link between resources (an index or hyperlink), or a servicesuchasanapplicationoratool. Resourcesarecharacterizedbycoreinf- mation including a name, a description of its input and its output (parameters or format), its address, and various additional properties expressed as me- data. Resources are organized with respect to metadata that characterize their content (for data sources), their semantics (in terms of ontological classes and relationships), their characteristics (syntactical properties), their performance (with metrics and benchmarks), their quality (curation, reliability, trust), etc. Resource discovery systems allow the expression of queries to identify and - cate resources that implement speci?c tasks. Machine-based resource discovery relies on crawling, clustering, and classifying resources discovered on the Web automatically. The First Workshop on Resource Discovery (RED) took place on November 25, 2008 in Linz, Austria. It was organized jointly with the 10th International Conference on Information Integration and Web-Based Applications and S- vices and its proceedings were published by ACM. The second edition of the workshop was co-located with the 35th International Conference on Very Large Data Bases (VLDB) in the beautiful city of Lyon, France. Nine papers were selected for presentation at this second edition. Areas of researchaddressedby these papers include the problem of resource characterization and classi?cation, resourcecomposition,andontology-drivendiscovery.
Bibliographic Level Mode of Issuance: Monograph
Immune-Inspired Method for Selecting the Optimal Solution in Web Service Composition -- Web Database Schema Identification through Simple Query Interface -- Semantic Interoperability and Dynamic Resource Discovery in P2P Systems -- Data Source Management and Selection for Dynamic Data Integration -- A Provenance-Based Approach to Resource Discovery in Distributed Molecular Dynamics Workflows -- On Building a Search Interface Discovery System -- Building Specialized Multilingual Lexical Graphs Using Community Resources -- An Efficient Semantic Web Services Matching Mechanism -- Efficiently Selecting the Best Web Services.
Resource discovery is the process of identifying and locating existing resources thathavea particularproperty. Aresourcecorrespondsto aninformationsource such as a data repositoryor databasemanagement system (e. g. , a query form or a textual search engine), a link between resources (an index or hyperlink), or a servicesuchasanapplicationoratool. Resourcesarecharacterizedbycoreinf- mation including a name, a description of its input and its output (parameters or format), its address, and various additional properties expressed as me- data. Resources are organized with respect to metadata that characterize their content (for data sources), their semantics (in terms of ontological classes and relationships), their characteristics (syntactical properties), their performance (with metrics and benchmarks), their quality (curation, reliability, trust), etc. Resource discovery systems allow the expression of queries to identify and - cate resources that implement speci?c tasks. Machine-based resource discovery relies on crawling, clustering, and classifying resources discovered on the Web automatically. The First Workshop on Resource Discovery (RED) took place on November 25, 2008 in Linz, Austria. It was organized jointly with the 10th International Conference on Information Integration and Web-Based Applications and S- vices and its proceedings were published by ACM. The second edition of the workshop was co-located with the 35th International Conference on Very Large Data Bases (VLDB) in the beautiful city of Lyon, France. Nine papers were selected for presentation at this second edition. Areas of researchaddressedby these papers include the problem of resource characterization and classi?cation, resourcecomposition,andontology-drivendiscovery.
Computer engineering.
Application software.
Computer communication systems.
Information storage and retrieval.
Software engineering.
Artificial intelligence.
Computer Engineering.
Information Systems Applications (incl. Internet).
Computer Communication Networks.
Information Storage and Retrieval.
Software Engineering.
Artificial Intelligence.