05203nam 22008175 450 991048350510332120251226203857.01-280-38798-X97866135659073-642-14415-210.1007/978-3-642-14415-8(CKB)2550000000015604(SSID)ssj0000446747(PQKBManifestationID)11314927(PQKBTitleCode)TC0000446747(PQKBWorkID)10504602(PQKB)10117669(DE-He213)978-3-642-14415-8(MiAaPQ)EBC3065486(PPN)149072767(EXLCZ)99255000000001560420100706d2010 u| 0engurnn#008mamaatxtccrResource Discovery Second International Workshop, RED 2009, Lyon, France, August 28, 2009, Revised Papers /edited by Zoé Lacroix1st ed. 2010.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2010.1 online resource (IX, 141 p. 40 illus.)Information Systems and Applications, incl. Internet/Web, and HCI,2946-1642 ;6162Bibliographic Level Mode of Issuance: Monograph3-642-14414-4 Includes bibliographical references and index.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.Information Systems and Applications, incl. Internet/Web, and HCI,2946-1642 ;6162Computer engineeringComputer networksApplication softwareInformation storage and retrieval systemsSoftware engineeringArtificial intelligenceComputer Engineering and NetworksComputer and Information Systems ApplicationsComputer Communication NetworksInformation Storage and RetrievalSoftware EngineeringArtificial IntelligenceComputer engineering.Computer networks.Application software.Information storage and retrieval systems.Software engineering.Artificial intelligence.Computer Engineering and Networks.Computer and Information Systems Applications.Computer Communication Networks.Information Storage and Retrieval.Software Engineering.Artificial Intelligence.621.39Lacroix Zoe1700089MiAaPQMiAaPQMiAaPQBOOK9910483505103321Resource discovery4198709UNINA