TY  - BOOK
AU  - Lacroix,Zoé
ED  - SpringerLink (Online service)
TI  - Resource Discovery: Second International Workshop, RED 2009, Lyon, France, August 28, 2009. Revised Papers 
T2  - Lecture Notes in Computer Science,
SN  - 9783642144158
AV  - QA76.76.A65 
U1  - 005.7 23
PY  - 2010///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Computer science
KW  - Computer Communication Networks
KW  - Software engineering
KW  - Database management
KW  - Information storage and retrieval systems
KW  - Information systems
KW  - Artificial intelligence
KW  - Computer Science
KW  - Information Systems Applications (incl.Internet)
KW  - Information Storage and Retrieval
KW  - Software Engineering
KW  - Artificial Intelligence (incl. Robotics)
KW  - Database Management
N1  - 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
N2  - 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
UR  - http://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-14415-8
ER  -