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020 _a9783642144158
_9978-3-642-14415-8
040 _cMX-MeUAM
050 4 _aQA76.76.A65
082 0 4 _a005.7
_223
100 1 _aLacroix, Zoé.
_eeditor.
245 1 0 _aResource Discovery
_h[recurso electrónico] :
_bSecond International Workshop, RED 2009, Lyon, France, August 28, 2009. Revised Papers /
_cedited by Zoé Lacroix.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2010.
300 _aIX, 141p. 40 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v6162
505 0 _aImmune-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.
520 _aResource 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.
650 0 _aComputer science.
650 0 _aComputer Communication Networks.
650 0 _aSoftware engineering.
650 0 _aDatabase management.
650 0 _aInformation storage and retrieval systems.
650 0 _aInformation systems.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aInformation Systems Applications (incl.Internet).
650 2 4 _aComputer Communication Networks.
650 2 4 _aInformation Storage and Retrieval.
650 2 4 _aSoftware Engineering.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aDatabase Management.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642144141
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v6162
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-14415-8
596 _a19
942 _cLIBRO_ELEC
999 _c202635
_d202635