RED 2010
         Montpellier, France, May 26th, 2013

  Sixth International Workshop on Resource Discovery
 

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RED Sixth International Workshop on
REsource Discovery (Full-day) May 26th, 2013

Existing Web infrastructures such as the Semantic Web, Linking Data Projects, and Semantic Grids have supported the publication of a tremendous amount of resources. In order to provide users with the capability of using available resources in their day-to-day tasks, scalable infrastructures and efficient techniques to discover, select and access resources are required. Semantic descriptions of functionally and quality of resources as well as user preferences, play an important role in the achievement of this goal.

A resource may be a data repository, a database management system, a SPARQL endpoint, a link between resources, an entity in a social network, a semantic wiki, or a linked service. Resources are characterized by core information including a name, a description of its functionality, its URLs, and various additional Quality of Service parameters that express its non-functional characteristics. Resource discovery is the process of identifying, locating and selecting existing resources that satisfy specific functional and non-functional requirements; also, resource discovery includes the problem of predicting links between resources. Current research includes crawling, indexing, ranking, clustering, and rewriting techniques, for collecting and consuming the resources for a specific request; additionally, processing techniques are required to ensure an efficient and effective access of the resources.

The Sixth International Workshop on Resource Discovery aims at bringing together researchers from the database, artificial intelligence and semantic web areas, to discuss research issues and experiences in developing and deploying concepts, techniques and applications that address various issues related to resource discovery. Papers presenting theoretical or applicative material are expected. This sixth edition will focus on techniques to adress resource discovery to support Big Data applications. Big Data is characterized by volume, velocity and variety; Big Data refers to large,  diverse, complex, longitudinal or distributed  datasets generated from instruments, sensors, Internet  transactions, email, video, click streams, and other digital resources. Tools of special interest should allow  handling, storing, transmitting as welll as the analysis of resources that are part of Big Data.


Workshop key dates

Full Paper Submission Deadline:
March 4, 2013 Hawaii Time
Acceptance notification:
April 1, 2013
Camera ready:
April 15, 2013
Workshop Full-Day: May 26/27, 2013

We invite the submission of 15 pages (long papers), short research papers (up to 8 pages) in LNCS format.

Papers accepted to workshop will be available on-line. All papers presented at the workshop will be invited to be revised and extended for a second peer-review process. At the issue of the second review process, accepted papers in a volumen of Lecture Notes in Computer Science by Springer. 



Workshop Chairs and
Organizing Committee

Zoé Lacroix, Arizona State University, USA.
Maude Manouvrier, Université Paris-Dauphine, France
.
Edna Ruckhaus, Universidad Simón Bolívar, Caracas, Venezuela.
Maria-Esther Vidal, Universidad Simón Bolívar, Caracas, Venezuela.

LNCS Springer


May 26/27,  2013
Montpellier, France
At the 10th Extended Semantic Web Conference (ESWC 2013)
Topics of Interest include (but are not limited to):

  • Big Data Applications.
  • Services in the Cloud to support Big Data handling, storing, transmision and analysis.
  • Ontology-based Resource Discovery. 
  • Formalisms to semantically describe resources as well as user preferences and quality.
  • Resource Discovery and Social Networks.
  • Resource Discovery and Peer-to-Peer.
  • Resource Discovery query languages.
  • Applications to register, produce and consume resources, e.g., crawlers, search engines, federated frameworks.
  • Query rewriting over Web resources such as Web Services and Linked Datasets.
  • Cost-models and Optimization for Resource Discovery.
  • Link prediction techniques for Resource Discovery.
  • QoS-based Resource Discovery and Ranking Techniques.
  • Query Optimization and Execution Techniques for Web Resources.
  • Automatic extraction of metadata.
  • Scalable architectures for Resource Discovery.
  • Applications in E-government, Life Science, Healthcare, among others.
  • Benchmarks for Resource Discovery Evaluation.