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Suited Support for Distributed Web Intelligence Cooperative Work

  • Dominique DecouchantEmail author
  • Sonia Mendoza
  • José Rodríguez
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

Researches and efforts currently being developed within the World Wide Web environment, whose aim is to provide cooperative supports, are mainly performed in the field of the “Semantic Web”. These efforts are based on technological components such as the XML, RDF, and OWL languages that allow the cooperative definition of distributed ontologies. From these components, it is possible to develop “reasoning” programs that are able a) to infer information from data described with these languages and b) to exploit the defined ontologies. Moreover, programs may also be defined to provide supports to collaborators to cooperatively exploit the defined ontologies. However, all theses efforts remain developed at the application level. Thus, no suited distributed support for Web cooperative work had been investigated that deals with the unreliability of such a distributed environment.

In this chapter, we present the PIÑAS infrastructure which provides means for supporting cooperative work on the Web. Using cooperative applications that are built employing the services of this infrastructure, several users can access and modify replicated shared entities in a consistent and controlled way. PIÑAS provides suited features, such as: user identification, multi-site user definition, user and entity naming, shared entity fragmentation and replication, storage, consistency, and automatic distributed updating. We propose seamless extensions to standard Web services that can be fully integrated within the Web environment. Moreover, the innovative PIÑAS features provide reliable support for temporarily disconnected and nomadic work.

Keywords

Storage Site Cooperative Work Distribute Coordination Function User Definition Hybrid Architecture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2010

Authors and Affiliations

  • Dominique Decouchant
    • 1
    • 2
    Email author
  • Sonia Mendoza
    • 3
  • José Rodríguez
    • 3
  1. 1.Laboratoire LIG de GrenobleGrenobleFrance
  2. 2.UAM-CuajimalpaMéxico D.F.México
  3. 3.Departamento de ComputaciónCINVESTAV-IPNMéxico D.F.México

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