Global Understanding Environment: Towards Self-managed Web of Everything

  • Vagan Terziyan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7096)

Abstract

Current Web grows rapidly to several directions (from the Web of Documents to the Webs of Humans, Things, Services, Knowledge, Intelligence, etc.). Consequently the recent and future Web-based applications, systems and frameworks (like, e.g., Social and Ubiquitous Computing, SOA and Cloud Computing, etc.) should take into account challenges related to extremely high heterogeneity of components and exponentially increased complexity of a business logic connecting and making them interoperable. Enabling self-management enhanced with semantic technology seems to be an only option to handle that. We suggest adding a “virtual representative” to every resource in the Web to solve the global interoperability problem. Intelligent agent (a kind of “software robot”) will act, communicate and collaborate as a proxy on behalf of each Web resource. It will be connected with its resource via “semantic adapter”, will communicate with other agents via “semantic communication” and will be coordinated via “semantic business logic”. The relevant “Global Understanding Environment” (GUN) vision of Industrial Ontologies Group will be briefly presented. It can be considered as a kind of ubiquitous eco-system, which will be such proactive, self-managed evolutionary Semantic Web of Everything where all kinds of entities are assumed to understand, interact, serve, develop and learn from each other. The key set of enabling technologies for the GUN vision implementation includes: Artificial Intelligence; Semantic and Agent technologies; SOA and Cloud Computing. Some activities and projects, results and lessons learned by Industrial Ontologies Group on their way towards GUN will be briefly discussed.

Keywords

Cloud Computing High Heterogeneity Intelligent Agent Ubiquitous Computing Agent Technology 
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.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Vagan Terziyan
    • 1
  1. 1.Industrial Ontologies GroupUniversity of JyväskyläFinland

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