By Jürgen Bohn, Friedemann Mattern (auth.), Panos Markopoulos, Berry Eggen, Emile Aarts, James L. Crowley (eds.)
This quantity of the LNCS is the formal complaints of the 2d eu Symposium on Ambient Intelligence, EUSAI 2004. This occasion was once hung on November 8–10, 2004 on the Eindhoven college of know-how, in Eindhoven, the Netherlands. EUSAI 2004 a profitable first occasion in 2003, geared up via Philips examine. This grew to become out to be a well timed initiative that created a discussion board for bringing jointly eu researchers, engaged on varied disciplines all contributing in the direction of the human-centric technological imaginative and prescient of ambient intelligence. in comparison to meetings engaged on related and overlapping fields, the 1st EUSAI used to be characterised through a robust commercial concentration mirrored within the software committee and the content material of this system. As software chairs of EUSAI 2004 we attempted to maintain the nature for this occasion and its mixed specialise in the 4 significant thematic components: ubiquitous computing, context wisdom, intelligence, and ordinary interplay. additional, we attempted to make EUSAI 2004 develop right into a full-fledged double-track convention, with surrounding occasions like tutorials and really expert workshops, a poster and demonstration exhibition and a scholar layout festival. The convention application incorporated 3 invited keynotes, Ted Selker from MIT, Tom Rodden from the collage of Nottingham and Tom Erickson from IBM.
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Extra info for Ambient Intelligence: Second European Symposium, EUSAI 2004, Eindhoven, The Netherlands, November 8-11, 2004. Proceedings
The scenario continues in John’s car where he exchanges data with his friend (Paul). John’s PMA (Personal Mobile Assistant) is responsible for personalised assistant since he left home to go to his office and helps John to transfer data to Paul’s PDA and find information about the intended rout. Figure 1 models part of the use case scenario to show how we can apply the performance model. is John’s workstation at his home, and are John’s and his friend’s PDAs respectively, both are located in his car, and is his office workstation.
Specifically the GAS Ontology is divided into two layers: the GAS Core Ontology (GAS-CO); that contains the common vocabulary, and the GAS Higher Ontology (GAS-HO); that represents artifact’s specific knowledge using concept represented into GAS-CO. 2 The GAS Core Ontology (GAS-CO) The GAS-CO describes the common language that artifacts use to communicate. So it must describe the semantics of the basic terms of UbiComp applications and define their inter-relations. It must also contain the service classification in order to support the service discovery mechanism.
Although the service discovery mechanism always proposed an appropriate artifact, the current version of the GAS Ontology is restrictive since it demands all artifacts to have the same service classification. This is a limitation that we intent to eliminate by adding to GAS Ontology manager the capability to map a service description to another one, using the knowledge that artifacts have acquired from their collaboration. The design of the GAS Ontology and the approach to divide it into two layers resulted to be very helpful for both its development and use.