User-centric inference based on history of context data in pervasive environments

Kalatzis, N; Roussaki, L; Liampotis, N; Strimpakou, M; Pils, C

dc.contributor.author	Kalatzis, N	en
dc.contributor.author	Roussaki, L	en
dc.contributor.author	Liampotis, N	en
dc.contributor.author	Strimpakou, M	en
dc.contributor.author	Pils, C	en
dc.date.accessioned	2014-03-01T02:45:50Z
dc.date.available	2014-03-01T02:45:50Z
dc.date.issued	2008	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32423
dc.subject	Basic prediction rules	en
dc.subject	Context prediction	en
dc.subject	History of context	en
dc.subject	Rule generation models	en
dc.subject	User status	en
dc.subject.other	Basic prediction rules	en
dc.subject.other	Context data	en
dc.subject.other	Context information	en
dc.subject.other	Context prediction	en
dc.subject.other	Context- awareness	en
dc.subject.other	Context-aware systems	en
dc.subject.other	Human machine interaction	en
dc.subject.other	Minimal processing	en
dc.subject.other	Multiple contexts	en
dc.subject.other	Pervasive computing systems	en
dc.subject.other	Pervasive environments	en
dc.subject.other	Prediction rule generation	en
dc.subject.other	Rule generation models	en
dc.subject.other	Storage resources	en
dc.subject.other	Success ratio	en
dc.subject.other	User status	en
dc.subject.other	User-centric	en
dc.subject.other	Data compression	en
dc.subject.other	Ubiquitous computing	en
dc.title	User-centric inference based on history of context data in pervasive environments	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1145/1387309.1387316	en
heal.identifier.secondary	http://dx.doi.org/10.1145/1387309.1387316	en
heal.publicationDate	2008	en
heal.abstract	Pervasive computing systems need to be strongly proactive. Context-awareness contributes to this, thus minimizing human-machine interaction. Context-aware systems are greatly enhanced by the utilization of recorded history of the users' situations and interactions. In this paper, an approach is proposed for modelling, storing and exploiting history-of-context, in order to predict or estimate context information. The proposed framework is context-type-independent, requires minimal processing and storage resources, and can be used for data compression. It is based on multiple context prediction rule generation models, demonstrates high prediction success ratio, and has been empirically evaluated via extensive experiments. Copyright 2008 ACM.	en
heal.journalName	Proceedings of the International Conference on Pervasive Services, ICPS 2008 3rd International Workshop on Services Integration in Pervasive Environments, SIPE'08	en
dc.identifier.doi	10.1145/1387309.1387316	en
dc.identifier.spage	25	en
dc.identifier.epage	30	en