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dc.contributor.authorAnagnostopoulos, Theodoros-
dc.contributor.authorAnagnostopoulos, Christos-
dc.contributor.authorHadjiefthymiades, Stathes-
dc.date.accessioned2024-07-10T13:43:37Z-
dc.date.available2024-07-10T13:43:37Z-
dc.date.issued2010-12-01-
dc.identifierscopus-84885891537-
dc.identifier.isbn978-3-642-11482-3-
dc.identifier.issn1867-8211-
dc.identifier.other84885891537-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2714-
dc.description.abstractContext-awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify and predict context in order to act efficiently, beforehand, for the benefit of the user. In this paper, we propose a mobility prediction model, which deals with context representation and location prediction of moving users. Machine Learning (ML) techniques are used for trajectory classification. Spatial and temporal on-line clustering is adopted. We rely on Adaptive Resonance Theory (ART) for location prediction. Location prediction is treated as a context classification problem. We introduce a novel classifier that applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Since our approach is time-sensitive, the Hausdorff distance is considered more advantageous than a simple Euclidean norm. A learning method is presented and evaluated. We compare ART with Offline kMeans and Online kMeans algorithms. Our findings are very promising for the use of the proposed model in mobile context aware applications.en_US
dc.language.isoenen_US
dc.relation.ispartofAutonomic Computing and Communications Systems: 3rd International ICST Conference, Autonomics 2009en_US
dc.relation.ispartofseriesLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineeringen_US
dc.subjectAdaptive resonance theoryen_US
dc.subjectClassificationen_US
dc.subjectContext-awarenessen_US
dc.subjectLocation predictionen_US
dc.subjectMachine learningen_US
dc.subjectOnline clusteringen_US
dc.titleAn online adaptive model for location predictionen_US
dc.typeConference Paperen_US
dc.relation.conference3rd International ICST Conference "Autonomic Computing and Communications Systems" (Autonomics 2009), 9-11 September 2009, Limassol, Cyprusen_US
dc.identifier.doi10.1007/978-3-642-11482-3_5en_US
dc.identifier.scopus2-s2.0-84885891537-
dcterms.accessRights0en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.identifier.spage64en_US
dc.identifier.epage78en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusnot verifieden_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypeConference Paper-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0002-5587-2848-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
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