DC FieldValueLanguage
dc.contributor.authorAnagnostopoulos, Theodoros-
dc.contributor.authorGilman, Ekaterina-
dc.contributor.authorCortes, Marta-
dc.contributor.authorKostakos, Panos-
dc.contributor.authorMehmood, Hassan-
dc.contributor.authorPirttikangas, Susanna-
dc.date.accessioned2024-07-05T14:15:56Z-
dc.date.available2024-07-05T14:15:56Z-
dc.date.issued2021-03-01-
dc.identifierscopus-85108571018-
dc.identifier.issn2624-6511-
dc.identifier.other85108571018-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2685-
dc.description.abstractReal-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed.en_US
dc.language.isoenen_US
dc.relation.ispartofSmart Citiesen_US
dc.subjectConcept driften_US
dc.subjectData analysisen_US
dc.subjectDistributed processingen_US
dc.subjectEdge computingen_US
dc.subjectMachine learningen_US
dc.subjectSmart citiesen_US
dc.subjectTime series analysisen_US
dc.titleConcept drift adaptation techniques in distributed environment for real-world data streamsen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/smartcities4010021en_US
dc.identifier.scopus2-s2.0-85108571018-
dcterms.accessRights1en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume4en_US
dc.relation.issue1en_US
dc.identifier.spage349en_US
dc.identifier.epage371en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.subject.fieldEngineering and Technologyen_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusverifieden_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
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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