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dc.contributor.authorNúñez-Valdez, Edward
dc.contributor.authorSolanki, Vijender Kumar
dc.contributor.authorBalakrishna, Sivadi
dc.contributor.authorThirumaran, M
dc.date2020-06
dc.date.accessioned2022-03-28T08:10:35Z
dc.date.available2022-03-28T08:10:35Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/12729
dc.description.abstractIn the Internet of Things (IoT), Cyber-Physical Systems (CPS), and sensor technologies huge and variety of streaming sensor data is generated. The unification of streaming sensor data is a challenging problem. Moreover, the huge amount of raw data has implied the insufficiency of manual and semi-automatic annotation and leads to an increase of the research of automatic semantic annotation. However, many of the existing semantic annotation mechanisms require many joint conditions that could generate redundant processing of transitional results for annotating the sensor data using SPARQL queries. In this paper, we present an Incremental Clustering Driven Automatic Annotation for IoT Streaming Data (IHC-AA-IoTSD) using SPARQL to improve the annotation efficiency. The processes and corresponding algorithms of the incremental hierarchical clustering driven automatic annotation mechanism are presented in detail, including data classification, incremental hierarchical clustering, querying the extracted data, semantic data annotation, and semantic data integration. The IHCAA-IoTSD has been implemented and experimented on three healthcare datasets and compared with leading approaches namely- Agent-based Text Labelling and Automatic Selection (ATLAS), Fuzzy-based Automatic Semantic Annotation Method (FBASAM), and an Ontology-based Semantic Annotation Approach (OBSAA), yielding encouraging results with Accuracy of 86.67%, Precision of 87.36%, Recall of 85.48%, and F-score of 85.92% at 100k triple data.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.ispartofseries;vol. 6, nº 2
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/2757es_ES
dc.rightsopenAccesses_ES
dc.subjectinternet of thingses_ES
dc.subjectclustering; agentses_ES
dc.subjectsensores_ES
dc.subjectsemanticses_ES
dc.subjectautomatic annotationes_ES
dc.subjecthierarchical clusteringes_ES
dc.subjecthealthes_ES
dc.subjectSPARQLes_ES
dc.subjectIJIMAIes_ES
dc.titleIncremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Dataes_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2020.03.001


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