02330nas a2200301   4500000000100000000000100001008004100002260001200043653002300055653001500078653001100093653001100104653001400115653002500129653002800154653001100182653001100193100002600204700002700230700002300257700001700280245010100297856008200398300000700480490000600487520152100493022001402014       2020                            d  c06/202010aInternet of things10aClustering10aAgents10aSensor10aSemantics10aAutomatic Annotation10aHierarchical Clustering10aHealth10aSPARQL1 aEdward Núñez-Valdez1 aVijender Kumar Solanki1 aSivadi Balakrishna1 aM Thirumaran00aIncremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data  uhttps://www.ijimai.org/journal/sites/default/files/2020-05/ijimai_6_2_7_0.pdf  a150 v63 aIn 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.  a1989-1660