Incremental hierarchical clustering driven automatic annotations for unifying iot streaming data

  1. Sivadi Balakrishna 1
  2. M.Thirumaran 1
  3. Vijender Kumar Solanki 2
  4. Edward Rolando Núñez-Valdez 3
  1. 1 Pondicherry Engineering College
  2. 2 CMR Institute of Technology, Hyderabad, TS (India)
  3. 3 Universidad de Oviedo
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    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

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Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2020

Volumen: 6

Número: 2

Páginas: 56-70

Tipo: Artículo

DOI: 10.9781/IJIMAI.2020.03.001 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: IJIMAI

Resumen

In 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.

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