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Data Compatibility to Enhance Sustainable Capabilities for Autonomous Analytics in IoT

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Listed:
  • Kaleem Razzaq Malik

    (Department of Computer Science and Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
    Department of Computer Science, COMSATS Institute of Information Technology, Sahiwal 57000, Pakistan)

  • Masood Habib

    (Department of Computer Science, COMSATS Institute of Information Technology, Sahiwal 57000, Pakistan)

  • Shehzad Khalid

    (Department of Computer Engineering, Bahria University, Islamabad 44000, Pakistan)

  • Farhan Ullah

    (Department of Computer Science, COMSATS Institute of Information Technology, Sahiwal 57000, Pakistan)

  • Muhammad Umar

    (Department of Computer Science, COMSATS Institute of Information Technology, Sahiwal 57000, Pakistan)

  • Taimur Sajjad

    (Department of Computer Science, COMSATS Institute of Information Technology, Sahiwal 57000, Pakistan)

  • Awais Ahmad

    (Department of Information and Communication Engineering, Yeungnum University, Gyeongsan 38541, Korea)

Abstract

The collection of raw data is based on sensors embedded in devices or the environment for real-time data extraction. Nowadays, the Internet of Things (IoT) environment is used to support autonomous data collection by reducing human involvement. It is hard to analyze such data, especially when working with the sensors in a real-time environment. On using data analytics in IoT with the help of RDF, many issues can be resolved. Resultant data will have a better chance of quality analytics by reforming data into the semantical annotation. Industrial correspondence through data will be improved by the induction of semantics at large due to efficient data capturing, whereas one popular medium of sensors’ data storage is Relational Database (RDB). This study provides a complete automated mechanism to transform from loosely structured data stored in RDB into RDF. These data are obtained from sensors in semantically annotated RDF stores. The given study comprises methodology for improving compatibility by introducing bidirectional transformation between classical DB and RDF data stores to enhance the sustainable capabilities of IoT systems for autonomous analytics. Two case studies, one on weather and another on heart-rate measurement collections through IoT sensors, are used to show the transformation process in operation.

Suggested Citation

  • Kaleem Razzaq Malik & Masood Habib & Shehzad Khalid & Farhan Ullah & Muhammad Umar & Taimur Sajjad & Awais Ahmad, 2017. "Data Compatibility to Enhance Sustainable Capabilities for Autonomous Analytics in IoT," Sustainability, MDPI, vol. 9(6), pages 1-13, May.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:6:p:877-:d:99417
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    References listed on IDEAS

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    2. Steenbeek, Jeroen & Coll, Marta & Gurney, Leigh & Mélin, Frédéric & Hoepffner, Nicolas & Buszowski, Joe & Christensen, Villy, 2013. "Bridging the gap between ecosystem modeling tools and geographic information systems: Driving a food web model with external spatial–temporal data," Ecological Modelling, Elsevier, vol. 263(C), pages 139-151.
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