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Autonomic Source Selection for Real-time Predictive Analytics Using the Internet of Things and Open Data

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Abstract

Real-time predictive data analytics is a very important tool for effective decision support within intelligent systems. When making decisions using data, it is critical to use the most appropriate data. When creating predictive analytics, the selection of data sources is important as the quality of the sources influences the accuracy of the predictive model. Within a smart environment, a dataspace is valuable for data scientists as it provides a one-stop shop of all the data required for creating their analytical models: enterprise data, Internet of Things (IoT), sensor data, and open data. However, the increase in the number of data sources presents a challenge in selecting the most appropriate data source to use. The co-existence approach of dataspaces results in them containing much more data sources than within traditional data management approaches. This means that the need to perform source selection is an ongoing activity; as the dataspace is incrementally improved, sources will need to be re-examined to determine their suitability for tasks. We propose an autonomic source selection service for predictive analytics for intelligent systems within a smart environment. This service has been evaluated in real-world settings using a Real-time Linked Dataspace for energy predictions using IoT sensor data and open weather data.

Keywords

  • Source selection
  • Predictive analytics
  • Autonomic computing
  • Decision support
  • Internet of Things
  • Open data
  • Dataspaces

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Correspondence to Edward Curry .

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Arabekar, N., Derguech, W., Burke, E., Curry, E. (2020). Autonomic Source Selection for Real-time Predictive Analytics Using the Internet of Things and Open Data. In: Real-time Linked Dataspaces. Springer, Cham. https://doi.org/10.1007/978-3-030-29665-0_15

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  • DOI: https://doi.org/10.1007/978-3-030-29665-0_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29664-3

  • Online ISBN: 978-3-030-29665-0

  • eBook Packages: Computer ScienceComputer Science (R0)