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

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.

Authors and Affiliations

1. Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland

   Ninad Arabekar, Wassim Derguech & Eanna Burke

2. National University of Ireland Galway, Galway, Ireland

   Edward Curry

Corresponding author

Correspondence to Edward Curry .

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and permissions

© 2020 The Author(s)

Policies and ethics