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

Abstract

the number of data sources presents a challenge in selecting the most appropriate data source to use. The coexistence 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 reexamined 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. The chapter is structured as follows: Discussion in Sect. 15.2 details the challenges of source selection for analytics. Section 15.3 provides an overview of the autonomic source selection approach, including its architecture and the suitability of prediction models. Section 15.4 details the source selection workflow and the criteria for reselection. In Sect. 15.5 we explore the source selection service together with machine learning models in two real-world intelligent systems. The chapter concludes with a summary in Sect. 15.6.