An ontology-mediated analytics-aware approach to support monitoring and diagnostics of static and streaming data

Abstract

Streaming analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios including the case of industrial IoT where several pieces of industrial equipment such as turbines in Siemens are integrated into an IoT. The OBDA approach has a great potential to facilitate such tasks; however, it has a number of limitations in dealing with analytics that restrict its use in important industrial applications. We argue that a way to overcome those limitations is to extend OBDA to become analytics, source, and cost aware. In this work we propose such an extension. In particular, we propose an ontology, mapping, and query language for OBDA, where aggregate and other analytical functions are first class citizens. Moreover, we develop query optimisation techniques that allow to efficiently process analytical tasks over static and streaming data. We implement our approach in a system and evaluate our system with Siemens turbine data.