Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)
This work addresses the problem of real-time analytics integration in industrial applications like turbine diagnostics for companies such as Siemens, representing an incremental improvement to OBDA.
The paper tackles the limitations of Ontology-Based Data Access (OBDA) in handling real-time analytics over heterogeneous static and streaming data, proposing an extension that integrates analytical functions as first-class citizens and achieves efficient query processing, as evaluated with Siemens turbine data.
Real-time analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios such as diagnostics of turbines in Siemens. 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. Based on our experience with Siemens, we argue that in order to overcome those limitations OBDA should be extended and 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.