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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. [ABSTRACT FROM AUTHOR]

We present a description and analysis of the data access challenge in Siemens Energy. We advocate Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. The platform is based on a number of novel techniques and components including a deployment module, BootOX for ontology and mapping bootstrapping, a query language STARQL that allows for a uniform querying of both streaming and static data, a highly optimised backend, ExaStream, for processing such data, and a query formulation interface, OptiqueVQS, that allows to formulate STARQL queries without prior knowledge of its formal syntax. Finally, we describe our installation and evaluation of the platform in Siemens. [ABSTRACT FROM AUTHOR]