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.

在许多工业场景中，包括异构和分布式数据流以及静态数据的集成和聚合的流分析是一项典型的任务，包括工业物联网的情况，其中将诸如西门子涡轮机的几台工业设备集成到物联网中。OBDA方法具有促进此类任务的巨大潜力。但是，它在处理分析时有许多限制，限制了其在重要工业应用中的使用。我们认为，克服这些限制的一种方法是将OBDA扩展为具有分析能力，来源和成本意识。在这项工作中，我们提出了这样的扩展。特别是，我们为OBDA提出了一种本体，映射和查询语言，其中聚集和其他分析功能是一等公民。此外，我们开发了查询优化技术，可有效处理静态和流数据上的分析任务。我们在系统中实施我们的方法，并使用西门子涡轮数据评估我们的系统。