With the broader dissemination of digital technologies, visionary concepts like the Internet of Things also affect an increasing number of use cases with interfaces to humans, e.g. manufacturing environments with technical operators monitoring the processes. This leads to additional challenges, as besides the technical issues also human aspects have to be considered for a successful implementation of strategic initiatives like Industrie 4.0. From a technical perspective, complex event processing has proven itself in practice to be capable of integrating and analyzing huge amounts of heterogeneous data and establishing a basic level of situation awareness by detecting situations of interests. Whereas this reactive nature of complex event processing systems may be sufficient for machine-to-machine use cases, the new characteristic of application fields with humans remaining in the control loop leads to an increasing action distance and delayed reactions. Taking human aspects into consideration leads to new requirements, with transparency and comprehensibility of the processing of events being the most important ones. Improving the comprehensibility of complex event processing and extending its capabilities towards an effective support of human operators allows tackling technical and non-technical challenges at the same time.
The main contribution of this thesis answers the question of how to evolve state-of-the-art complex event processing from its reactive nature towards a transparent and holistic situation management system. The goal is to improve the interaction among systems and humans in use cases with interfaces between both worlds. Realizing a holistic situation management requires three missing capabilities to be introduced by the contributions of this thesis: First, based on the achieved transparency, the retrospective analysis of situations is enabled by collecting information related to a situation's occurrence and development. Therefore, CEP engine-specific situation descriptions are transformed into a common model, allowing the automatic decomposition of the underlying patterns to derive partial patterns describing the intermediate states of processing. Second, by introducing the psychological model of situation awareness into complex event processing, human aspects of information processing are taken into consideration and introduced into the complex event processing paradigm. Based on this model, an extended situation life-cycle and transition method are derived. The introduced concepts and methods allow the implementation of the controlling function of situation management and enable the effective acquisition and maintenance of situation awareness for human operators to purposefully direct their attention towards upcoming situations. Finally, completing the set of capabilities for situation management, an approach is presented to support the generation and integration of prediction models for predictive situation management. Therefore, methods are introduced to automatically label and extract relevant data for the generation of prediction models and to enable the embedding of the resulting models for an automatic evaluation and execution. The contributions are introduced, applied and evaluated along a scenario from the manufacturing domain.