Complex Event Processing as an Approach for realtime Analytics in industrial Environments Realtime analysis of Internet of Things sensor data is crucial for players in the industrial sector for staying competitive. That is, why highly capable and easy to integrate solutions are needed for this, which can be deployed close to the data sources. In this paper, we argue that Complex Event Processing (CEP), which is a modeldriven data analytics approach, is such a technique. CEP is able to achieve high throughput of data without the need of the computing power available in modern cloud infrastructures, while producing semantically higher value data in real time.Our here presented solution using CEP is easily integrated, scalable and capable of processing big amounts of data while giving semantic assurances through meta data modeling. Users of our solution do not need to learn any languages to model patterns, but can do that with an intuitive, graphical approach running on mobile devices, which makes it a good fit for domain experts working in industrial environments today.Solutions like the one presented in this paper can be a keyenabler for new business models in the industrial sector and smart factories.

pSPICE: Partial Match Shedding for Complex Event Processing. Complex event processing (CEP) systems continuously process input event streams to detect patterns. Over time, the input event rate might fluctuate and overshoot the systemu0027s capabilities. One way to reduce the overload on the system is to use load shedding. In this paper, we propose a load shedding strategy for CEP systems which drops a portion of the CEP operatoru0027s internal state (a.k.a. partial matches) to maintain a given latency bound. The crucial question here is how many and which partial matches to drop so that a given latency bound is maintained while minimizing the degradation in the quality of results. In the stream processing domain, different load shedding strategies have been proposed that mainly depend on the importance of individual tuples. However, as CEP systems perform pattern detection, the importance of events is also influenced by other events in the stream. Our load shedding strategy uses Markov chain and Markov reward process to predict the utilityimportance of partial matches to determine the ones to be dropped. In addition, we represent the utility in a way that minimizes the overhead of load shedding. Furthermore, we provide algorithms to decide when to start dropping partial matches and how many partial matches to drop. By extensively evaluating our approach on three realworld datasets and several representative queries, we show that the adverse impact of our load shedding strategy on the quality of results is considerably less than the impact of stateoftheart load shedding strategies.