Online process analysis aims at identifying behavioral regularities or abnormalities in processes in near-real-time from continuous event streams. Yet, its realization is challenging, due to the requirements in terms of scalability and accuracy imposed by processes in Internet-of-Things environments. Against this background, this paper presents an approach for online process analysis that is based on standard models and systems for complex event processing (CEP). We present the “Detect and Conquer” approach that includes generic process templates to accurately capture behavioral regularities or deviations, which are then mapped to CEP queries to achieve their efficient evaluation. We evaluated our approach against synthetic and real-world datasets. The results demonstrate the feasibility and efficiency of our approach.