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Composite Event Recognition (CER) refers to the activity of detecting patterns in streams of continuously arriving "event" data over, possibly geographically, distributed sources. CER is key in Big Data applications that require the processing of such event streams to obtain timely insights and to implement reactive and proactive measures. Examples include the recognition of emerging stories and trends on the Social Web, traffic and transport incidents in smart cities, and epidemic spread. Numerous CER languages have been proposed in the literature. While these systems have a common goal, they differ in their data models, pattern languages and processing mechanisms, resulting in heterogeneous implementations with fundamentally different capabilities. Moreover, we lack a common understanding of the trade-offs between expressiveness and complexity, and a theory for comparing the fundamental capabilities of CER systems. As such, CER frameworks are difficult to understand, extend and generalise. It is unclear which of the proposed approaches better meets the requirements of a given application. Furthermore, the lack of foundations makes it hard to leverage established results - from automata theory, temporal logics, etc - thus hindering scientific and technological progress in CER. The objective of the seminar was to bring together researchers and practitioners working in Databases, Distributed Systems, Automata Theory, Logic and Stream Reasoning; disseminate the recent foundational results across these fields; establish new research collaborations among these fields; thereby start making progress towards formulating such foundations.