In order to obtain timely insights and implement reactive and proactive measures, many contemporary applications require reasoning about actions and events over streams of continuously arriving data. For example, in a wide range of applications, critical activities are formalised as events that have to be detected in real-time, or even forecast ahead of time. Examples include the recognition of attacks on computer network nodes, human activities on video content, emerging stories and trends on the Social Web, traffic and transport incidents in smart cities, error conditions in smart energy grids, violations of maritime regulations, detecting cardiac arrhythmia, and tracking epidemic spread. In each application, reasoning about events and actions allows one to make sense of streaming data, react accordingly and prepare for counter-measures.
Recent years have witnessed increased activity in diverse fields of Computer Science on topics related to reasoning about actions and events over data streams: temporal representation and reasoning, temporal logic, action languages, commonsense reasoning, reasoning under uncertainty, online relational learning, distributed reasoning, incremental reasoning, theoretical complexity results related to processing database queries under updates, expressiveness and complexity of logics in dynamic settings, and so on.