In recent years, there has been an increased need for the use of active systems - systems required to act automatically based on events, or changes in the environment. Such systems span many areas, from active databases, through applications that drive the core business processes of today’s enterprises. However, in many cases, the events to which the system must respond are not generated by monitoring tools, but must be inferred from other events based on complex temporal predicates. In addition, in many practical applications, such inference is inherently
uncertain. In this paper, we introduce a formal framework for knowledge representation and reasoning enabling such event inference. Based on probability theory, we define the representation of the associated uncertainty. In addition, we formally define the probability space, and show how the relevant probabilities can be calculated by dynamically constructing a Bayesian network. To the best of our knowledge, this is the first work that enables taking such uncertainty into account in the context of active systems. Therefore, our contribution is twofold: We formally de-
…ne a probabilistic representational model for event composition, and show how to apply this model to the quanti…cation of the occurrence probability of events. This results in a framework enabling any active system to
handle such uncertainty.