A Model for Reasoning with Uncertain Rules in Event Composition Systems
  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 to
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 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.
herefore, our contribution is twofold: We formally define the representation
and semantics of event composition for probabilistic settings, and show how to
apply these extensions to the quantification of the occurrence probability of
events. These results enable any active system to handle such uncertainty.
