How to observe real-world events through a distributed world model

In this paper we propose an approach for observing real-world events through a distributed model providing dynamic data about the state of the world. Typical examples for real-world events are spatial events, i.e. events that occur when a user enters a certain spatial relationship with other users or his environment. To the best of our knowledge, this paper is the first to propose a uniform approach for observing real-world events that takes the issues related to the distribution of the model into account. The state of the world is only available with limited accuracy in both the value and time dimension, which is due to sensor inaccuracy and the properties of the distributed system. Therefore, it is not always possible to determine for certain, if an event has occurred. We propose to calculate the probability with which an event has occurred. The event is considered to have occurred when the calculated probability is above a threshold probability specified by the user. To realize this approach, we show which system parameters influence the observation, how update protocols provide the data to the observer model, on which the event is actually observed, and how the probability that an event has occurred can be calculated based on this model.