14. The method of claim 12 , the method further comprises: generating a closed-world submodel based upon the observed data, the closed-world submodel being used to generate the probabilities of previously observed events recurring; and generating an open-world submodel by evaluating a likelihood of observing the non-observed data over a future period of time, the open-world submodel being used to generate the probabilities of previously non-observed events occurring.
12. A method that facilitates handling incompleteness of data sets to enable generating a prediction, comprising: with at least one processor: receiving observed data; computing, based on the observed data, probabilities of previously observed events recurring; and extrapolating the observed data to yield probabilities related to non-observed events; and generating a prediction of an event based at least in part upon a combination, the combination being a combination of the probabilities related to the non-observed events and the previously observed events, wherein the probabilities are weighted in forming the combination by a dynamic factor to place greater emphasis on probabilities related to the previously observed events when the amount of received data increases.