Complex Event Processing Under Uncertainty Using Markov Chains, Constraints, and Sampling

For the last two decades, complex event processing under uncertainty has been widely studied, but, nowadays, researchers are still facing difficult problems such as combinatorial explosion or lack of expressiveness while inferring about possible outcomes. Numerous approaches have been proposed, like automaton-based methods, stochastic context-free grammars, or mixed methods using first-order logic and probabilistic graphical models. Each technique has its own pros and cons, which rely on the problem structure and underlying assumptions. In our case, we want to propose a model providing the probability of a complex event from long data streams produced by a simple, but large system, in a reasonable amount of time. Furthermore, we want this model to allow considering prior knowledge on data streams with a high degree of expressiveness.