In event stream applications, events flow through a net-
work of components that perform various types of opera-
tions, e.g., filtering, aggregation, transformation. When the
operation only depends on the input events, one can triv-
ially parallelize its processing by replicating the associated
components. This is not possible, however, with stateful
components or when there exist dependencies between the
events. Parallel versions of a number of simple stream min-
ing operators have been designed, but, in general, complex
and user-defined operators are limited by single thread per-
formance. In this paper, we propose leveraging the pro-
cessing capabilities of multi-core processors to improve the
efficiency of stateful components using optimistic paralleliza-
tion techniques (as provided by transactional memory). We
show that, even though some speculative event executions
might need to be disregarded, the overall throughput in-
creases noticeably in the general case and latency can be
reduced by pre-processing out-of-order events. Moreover,
we show how simple conflict predictors can boost the paral-
lelism even more and reduce the amount of resources used
for a given level of parallelism.