Applications like data processing from RFID, sensor net-
works, algorithmic trading and network management are in-
tuitively mapped in Event-Driven Architectures (EDA), but
have requirements (i.e., throughput, latency, search for com-
plex patterns) that were not available in traditional event-
driven systems. Recently, these requirements have been sat-
isfied by technologies like Complex Event Processing (CEP)
and Event Stream Processing (ESP). In this work, we inves-
tigate a general-purpose CEP/ESP tool named Firefly and
propose solutions for increasing parallelism in event process-
ing. In particular, we focus at the transformation phase of
events, which is responsible for executing computation over
the events before they can be processed by a correlation
engine. We show an approach that statically analyzes rules
and that groups them according to their event dependencies.
Through this approach we extract available parallelism while
still respecting the two correctness requirements: (1) the or-
der of the events in the stream is preserved; and, (2) if there
are dependencies between these events, they are processed
sequentially.