  Distributed Complex Event Processing (DCEP) is a paradigm to infer the
occurrence of complex situations in the surrounding world from basic events
like sensor readings. In doing so, DCEP operators detect event patterns on
their incoming event streams. To yield high operator throughput, data
parallelization frameworks divide the incoming event streams of an operator
into overlapping windows that are processed in parallel by a number of operator
instances. In doing so, the basic assumption is that the different windows can
be processed independently from each other. However, consumption policies
enforce that events can only be part of one pattern instance; then, they are
consumed, i.e., removed from further pattern detection. That implies that the
constituent events of a pattern instance detected in one window are excluded
from all other windows as well, which breaks the data parallelism between
different windows. In this paper, we tackle this problem by means of
speculation: Based on the likelihood of an event's consumption in a window,
subsequent windows may speculatively suppress that event. We propose the
SPECTRE framework for speculative processing of multiple dependent windows in
parallel. Our evaluations show an up to linear scalability of SPECTRE with the
number of CPU cores.
