  Conformance checking techniques aim to collate observed process behavior with
normative/modeled process models. The majority of existing approaches focuses
on completed process executions, i.e., offline conformance checking. Recently,
novel approaches have been designed to monitor ongoing processes, i.e., online
conformance checking. Such techniques detect deviations of an ongoing process
execution from a normative process model at the moment they occur. Thereby,
countermeasures can be taken immediately to prevent a process deviation from
causing further, undesired consequences. Most online approaches only allow to
detect approximations of deviations. This causes the problem of falsely
detected deviations, i.e., detected deviations that are actually no deviations.
We have, therefore, recently introduced a novel approach to compute exact
conformance checking results in an online environment. In this paper, we focus
on the practical application and present a scalable, distributed implementation
of the proposed online conformance checking approach. Moreover, we present two
extensions to said approach to reduce its computational effort and its
practical applicability. We evaluate our implementation using data sets
capturing the execution of real processes.
