Abstract: Process mining provides ways to analyze business
processes. Common process mining techniques consider the process
as a whole. However, in real-life business processes, different
behaviors exist that make the overall process too complex to interpret.
Process comparison is a branch of process mining that isolates
different behaviors of the process from each other by using process
cubes. Process cubes organize event data using different dimensions.
Each cell contains a set of events that can be used as an input to apply
process mining techniques. Existing work on process cubes assume
single case notions. However, in real processes, several case notions
(e.g., order, item, package, etc.) are intertwined. Object-centric
process mining is a new branch of process mining addressing multiple
case notions in a process. To make a bridge between object-centric
process mining and process comparison, we propose a process cube
framework, which supports process cube operations such as slice and
dice on object-centric event logs. To facilitate the comparison, the
framework is integrated with several object-centric process discovery
approaches.
Abstract: Nowaday-s, many organizations use systems that
support business process as a whole or partially. However, in some
application domains, like software development and health care
processes, a normative Process Aware System (PAS) is not suitable,
because a flexible support is needed to respond rapidly to new
process models. On the other hand, a flexible Process Aware System
may be vulnerable to undesirable and fraudulent executions, which
imposes a tradeoff between flexibility and security. In order to make
this tradeoff available, a genetic-based anomaly detection model for
logs of Process Aware Systems is presented in this paper. The
detection of an anomalous trace is based on discovering an
appropriate process model by using genetic process mining and
detecting traces that do not fit the appropriate model as anomalous
trace; therefore, when used in PAS, this model is an automated
solution that can support coexistence of flexibility and security.
Abstract: The running logs of a process hold valuable
information about its executed activity behavior and generated activity
logic structure. Theses informative logs can be extracted, analyzed and
utilized to improve the efficiencies of the process's execution and
conduction. One of the techniques used to accomplish the process
improvement is called as process mining. To mine similar processes is
such an improvement mission in process mining. Rather than directly
mining similar processes using a single comparing coefficient or a
complicate fitness function, this paper presents a simplified heuristic
process mining algorithm with two similarity comparisons that are
able to relatively conform the activity logic sequences (traces) of
mining processes with those of a normalized (regularized) one. The
relative process conformance is to find which of the mining processes
match the required activity sequences and relationships, further for
necessary and sufficient applications of the mined processes to process
improvements. One similarity presented is defined by the relationships
in terms of the number of similar activity sequences existing in
different processes; another similarity expresses the degree of the
similar (identical) activity sequences among the conforming processes.
Since these two similarities are with respect to certain typical behavior
(activity sequences) occurred in an entire process, the common
problems, such as the inappropriateness of an absolute comparison and
the incapability of an intrinsic information elicitation, which are often
appeared in other process conforming techniques, can be solved by the
relative process comparison presented in this paper. To demonstrate
the potentiality of the proposed algorithm, a numerical example is
illustrated.