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TABLE III.
DISTRIBUTION OF CLUSTER ELEMENTS
Case ID Timestamp Activity Originator
IM0000004 07/01/2013 08:17 Reassignment TEAM0001
IM0000004 04/11/2013 13:41 Reassignment TEAM0002
IM0000004 04/11/2013 13:41 Update from customer TEAM0002
IM0000004 04/11/2013 12:09 Operator Update TEAM0003
IM0000004 04/11/2013 12:09 Assignment TEAM0003
IM0000004 04/11/2013 13:41 Assignment TEAM0002
D. Interactive Data-aware Heuristic Miner (iDHM)
In the application of the Heuristic Miner algorithm, an
interactive Data-aware Heuristic Miner (iDHM) is used in
ProM to generate the best dependency values automatically.
iDHM provides interactive exploration of the parameter space
and includes a built-in conformance check to diagnose the
quality of the found model. Thus, it is easier to explore large left of the activity to indicate a conformance issue.
parameter spaces and directly find fit problems of the model
found, for example, deviant and missing behavior in event
logs. Furthermore, iDHM uses attribute data from event logs
to reveal conditional dependencies that occur infrequently
[28].
Parameters obtained from iDHM measurements include
the observation frequency threshold of 0.1, dependency mea-
sure threshold of 0.9, binding frequency threshold of 0.1, and
condition quality threshold of 0.5. These parameters are in
accordance with the best results which shows the accuracy
of the parameters in iDHM without performing a number of
dependency variations on the test [10]. The process model is
visualized in C-net with clear semantics.
The process model produced by Heuristic Miner is in
the form of a dependency graph (Fig. 5.). The dependency
relationship between two activities represents the causal de-
pendence of one activity on the other which is represented as
a directed edge between the two activities. Only strong depen-
dencies that exceed the configured dependency threshold and
are observed more frequently than the included configuration
observation frequency threshold. The data perspective is taken
into account when discovering the control flow of a process.
Classification techniques are used to reveal data dependen-
cies between activities. These data dependencies are used to
distinguish random noise from conditional dependencies that
occur infrequently. The technique is based on a control flow
perspective and ignores infrequent behavior such as noise.
Conditional dependencies can provide insight for process
analysts as they can indicate solutions and deviations from
normal process behavior. iDHM only includes conditional
dependencies whose underlying data state quality exceeds
the configured state threshold. Disconnected points represent
exclusive splits (XOR) and connected points represent parallel
splits (AND). Decision rules can be filtered based on the
decision rule quality threshold. C-Net conformance checking
Andreswari, Millenia, Rizky, Haniyah & Mufti
Fig. 5. Heuristic Miner process model
techniques and event logs to project frequency on activities
and bindings. Activities and bindings are color coded based
on how often they occur according to the event log. The size
of the binding dots also scales with the frequency with which
they occur. A small circle of the respective color to the top
The process model obtained shows the dependence of as-
signments and status changes on open activities. There are
compatibility issues with update, assignment, and operator up-
date activities. This is because the Heuristic Miner trims some
data with low frequency to be displayed on the process model,
while in the process model generated by Fuzzy Miner it is
known that the assignment has a large number of relationships
with other activities.
E. Enhancement
The enhancement process is carried out by analyzing the
originator. Based on the results of conformance, it is known
that the process model shows a tendency for workloads in
the assignment process, so an analysis is carried out on the
division of teams to make assignments to the process. The
analysis is done by knowing the division of workload for each
team in the assignment process. Based on the event log data
used, there were 88,502 assignment activities performed.
The distribution of the frequency of assignment activities
carried out for each team shows that there is an imbalance
of tasks in the assignment process (Fig. 6). Team008 does
more assignments than Team0241. This causes the services
provided by Rabobank Group ICT to be less than optimal due
to the uneven workload. For this reason, it is recommended
to pay more attention to the division of workload on each IT
service desk/operations team so that the services provided
are more optimal and increase customer trust so that they can
increase the amount of marketing.