=Paper=
{{Paper
|id=Vol-2420/papeDT15
|storemode=property
|title=POMElog: Generating Event Logs from Unplugged Processes
|pdfUrl=https://ceur-ws.org/Vol-2420/papeDT15.pdf
|volume=Vol-2420
|authors=Luis Leiva,Jorge Munoz-Gama,Juan Salas-Morales,Victor Galvez,Wai Lam Jonathan Lee,Rene de La Fuente,Ricardo Fuentes,Marcos Sepúlveda
|dblpUrl=https://dblp.org/rec/conf/bpm/LeivaMSGLFFS19
}}
==POMElog: Generating Event Logs from Unplugged Processes==
https://ceur-ws.org/Vol-2420/papeDT15.pdf
POMElog: Generating Event Logs from
Unplugged Processes
Luis Leiva1 , Jorge Munoz-Gama1 , Juan Salas-Morales1 , Victor Galvez1 ,
Wai Lam Jonathan Lee1 , Rene de la Fuente2 , Ricardo Fuentes2 , and
Marcos Sepúlveda1
1
Department of Computer Science, School of Engineering
2
Department of Anesthesiology, School of Medicine
Pontificia Universidad Católica de Chile, Santiago, Chile
Abstract. We consider unplugged processes to be those processes that
are not supported by any information system. In order to analyze them
using Process Mining techniques, those processes could be video recorded
and then watch the recordings to create an event log. POMElog was de-
veloped as a simple user-friendly process-aware tool to aid a domain
expert on manually tagging unplugged processes recordings. The tool al-
lows to create events, update times, and export the event logs generated.
Also, the tool has been used in real cases, specially to generate event logs
for surgical procedures in the medical education domain.
Keywords: process mining, log generation, event logs, PODS4H
1 Process Mining for Unplugged Processes
Process Mining and other Process-Oriented Data Science techniques have been
widely applied to processes supported by an information system that records
event data about their execution. This event data is extracted from the informa-
tion system database, and an Event Log is created to be used by process mining
commercial or open-source tools. However, there are other processes that could
benefit from a similar analysis, but that are not supported by any information
system, and therefore, there is no database with event data stored on it. Exam-
ples of those processes are manual tasks, such as the repair of an air conditioning
system or a surgical procedure. We call the former plugged processes and the lat-
ter unplugged processes.
In [1], the authors propose a method to analyze unplugged processes using
process mining. In this particular case, the processes were surgical procedures
in the context of a medical training course. The method proposes to record the
unplugged processes, then to watch them, and manually tag the activities that
happened in the moment they occurred. This tagging was subsequently exported
in terms of an Event Log in order to analyze it using Process Mining tools.
Figure 1 shows a comparison of the proposed approach for unplugged processes
and the classic one for plugged processes.
2 Leiva, Munoz-Gama, Salas-Morales, Galvez, et al.
Fig. 1. Process Mining for plugged processes (left) and unplugged processes (right).
The key step of the method was the tagging procedure. It was necessary to
have a tool that aided the domain expert on the tagging process. The criteria
defined for the tool were: 1) user-friendly for non-computing related domain
experts (e.g., a doctor in this case), 2) simple since domain experts could get
overwhelm with options and menus, and reject the use of the whole method-
ology, and 3) having an underlying process-oriented perspective and treating
processes in the tool as first-class citizen, incorporating the notion of event log,
activities, instances, among others. Pilot studies were developed with tools from
other domains, e.g., VCode VData. However, they were far from intuitive for
non academic experts, and they were not process-oriented friendly. Finally, we
decided to develop our own tool that satisfied all three criteria: POMElog.
2 POMElog and its Features
In this section, we introduce the main features of POMElog. Figure 2 shows
where are these features in the tool interface:
Interact with the recorded processes: The tool allows to interact with
the video in a common way, i.e., play and stop the video, move forward and
backwards, mute and allow sound, and to display the video in full-screen to
observe the details in a more clear way. See area B in Figure 2. POMElog allows
to decrease the speed of the video, to analyze the difficult parts that require a
slow video but without having to stop it, and to increase the speed to identify
activities in processes with a high sparsity of activities. See D in Figure 2.
Walk through the list of activities: Giving an unplugged process and
its corresponding set of activities, the tool allows the user to walk through the
list of activities. For an easy and more direct access to the activities, those are
grouped in sets (with an intuitive name and a color). For example, those sets
could correspond to different phases of a process. Once clicked, a set expands
showing the list of activities associated to it. Each activity has a short name
POMElog: Generating Event Logs from Unplugged Processes 3
(to easy identify the activity), and a longer and more descriptive name to fully
understand when the activity should be identified in the video. See A in Figure 2.
Adding a new event: When an activity is clicked, it is directly added to
the event log (see area C in Figure 2), with the time (start and complete) the
video was at the time of clicking. The video can be played or stopped (for more
precision) at the time of the event creation. Events are displayed in ascending
order by the time of their creation, to facilitate access for the user. Events can
be easily removed clicking the rubbish bin icon.
Updating start and end time: That is probably one of the most intuitive
and appreciated feature by the final users, specially compared with other tools.
Clicking on the time (start or complete) of an activity will update the time with
the video time in the moment of the click. A common action is to create the
event at the start of the activity, and wait for it to finish to click on the end
time to update it. Notice that, unlike some tools, POMElog interface has no
drag-and-drop bars for each activity that would over-complicate the interface.
Event log export: Finally, POMElog allows to export the events in terms
of an Event Log (currently in CSV and XES in the future). The tool allows both
to export it at the activity level (the more classic approach), or at the set/phase
level, where the activities of the Event log are defined by the set/phase of the
event and not the activity of the event. The latter is useful for a high-level
analysis of the process. See E in Figure 2.
Notice that, in Section 4, there is a link to a video and a demo of the tool,
where the features could be better understood. POMElog was developed as an
user-friendly intuitive simple tool to aid domain experts on generating event logs
from manually tagged recordings of unplugged process executions. Notice that,
the name of the tool, POMElog, derives from the domain where it was first used,
i.e., Process-Oriented Medical Education (POME).
3 Maturity and Case Studies of POMElog
POMElog is a mature tool used in a larger software suite for unplugged processes
in the medical education domain. Consequently, there are several cases studies:
Tailored Process Feedback [1]: In [1] the authors proposed an approach to
provide Tailored Process Feedback for Surgical Procedures in Medical Training.
In this case, the authors analyzed videos of the execution of central venous
catheter placements on a medical phantom by a group of students on a surgical
training course. Event logs were generated from the videos using POMElog, and
latter used as input for the process mining tool Celonis to create meaningful
process-oriented reports.
Conformance Checking Challenge 2019 [2]: In 2019, the Conformance
Checking Challenge 2019 (CCC19) was proposed as part of the 1st International
Conference on Process Mining. In this challenge, the participants were provided
with a model and a log, being the challenge to provide a meaningful conformance
report on the case. In that edition, the event log provided was generated using
POMElog.
4 Leiva, Munoz-Gama, Salas-Morales, Galvez, et al.
4 Screencast and Availability
As it was mentioned, POMElog was developed as a tool to be used in a larger suit
called POME (Process-Oriented Medical Education), a web system developed
to completely incorporate the process perspective into the training of surgical
skills. POME allows the creation of courses, grades, users, activities, and roles.
Given the nature of the information contained in POME and the non-disclosure
agreements (NDA) considered in the project, in this demo paper we provide
a proof-of-concept prepared version of POMElog. This version is available for
reviewers and readers in https://pome-icpm.firebaseapp.com. It contains a
pre-loaded set of activities and a single video to tag. To illustrate the maturity
of the tool, both the set of activities (in Spanish) and the video corresponds to
a real medical education case used in a real analysis [1]. Additionally, there is a
screencast in https://youtu.be/IJKbrFhwq2A showing the main features.
5 Conclusion
This paper presented POMElog, a tool to generate event logs from unplugged
processes, i.e., processes that are not supported by an information system, but
that could benefit from being analyzed using Process Mining and other Process-
Oriented Data Science techniques. The tool visualizes video recordings of such
unplugged processes executions, and allows the user to manually tag activities
according to when they start and finish in the video. The tagging can latter be
exported in terms of an Event Log to be analyzed with state-of-the-art Process
Mining tools. Notice that, POMElog was designed to aid in the manual (not
automatic) tagging for a domain expert, reducing the time and effort needed.
That is not necessary a bottleneck of the approach, and there are scenarios where
that could be beneficial (e.g., pedagogically, medical students tagging they own
video increases the understanding of their mistakes). Despite that, POMElog
could include future features that automatically aid in that manual tagging. For
example, suggesting/predicting an activity (or a small set) to be included next,
based on previous taggings of the same processes.
Acknowledgments. CONICYT FONDECYT 11170092, CONICYT REDI 170136,
VRI-UC Interdisciplinary II170003, FOND-DCC 2017-0001, CONICYT-PFCHA
/ Doctorado Nacional / 2017-21170612 and 2019-21190116.
References
1. Lira, R., et al.: Process-oriented feedback through process mining for surgical proce-
dures in medical training: The ultrasound-guided central venous catheter placement
case. Int. J. Environ. Res. Public Health 16(11) (2019)
2. Munoz-Gama, J., , Garcia-Bañuelos, L., Armas-Cervantes, A.: Conformance Check-
ing Challenge 2019 (CCC19). In: ICPM, Aachen, Germany, June 24-26, 2019 (2019)
POMElog: Generating Event Logs from Unplugged Processes 5
D
E
Fig. 2. POMElog and features.
B
C
A