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 developed as a simple user-friendly process-aware tool to aid a domain expert on manually tagging unplugged processes recordings. The tool allows 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.
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 de ned 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 methodology, and 3) having an underlying process-oriented perspective and treating processes in the tool as rst-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 satis ed all three criteria: POMElog. 2
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 di cult 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 di erent phases of a process. Once clicked, a set expands showing the list of activities associated to it. Each activity has a short name (to easy identify the activity), and a longer and more descriptive name to fully understand when the activity should be identi ed 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 nal 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 nish 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 de ned 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 rst used, i.e., Process-Oriented Medical Education (POME). 3
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 [
Conformance Checking Challenge 2019 [
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 [
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 bene t from being analyzed using Process Mining and other ProcessOriented 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 nish 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 e ort needed. That is not necessary a bottleneck of the approach, and there are scenarios where that could be bene cial (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. . s e r u t a e f d n a g o l E M O P . 2 . g i F