=Paper=
{{Paper
|id=Vol-3783/paper_357
|storemode=property
|title=A Taste of the Different Flavors of Tiramisù
|pdfUrl=https://ceur-ws.org/Vol-3783/paper_357.pdf
|volume=Vol-3783
|authors=Anti Alman,Alessio Arleo,Iris Beerepoot,Andrea Burattin,Claudio Di Ciccio,Manuel Resinas
|dblpUrl=https://dblp.org/rec/conf/icpm/AlmanABBCR24
}}
==A Taste of the Different Flavors of Tiramisù==
https://ceur-ws.org/Vol-3783/paper_357.pdf
A Taste of the Different Flavors of Tiramisù
Anti Alman1,∗,† , Alessio Arleo2,∗,† , Iris Beerepoot3,∗,† , Andrea Burattin4,∗,† ,
Claudio Di Ciccio3,∗,† and Manuel Resinas5,∗,†
1
University of Tartu, Estonia
2
Eindhoven University of Technology, The Netherlands
3
Utrecht University, The Netherlands
4
Technical University of Denmark, Kgs. Lyngby, Denmark
5
Universidad de Sevilla, Spain
Abstract
The amalgamation of process mining and visual analytics holds the promise for a fruitful new frontier of
process exploration, where both fields provide crucial ingredients to the resulting delicacy. The field of
process mining brings the concepts of events, traces, and process thinking in general, while the field
of visual analytics brings the idea of interactive and cyclical analysis, accounting for the expectations
and desires of specific domains and the individuals therein. In this paper, we focus on one such delicacy,
aptly named the Tiramisù framework, presenting two flavors of the corresponding multi-layered recipe.
More specifically, we build on our earlier work to provide refined servings of Tiramisù for the healthcare
and the personal informatics domain, with both being taste-tested by the end users, and the latter being
significantly enhanced as a result.
Keywords
Process mining, Visual analytics, Knowledge-intensive processes, Visualization
Metadata description Value
Tool name Tiramisù
Current version 1.0
Legal code license Apache 2.0
Languages, tools and services used VueJS, Python, Streamlit
Supported operating environment Microsoft Windows, GNU/Linux, Mac
Download/Demo URL https://tiramisuframework.github.io/healthcare/,
https://tiramisu-calendar.streamlit.app
Documentation/Source code repository https://github.com/tiramisuframework/healthcare,
https://github.com/tiramisuframework/tiramisu-calendar
Screencast video https://www.youtube.com/watch?v=WoTT85I9-sI
1. Introduction
Process mining aims at extracting information from events recorded by information systems,
with the ultimate goal to provide insights into the executed processes based on the analysed
ICPM 2024 Tool Demonstration Track, October 14-18, 2024, Kongens Lyngby, Denmark
∗
Corresponding author.
†
These authors contributed equally.
Envelope-Open anti.alman@ut.ee (A. Alman); a.arleo@tue.nl (A. Arleo); i.m.beerepoot@uu.nl (I. Beerepoot); andbur@dtu.dk
(A. Burattin); claudio.diciccio@uniroma1.i (C. D. Ciccio); resinas@us.es (M. Resinas)
Orcid 0000-0002-5647-6249 (A. Alman); 0000-0003-2008-365 (A. Arleo); 0000-0002-6301-9329 (I. Beerepoot);
0000-0002-0837-0183 (A. Burattin); 0000-0001-5570-0475 (C. D. Ciccio); 0000-0002-0837-0183 (M. Resinas)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
data [1]. Event data may vary based on the process domain, and typically covers different
dimensions (control flow, time, resources, object lifecycles). A critical, yet largely unaddressed,
issue of process mining is the inability to navigate through distinct, and possibly domain-specific,
dimensions [2]. This limitation reportedly hinders process improvement investigation [3], as it
narrows the analysis scope to a partial view over an inherently complex search space, which is
typical of knowledge-intensive processes [4, 5]. In the discipline of visual analytics, the problem
of representing complex, multi-faceted phenomena has been extensively studied [6]. This is
also the type of phenomena often encountered within real-life business processes.
In previous work, we thus introduced Tiramisù [7, 8], a novel conceptual framework lever-
aging visual analytics principles [6] to enhance process mining with an interactive investigation
of data integrating process-specific and context-related information. Tiramisù is designed in a
pluri-layered fashion, to equip end users with a context-aware visualization mixing classical
process mining representation elements —such as workflow nets or directly-follows graphs [1]—
with additional diagrams and visual cues tailored for context-variable and metadata representa-
tions (e.g., timelines and calendars for time, geographical or floor maps for space).
In this demo paper, we illustrate Tiramisù ’s core ingredients and two variations of the recipe,
i.e., prototypical implementations grounding its components into usable toolkits.
2. Recipe (Architecture)
Figure 1 illustrates the layered architecture of Tiramisù
Tiramisù , a framework consisting of a back-
drop providing context, and one or more di-
mension layers. From a visualization stand- Overlay
⋮
point, the backdrop establishes a common
context for the other layers in our framework.
On-demand
This backdrop might serve as a spatial ref- ⋮
erence, such as a geographic location or the
layout of a building (see Sect. 3.1), or as a tem- Overlay
poral reference, like a calendar (see Sect. 3.2).
In the tiramisù metaphor, the backdrop is akin
to the cream that infuses all the layers of the
dessert. Superimposed layers can represent Backdrop Layers ↑
both process-related and non-process-related
Figure 1: The Tiramisù architecture at large
data. We distinguish between the following
(the dessert drawings were generated
two types of dimension layers: Overlay lay-
with the assistance of AI)
ers enhance the visualization by mapping
data onto new layers that are placed over the backdrop; On-demand layers provide ad-
ditional contextual information for individual elements or small groups of elements. These
layers can be activated by interactions such as hovering over or clicking on elements of the
backdrop or those representing the process. Layers can encapsulate the behavior of the process
being studied, similar to how the coffee-soaked ladyfingers provide structure and texture to the
dessert. Overlay layers explicitly anchor to the backdrop, thus contextualizing the information.
For example, in case of a floor plan, the data on overlay layers would directly link to that floor,
facilitating the user’s understanding of how events are related within the same location.
3. Serving Suggestions (Functionality)
Here we describe two implementations of Tiramisù , focused on different classes of knowledge-
intensive processes. Their names are inspired by variations of the tiramisù dessert: Berries,
pertaining to healthcare [9]: and Banana, focusing on personal information management [10].
3.1. Berries (Behavioral Deviation Analysis in Healthcare)
The first instance of Tiramisù has been de-
signed to help understanding deviations from
personal behavior in the context of health-
care. In this case, in particular, we can ana-
lyze whether the daily routines of a person
in a nursing home are maintained over time
or not (as possible symptoms of worsening of
neurodegenerative diseases). Simply apply-
ing existing process mining techniques to this
task might result in basic process maps where
nodes represent various patient activities and Figure 2: Screenshot of Tiramisù application
arcs indicate dependencies or temporal con- depicting a morning routine.
straints among these activities [11].
The system , which is designed to emphasize the spatial dimension of processes and activities
happening in different locations, is implemented as a JavaScript application using the Vue.js
framework1 . A screenshot of it is reported in Fig. 2. It takes a URL of a JSON configuration file
and a DFG file describing the process as input. The configuration file specifies the backdrop as
the floor map and the placement of different activity representations on top of it.
3.2. Banana (Personal Work Process Analysis)
The second tool we have developed following the Tiramisù recipe has been built to provide
insights about personal work processes. For instance, for an academic, her personal work
processes performed during her daily work might involve writing a paper, preparing a course,
reviewing research papers, or supervising a PhD student, among many other processes. As can
be observed by these examples, depending on the characteristics of the work, personal work
processes can be knowledge-intensive and significantly unstructured, which means they are
well-suited to benefit from the characteristics of the Tiramisù architecture [8].
To analyze personal work processes, the tool must be provided with collected personal
information in the form of an event log that can be analyzed using process mining techniques.
There are several techniques that can be used to collect personal information such as timesheet
1
https://vuejs.org/
Figure 3: Screenshot of Tiramisù calendar depicting the Active Window Tracking data against the
calendar backdrop and showing the details of an interval using the details-on-demand principle.
techniques or screen recordings, each with their own advantages and disadvantages [12]. Our
tool supports two types of data, namely: Active Window Tracking data and calendar data.
Active Window Tracking data [13] can be seen as an event log that records the active window
in a computer at any moment in time. Each event includes the title of the active window, the
name of the corresponding app, and the timestamps when the window became active and
stopped being active. There are several different tools that record this information. One of
them is Tockler,2 which is open source and stores all data locally, avoiding privacy concerns.
We also assume that the user has labelled each event in the log with information about (i) the
activity that was being done at that moment, e.g., conducting research, preparing lectures, etc.,
and (ii) the case the activity belongs to, e.g., the ICPM Tiramisù demo paper. The Worktagger
tool3 can be used to support the user in the labelling task. We have chosen this type of data
because it has proven to be very useful to obtain insights [13, 14] from personal work processes.
Calendar data can be obtained from any calendar management application and it involves the
events scheduled in one’s calendar including the start and end time, and the title of the event.
Since the intention of the tool is to give the user insights into a working day and how
it develops, we have chosen the calendar as the backdrop. A calendar is a very intuitive
visualization for every type of user, which is another reason why it is a good choice as a
backdrop. Against this backdrop, both the Active Window Tracking data and the Calendar data
are visualized as different layers. At this moment, the tool shows Active Window Tracking data
all the time, while allows the user to show or hide Calendar data.
Figure 3 depicts a screenshot of the Tiramisù calendar. Active Window Tracking data is
2
https://maygo.github.io/tockler/
3
https://github.com/project-pivot/worktagger
visualized on the left-hand side on top of the calendar backdrop. The activities are represented
by boxes, titled as: [activity name] - [case name]. The position and size of each box represents
when the activity started and its duration, respectively. The red buttons above the calendar
support common calendar functionalities such as navigating to different time periods and
choosing the time period length (or showing all of the activities in a list view). The checkbox
labelled Include calendar data allows the user to show or hide the calendar data layer on top of
the backdrop. The Active Window Tracking data cannot be hidden, but it is possible to show
only the activities selected in the Filter by activity drop box that appears in the top of the figure.
In many cases, for instance, when a person multitasks or when there are frequent interruptions
in the environment, the duration of activities can be very short, e.g., around two or three minutes.
Instead, calendars work best with a granularity of at most 15 minutes. Therefore, representing
those fine-grained activities would make the calendar too cluttered, and it would negatively
impact the quality of the visualization. For this reason, we abstract the activities obtained
from the Active Window Tracking data to 15-minute time slots. Specifically, we assign each
15-minute time slot to the activity that has been performed for the longest time in that period
as long as it is above a certain threshold. Otherwise, the 15-minute time slot is assigned to a
“misc” activity or to no activity if the user has been inactive for the majority of the time slot.
Besides the overview provided by the calendar backdrop and the layers of information on
top of it, the tool is designed to work following the details-on-demand principle. Specifically,
when the user clicks in any of the intervals in the calendar, a full set of details and metrics
regarding that interval appears, as shown in the right-hand side of Fig. 3. The information
provided includes (i) the details of the activities performed in the interval that were abstracted
away in the calendar; (ii) some metrics about interruptions, effectiveness, and other activities
performed in the same interval, and (iii) the details of the events and windows active in the
interval. Similarly, by clicking in Case details the user can find details of the case performed in
the interval selected. For both, the interval and the case details the metrics computed are based
on those proposed in [14] to analyze the interruptions during scientific research collaborations.
4. Storage Tips and Shelf Life (Maturity and Availability)
In our earlier work [8], the original implementations of the healthcare and personal information
management have undergone an extensive evaluation in the form of semi-structured interviews
with end users. The healthcare tool was considered intuitive and fulfilled the defined needs. As
such, only cosmetic changes were made to the tool.
The Banana implementation, on the other hand, is a much evolved version of the initial
proof-of-concept that was detailed in [8]. In that proof-of-concept, the Active Window Tracking
data was simply pre-processed, before a calendar was generated that was then integrated into
Google Calendar. The biggest limitation of that approach was that this generic tool did not
allow for providing further details on the data. Instead, the tool we are presenting here is a
dedicated tool that provides many details on demand, which was one of the most requested
features we gathered from the evaluation of the original proof-of-concept.
Both implementations follow the same core concepts of the Tiramisù framework. Differences
in the use cases, visual design, and the technology stack are deliberate, intended to showcase
the versatility of the framework for developing process mining tools for practical use cases.
Acknowledgments
This work started at Schloss Dagstuhl (Leibniz-Zentrum für Informatik), seminar 23271 “Hu-
man in the (Process) Mines”, and was partially supported by grants PID2021-126227NB-C21
funded by MCIN/AEI /10.13039/501100011033/FEDER, EU, and TED2021-131023B-C22 funded
by MCIN/AEI/10.13039/501100011033 and by the European Union “NextGenerationEU”/PRTR.
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