=Paper=
{{Paper
|id=Vol-3783/paper_349
|storemode=property
|title=iGEDI: interactive Generating Event Data with Intentional Features
|pdfUrl=https://ceur-ws.org/Vol-3783/paper_349.pdf
|volume=Vol-3783
|authors=Andrea Maldonado,Sai Anirudh Aryasomayajula,Christian M. M. Frey,Thomas Seidl
|dblpUrl=https://dblp.org/rec/conf/icpm/0001AF024
}}
==iGEDI: interactive Generating Event Data with Intentional Features==
https://ceur-ws.org/Vol-3783/paper_349.pdf
iGEDI: interactive Generating Event Data with
Intentional Features
Andrea Maldonado1,2,∗ , Sai Anirudh Aryasomayajula1 , Christian M. M. Frey3 and
Thomas Seidl1,2
1
Ludwig-Maximilians-Universität, Germany
2
Munich Center for Machine Learning Munich, Germany
3
University of Technology Nuremberg, Germany
Abstract
Process mining solutions aim to improve performance, save resources, and address bottlenecks in
organizations. However, success depends on data quality and availability, and existing analyses often
lack diverse data for rigorous testing. To overcome this, we propose an interactive web application tool,
extending the GEDI Python framework, which creates event datasets that meet specific (meta-)features.
It provides diverse benchmark event data by exploring new regions within the feature space, enhancing
the range and quality of process mining analyses. This tool improves evaluation quality and helps
uncover correlations between meta-features and metrics, ultimately enhancing solution effectiveness.
Keywords
Event Data Generation, Optimization, Event Log Features, Benchmarking
Metadata description Value
Tool name iGEDI
Current version 1.0
Legal code license MIT License
Languages, tools and services used Python
Supported operating environment GNU/Linux, MacOS, Microsoft Windows
Download/Demo URL https://github.com/lmu-dbs/gedi/archive/refs/heads/demo_icpm24.zip
https://huggingface.co/spaces/andreamalhera/igedi
Documentation URL https://github.com/lmu-dbs/gedi/blob/demo-icpm24/README.md
Source code repository https://github.com/lmu-dbs/gedi/tree/demo-icpm24
Screencast video https://youtu.be/9iQhaYwyQ9E
1. Introduction
The development of benchmark event data (ED) that employs comprehensive intentional feature
characteristics and their connections to metrics supports process miners to evaluate methods
more efficiently and reliably. However, the availability of diverse data often presents a challenge,
ICPM 2024 Tool Demonstration Track, October 14-18, 2024, Kongens Lyngby, Denmark
∗
Corresponding author.
Envelope-Open maldonado@dbs.ifi.lmu.de (A. Maldonado); anirudhsai027@gmail.com (S. A. Aryasomayajula);
christian.frey@utn.de (C. M. M. Frey); seidl@dbs.ifi.lmu.de (T. Seidl)
Orcid 0009-0009-8978-502X (A. Maldonado); 0000-0003-2458-6651 (C. M. M. Frey); 0000-0002-4861-1412 (T. Seidl)
© 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
Figure 1: iGEDI interface
limiting the ability to thoroughly evaluate these novel methods. [1, 2] Existing tools, such as
PURPLE[3] and Declare4Py[4][5], assist in generating event logs based on specific properties,
but they are often constrained to basic features like trace length and the number of variants.
To address this gap, we introduce an interactive online tool that integrates GEDI (Generating
Event Data with Intentional Features) [6] — a framework that offers a broad range of properties,
from statistical measures to entropy-based characteristics.
Our tool, interactive Generating Event Data with Intentional Features (iGEDI) empowers
users to create event data tailored to their specific needs and objectives. By supporting seamless
customization and integration, our innovative platform not only enhances the efficiency of
testing process mining methods but also enables researchers to explore deeper connections
between event data characteristics and evaluation metrics. In academic research, it’s crucial to
train and evaluate methods on diverse datasets to improve robustness and generalization. [6]
demonstrates that evaluation metrics in process discovery are interrelated when models are
trained on real-world benchmarks versus enriched data settings. Our tool also allows testing on
synthetic datasets that mimic characteristics of inaccessible test data, such as those restricted
by GDPR.
2. iGEDI’s Main Features
iGEDI, in fig. 1, is an interactive web application available both as an online service 1 and as
a locally executable program2 . It allows users to create configuration files to subsequently
1
https://huggingface.co/spaces/andreamalhera/igedi
2
https://pypi.org/project/gedi/
generate event data based on the framework “Generating Event Data with Intentional Features”
(GEDI) [6]. GEDI employs (meta-)features, which numerically describe event log properties, to
generate ED that have specific desired values. Supported event data features are presented in
the Feature Extraction From Event Data (FEEED) [7] framework and include statistics as well
as more complex, relationships between ED elements. Specifically, the feature types, that are
currently supported by iGEDI concern simple summary statistics, entropies [8], and epa-based
[9] about cardinality of traces/variants, trace length, variants, and (start/end) activities. For
detailed feature descriptions and default settings for realistic bounds, we refer to our repository3 .
Defined feature values are handled as targets in a hyperparameter optimization (HPO)
problem. As proposed in [6], GEDI embeds the Process Tree and Log Generator (PTLG) proposed
by Joucke et al. [2] and iteratively generates a process to optimize the parameters of PTLG, such
that novel EDs’ features align with the intended feature values, i.e. targets. The parameters of
the embedded generator module are optimized by Bayesian Optimization (BO). Intuitively,
BO iteratively selects and evaluates promising parameters, aiming to minimize an objective
function. Formally, GEDI’s objective function tackles a minimization problem of distances in
feature space between an array of desired feature values and an array of generated ED’s feature
values. Hence, by leveraging GEDI, users can reproduce single event logs based on their desired
feature values, or examine a grid of event logs, by regarding a hyperrectangle (grid) of specific
feature value combinations.
Alongside implementing our architecture, iGEDI assists users throughout the specification
process, automatically generates configuration files defining the feature space, and enables
them to deploy GEDI either locally or as an interactive web application. Using the online web
app, the user can directly download the generated event logs.
Next, we describe iGEDI’s two options to create one or multiple event logs at once:
iGEDI supports manual input as well as input from a
file. The supported file formats include event logs with a log rmcv ense
‘.xes‘ extension or ‘.csv‘ files. For the event log, users have BPIC15f4 0.003 0.604
the option to select features of interest, and the generated RTFMP 0.376 0.112
event log will be optimized to closely match the feature HD 0.517 0.254
values of the event log. For the ‘.csv‘ option, the file should
contain at least one feature column, according to FEEED’s Table 1: feature values for three
[7] features, a ’log’ column containing the name of the target real ED
event log. Therefore, one row represents a desired feature
combination. Table 1 shows a possible example for such a ‘.csv‘ file. It depicts the feature values
for ratio most common variant (rmcv) and epa-based normalized sequence entropy (ense), as in
[9], of three public available datasets4 , namely BPIC15f4[10], RTFMP[11] and HD[12]. While
rmcv compares the frequency of the most common variant to the overall number of traces in an
event-log, the intuition behind ense[9] is to measure the variability/predictability of sequences
captured by the event-log, considering their prefixes. A low ense indicates a process, where
most cases follow similar paths, and a high value indicates a complex or highly variable process
with many different paths.
3
https://github.com/lmu-dbs/gedi/tree/demo-icpm24
4
https://www.tf-pm.org/competitions-awards/bpi-challenge
Moreover, independently of the input option, the user can choose to generate point targets
or a multidimensional grid of targets lying within a finite hyperrectangle:
Point targets mode (as seen in fig. 1) aims to reproduce ED directly aiming at specified
feature values. In manual mode, the user can define specific target values for each selected
feature for one generation experiment. Manual input requires semantic knowledge about
selected features to choose values in sensible feature ranges. In contrast, inputting a table
(”From CSV” option) and choosing the point target option will generate one event log per
row, targeting their respective feature values for listed features. To reproduce ED, listed in
table 1 in terms of the two selected features, iGEDI will produce three sets of targets for
ED generation: [{rmcv: 0.003, ense: 0.604}, {rmcv: 0.376, ense: 0.112}, {rmcv: 0.517, ense:
0.254}] to reproduce BPIC15f4, RTFMP and HD, respectively. Using this option, we gener-
ated ED and measured their euclidean similarity to respective targets, as shown in fig. 2.
For the grid-based targets mode, iGEDI
provides two possibilities to define the grid:
the combinatorial method and the range
method. Selecting the combinatorial op-
tion, the user can manually define how many
combinations of features and feature values
they want to generate ED for. By defining 𝑚
features with 𝑘 values, the user will get 𝑘 𝑚
combinations, where each combination repre-
sents an event log to be generated. Otherwise
Figure 2: Euclidean similarity between gener-
using an input csv table, iGEDI suggests 𝑓𝑚𝑖𝑛 ,
ated ED and their respective targets.
𝑓𝑚𝑎𝑥 i.a. for each feature 𝑓 to create combina-
tions of those feature values. In that case, e.g.,
table 1 results in statistical values as {rmcv𝑚𝑖𝑛 : 0.003, rmcv𝑚𝑎𝑥 : 0.517, ense𝑚𝑖𝑛 : 0.112, ense𝑚𝑎𝑥 :
0.604} and four generation experiments with targets: [{rmcv: 0.003, ense: 0.112}, {rmcv: 0.003,
ense: 0.604}, {rmcv: 0.517, ense: 0.112}, {rmcv: 0.517, ense: 0.604}].
Finally, following the range option and manual input for each feature, the user can define a
range [𝑓𝑚𝑖𝑛 , 𝑓𝑚𝑎𝑥 ] and a 𝑓𝑠𝑡𝑒𝑝𝑠𝑖𝑧𝑒 for each feature 𝑓. A step size defines a feature-specific sampling
rate resulting in several samples along each feature dimension, which are separately used as
target values. For example, if the user creates a grid of chosen features rmcv and ense from
0.0 to 1.0, with step size 0.1, GEDI will run 11 ⋅ 11 = 121 generation experiments, with varying
feature combinations within those ranges. If the input is a table as table 1, value-based 𝑓𝑚𝑖𝑛 and
𝑓𝑚𝑎𝑥 are suggested based on the table’s statistics, e.g., {rmcv𝑚𝑖𝑛 : 0.003, rmcv𝑚𝑎𝑥 : 0.517}.
After specifying desired options and feature values, users can run the experiments in our
online application, and download the generated event logs. Alternatively, they can download
the respective configuration file to run the generation locally, using the displayed command.
3. Tool maturity
The quality of generated logs by GEDI in terms of feasibility, representativeness, and usage for
benchmarking process mining tasks has been elaborately evaluated in [6]. In [6], an in-depth
analysis of inter-feature relations in a generated grid setting is discussed. Figure 3 depicts the
target distance between generated event logs and their respective targets with a color scale. It
contains 121 combinations of features created by the range option, where both features vary
between 0.0 and 1.0 with a step size of 0.1, as in the example presented above. The lighter (darker)
the color, the closer (further away) the measured feature values from the generated ED to its
respective targets. The combination of rmcv and ense exemplarily shows a bright bottom left
side and a dark top corner. By definition, a high value of rmcv indicates that the most common
variant is highly frequent in the event log, which results in a high amount of cases following
a similar path, represented by a low ense value. In contrast, a high ense value indicates high
variability in the event logs paths, which constraints the most common path to a low frequency,
resulting in low rmcv values. For this reason combinations of simultaneously high values
for both rmcv and ense are unfeasible, as depicted in fig. 3. Therefore, the target distance of
generated features indicates the level of feasibility for that particular feature value combination.
Subsequently, further analysis about relations
between feature values and metrics for a spe-
cific task as, e.g. process discovery, can be
performed by benchmarking on highly feasi-
ble logs from the generated ED collection.
Overall, our tool iGEDI enhances existing
log generation tools by offering improved
functionality and expanded features. It fa-
cilitates understanding the relationship be-
tween feature sets and evaluation metrics, aid-
ing in the creation of tailored methods for
specific tasks. It also supports model pre-
training on diverse datasets, enhancing gen-
eralization. For testing, iGEDI can replicate
feature-based behavior of real-world data, en-
abling reproducible benchmarking and explo- Figure 3: Target similarity between grid gener-
ration of feature-metric relations. However, ated ED and their targets.
the framework’s effectiveness is sensitive to
feature selection, with increased complexity potentially leading to unfeasible solutions during
hyperparameter optimization.
4. Screencast and Website
iGEDI, as an online service is available at https://huggingface.co/spaces/andreamalhera/gedi .
The source code, as well as examples, artifacts generated during the experiments, user guide, and
examples are available at https://github.com/lmu-dbs/gedi/tree/demo-icpm24. For a short hands-
on experience, we refer to our screencast video available at https://youtu.be/9iQhaYwyQ9E.
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