=Paper=
{{Paper
|id=Vol-3648/paper_4226
|storemode=property
|title=Object-Centric process mining for process analysis and operational support
|pdfUrl=https://ceur-ws.org/Vol-3648/paper_4226.pdf
|volume=Vol-3648
|authors=Giulia Ruffini
|dblpUrl=https://dblp.org/rec/conf/icpm/Ruffini23
}}
==Object-Centric process mining for process analysis and operational support==
https://ceur-ws.org/Vol-3648/paper_4226.pdf
Object-Centric Process Mining for Process Analysis
and Operational Support⋆
Giulia Ruffini1,*,†
1
Department of Computer Science, University of Turin, Turin, Italy
Abstract
Object-centric process mining is a novel paradigm which has been gaining an increasing attention thanks
to its potential to overcome limitations of traditional process mining techniques. In this PhD project, we
aim at developing novel techniques able to leverage an object-centric paradigm to provide data-driven
decision support on running process executions. In particular, we plan to develop novel techniques to
a) identify process behaviors which impact the process outcome; b) generate diagnostics for deviant
behaviors; and, c) deliver predictions on a KPI of interest at a given moment of a process execution. The
manuscript will delve into the research questions and the methodology envisioned for each of these
goals.
Keywords
Object-Centric Process Mining, Predictive Process Monitoring, Conformance Checking, Pattern Extrac-
tion
1. Introduction
An increasing attention in the field of automated process analysis concerns the Object-Centric
Process Mining (OC-PM) perspective, which can better represent the complexity and intercon-
nections existing in business operations compared to classical Process Mining (PM) techniques
[1, 2]. First, OC-PM handles multi-dimensional event logs better: by considering different objects
rather than a single object as case ID and the other objects as attributes, the convergence and
divergence problem can be avoided [2]. OC-PM also considers the interrelationships between
different objects, allowing one to grasp information that would otherwise be lost.
Related Works. With the development of object-centric process mining and object-centric
event-log (OCEL) formats [2, 3], existing process mining techniques must be adapted as well.
Several efforts have already been made in this direction. Contributions made by previous
work include: the introduction of object-centric process models (e.g., [2] [4]); techniques to
explore process models and features in the OC setting (e.g. [5], or [6]); OC-based approaches to
performance analysis [7] and constraint monitoring [8]. However, to the best of our knowledge,
only a few studies have addressed important process analysis tasks such as process pattern
extraction [9], conformance checking techniques [10], and predictive process monitoring [11, 12]
within the OC paradigm. Several open challenges exist for each of these tasks.
ICPM Doctoral Consortium and Demo Track 2023
*
Corresponding author.
$ giulia.ruffini@unito.it (G. Ruffini)
0009-0003-4179-496X (G. Ruffini)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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This project aims to contribute to this research area by introducing OC techniques to support
the previously mentioned process analysis tasks. More precisely, the project aims at answering
the following research questions:
RQ1 How to extract OC process patterns which impact the process outcome?
RQ2 How to asses the conformance of process executions considering OC relations?
RQ3 How to leverage the OC paradigm to enhance the quality of process predictions?
The rest of this manuscript delves into each of these research problems, elaborating upon the
corresponding challenges and the envisioned methodology.
2. OC-Pattern Extraction
Research Problem. Process discovery techniques allow one to automatically extract a process
model from a given event log tracking past process executions. A well-known limitation of
these techniques is that many real-world processes are characterised by complex and variable
behaviours, that lead to the extraction of overly complex models which offer little or no support
to the analysis. A possible strategy to mitigate this issue consists in inferring interesting process
behaviours, rather than start-to-end process model. A process pattern, in classical PM, is a
sequence of activities that repeats over multiple cases. Traditional process pattern discovery
techniques return process patterns only focused on ordering relations existing between activities
occurring in the same process execution. In this way, potential interactions with process objects
and with activities occurring in another process instance cannot be captured. Few papers
leverage object-centric domain in pattern extraction, e.g. [9], with certain limitations since
authors considered flattened event-logs, leading to information loss, and focus on the frequency
to measure the importance of a pattern, which often leads to generating a multitude of not-
interesting patterns.
Research Goal. We aim at developing novel pattern mining techniques to extract OC process
patterns. Following recent approaches for process pattern extraction [13], we aim at extracting
patterns that are predictive of process outcome rather than focusing solely on frequent patterns.
To this end, we plan to extend previous pattern extraction approaches leveraging graph mining
and data abstraction techniques to: a) extend the set of pattern relations considered during the
mining to include object-dependent relations; b) develop tailored analysis techniques and tools
to allow human users to navigate the set of mined patterns, exploring both intrinsic pattern
characteristics and possible relations with other patterns.
Test and Validation. The proposed procedures will be validated using optimisation methods
to find particular patterns, e.g. with the highest correlation with the outcome. These techniques
will be tested on publicly available datasets after any necessary data pre-processing. Another
validation can be performed by considering a dataset of Italian public tenders: after exploring
the relational tables and extracting an OCEL from them [14], patterns can be extracted using
the investigated techniques.
3. OC-Conformance Checking
Research problem. Identifying anomalies present in an event-log requires a process model
and a case notion to compare with it. Classic conformance-checking techniques generate
diagnostics which consider process executions in isolation, neglecting possible OC relations
and thus potentially missing some deviant behaviours. Adapting conformance checking to the
OC paradigm would allow us to overcome this limitation, enabling more accurate diagnostics.
Some previous work (e.g., [10] and [5]) proposed techniques to calculate conformance metrics
in an OC domain. However, authors worked with "flattened" event-logs.
Research Goal. We aim at developing novel OC conformance checking techniques able to
handle the graph-structure of OCEL. In this setting, comparing an individual case with the
model entails comparing a graph extracted from OCEL with a graph representing admissible
behaviors within the considered model. We plan to investigate and leverage previous work
introducing OC case notions [15] and OC process models (e.g., the OC-PN model [2], or the
OC-DFG model [4]) to develop techniques able to find a particular morphism to map a graph
onto another graph, to identify conforming behaviours and anomalies in OCEL. We also aim
at investigating the use of heuristics to mitigate possible computational challenges usually
associated with graph analysis techniques.
Test and Validation. To validate the results, we first plan to use a synthetic dataset to test
the capability of the proposed method to detect injected anomalies. We then plan to explore
publicly available datasets.
4. OC-Predictive Process Monitoring
Research problem. Predictive process monitoring techniques aim at predicting the unfolding
of an ongoing process execution to determine, e.g. the next activity to be executed or the
total duration of the execution. Previous studies have pointed out the potential benefits of
leveraging the OC paradigm for prediction tasks, showing that taking OC relations into account
often results in improved prediction performance. As an example, [11] presented a predictive
approach using gradient boosting, while [12] proposed another predictive approach using LSTM
and neural networks. Despite considering a multi-dimensional event log, both techniques
operate with an enriched flattened event log and a multiset of sequences. This approach can
lead to a loss of information stemming from the relationships between objects.
Research Goal. We aim at developing novel predictive process monitoring techniques to
leverage the graph structure of OC event logs for prediction. To this end, we intend to investigate
the use of graph neural networks (GNN), given their ability to deal with graph structures natively.
Furthermore, we intend to investigate how to represent additional relations among process
activities (e.g., relations between event data, or ordering relations different from the directly
following ones) and to develop tailored XAI techniques to analyse the impact of the different
relations on the predicted outcome.
Test and Validation. We plan to test and validate the developed techniques on publicly available
datasets using traditional classification performance metrics, such as AUC and f1.
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