=Paper=
{{Paper
|id=Vol-3310/paper15
|storemode=property
|title=Towards Automated Process Planning and Mining
|pdfUrl=https://ceur-ws.org/Vol-3310/paper15.pdf
|volume=Vol-3310
|authors=Peter Fettke,Alexander Rombach
|dblpUrl=https://dblp.org/rec/conf/ijcai/FettkeR22
}}
==Towards Automated Process Planning and Mining==
https://ceur-ws.org/Vol-3310/paper15.pdf
Towards Automated Process Planning and Mining
Mining process data for process improvements
(Discussion/Short Paper)
Peter Fettke1,2,* , Alexander Rombach1,2,*
1
German Research Center for Artificial Intelligence (DFKI), Saarbruecken, Germany
2
Saarland University, Saarbruecken, Germany
Abstract
AI Planning, Machine Learning and Process Mining have so far developed into separate research fields. At
the same time, many interesting concepts and insights have been gained at the intersection of these areas
in recent years. For example, the behavior of future processes is now comprehensively predicted with
the aid of Machine Learning. For the practical application of these findings, however, it is also necessary
not only to know the expected course, but also to give recommendations and hints for the achievement
of goals, i.e. to carry out comprehensive process planning. At the same time, an adequate integration
of the aforementioned research fields is still lacking. In this article, we present a research project in
which researchers from the AI and BPM field work jointly together. Therefore, we discuss the overall
research problem, the relevant fields of research and our overall research framework to automatically
derive process models from executional process data, derive subsequent planning problems and conduct
automated planning in order to adaptively plan and execute business processes using real-time forecasts.
Keywords
Automated Planning, Process Mining, Machine Learning, Business Process Management
1. Introduction
Business Process Management (BPM) plays a vital role for organizations. The consistent design,
implementation, execution and monitoring of business processes promises efficiency increases
and cost reduction. In today’s organizations, BPM follows the paradigm of continuous process
improvement. After a process is designed, implemented and executed, the process execution
data is retrospectively analyzed with methods such as Process Mining in order to optimize
process design in a cyclic manner. In this framework however, process agility (e.g. process
variants) has to be defined at design time in order to achieve full process automation, which is
typically not feasible in real-world scenarios. Approaches using Process Mining and Machine
Learning (ML) to overcome this problem exist, however stakeholders are left to their own
on how to use these insights [1]. At the same time, existing research regarding applications
of AI planning in BPM focuses on the design phase and lacks planning of dynamic process
executions. Such methods also require an extensive manual definition of the planning problem
PMAI@IJCAI22: International IJCAI Workshop on Process Management in the AI era, July 23, 2022, Vienna, Austria
*
Corresponding author.
$ peter.fettke@dfki.de (P. Fettke); alexander_michael.rombach@dfki.de (A. Rombach)
0000-0002-0624-4431 (P. Fettke)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
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Proceedings
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Figure 1: Research gap and overall research framework
beforehand. To add on that, conventional Process Mining techniques are not necessarily suitable
as a foundation for subsequent automated planning, since discovered process models purposely
filter out information and/or simplify process details for the sake of readability.
Our work is positioned within this research gap at the intersection of AI Planning, ML and
Process Mining, as depicted in Figure 1. We propose developing new methods to achieve online
automated planning leading to an agile execution of business processes. In that regard, we aim at
answering the following research question: How is an automated planning of processes achieved
using automatically derived process models and automatically constructed planning models?
2. Overall research framework
We propose a procedure model divided into five phases leading from executional process data to
executable planning models, as depicted in the right part of Figure 1. The general starting point
is an ERP system providing process execution data in shape of relational data and execution
logs. The second stage deals with extracting a process execution log covering the occurring
cases and events. Based on that, the next important step is to automatically derive a process
model. The key aspect hereby is that the resulting model entails enough process details to allow
for AI planning, e.g. not only the general process flow, but also implicit domain knowledge,
involved resources or potential resource allocation constraints. Such elements can then be
used to automatically construct a sophisticated planning domain in subsequent phases. In this
regard we also propose taking a broader view and starting to think about mining entire system
models which describe the architecture of (hierarchical) systems including their static as well as
dynamic elements based on distributed runs observable from event logs [2]. Stage four covers
the automated extraction of a state space model from the previously discovered model, which
will then finally be (ideally automatically) transformed into a process planning model to conduct
AI planning in stage five. A plethora of different techniques can be integrated during this phase.
One possibility is using ML-based methods like DeepQ learning, as it will be portrayed in
section 4. These automated conversions once again illustrate the before mentioned necessity of
comprehensive process models, conventional Process Mining techniques usually cannot provide.
The planning model is used to obtain an optimal execution of the conceptual process model.
Resulting process plans are executed and thus generate new executional process data, leading
to an iterative pipeline that ultimately allows to conduct automated process planning.
Figure 2: Abstract view of an exemplary application case
3. Application cases
We designed different application cases to develop and evaluate our approach, most prominently
a classic flow-shop scheduling problem shown in Figure 2 modeled using Heraklit [2].
In this scenario, a set of different products have to be produced on a production line consisting
of different machine stations and workers to operate them, where each product has to cross
all stations. Figure 2 shows an exemplary setup consisting of 10 stations and 10 workers. The
scenario includes several variable factors, e.g. the process execution time of one production
step depends on the underlying product, station as well as the operating worker with a distinct
experience level. The general steps to perform at each station can differ, adding another
dimension to the planning problem. On top of that, the process includes walking times from
one station to another whenever necessary, which in turn is dependent on the layout of the
production line, i.e. how the individual stations are distributed.
4. Preliminary results
Regarding phases two and three, multiple modeling frameworks can be considered. While XES
is a well-known standard for event logs, we came to the conclusion that such representation
might not suffice due to not providing enough information about involved resources necessary
for planning domains. Heraklit on the other hand is a modeling language based on symbolic
Petri Nets which we regard as a good foundation for subsequent planning procedures. It also
allows us to model entire system models as described in section 2 including dependencies
between individual subsystems and agents. For more details about Heraklit, we refer to [2].
Several advances have been made regarding the latter parts of the framework, e.g. “domain-
specific Constraint Patterns” in the context of Constraint Programming [3]. These patterns
describe reoccurring problems and their solutions and can be combined in order to simplify
the construction of planning problems. An example of such pattern is the flow-shop. It is also
possible to automatically identify occurrences of these patterns in event logs and automatically
assemble parameterized constraint models. This assembly is ultimately based on a pattern
repository, which therefore requires manual yet reusable one-time definitions of these patterns.
We also propose a novel GPU-assisted approach for on-the-fly generation of training samples
for Deep-Q learning [4], a powerful solution for the main challenge of providing training data
for such techniques, which outperforms conventional approaches both in terms of run-time and
memory/storage consumption. It uses the domain description extracted from a process model
in order to iteratively instantiate randomized states and compute corresponding Q-matrices.
As a Proof of Concept, the input models are preliminarily provided in PNML file formats. In
the future we plan on integrating richer representations like heraklit in order to increase the
amount of usable domain knowledge, which is necessary in more complex use cases.
Based on this approach, we developed Deep-Q-Agents methods for optimization. To achieve
convincing results using such techniques, it is necessary to set up domain specific configurations,
which in turn requires domain knowledge. This however also requires a human assessment of
these configurations. To overcome this problem, we suggest an automated exploration of the
underlying search space as a heuristic to find potentially optimal configurations.
5. Conclusion and outlook
Several research initiatives in the fields of AI Planning and Process Mining in the context
of BPM have been made so far. However, there are no satisfying results when it comes to
planning and agile executions of business processes in an automated fashion. To this end, we
propose an iterative procedure model build upon executional process data to automatically
derive process models and automatically infer solvable planning problems to plan and optimize
business processes. Up until now, several concepts have been implemented and advancements
addressing individual parts of the procedure model have been made. The next main step is to
adequately combine these individual concepts and put the proposed "round-trip" into practice.
Acknowledgments
This work is part of the research project APPaM (Grant 01IW20006), which is partly funded by
the Federal Ministry of Education and Research (BMBF).
References
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[2] P. Fettke, W. Reisig, Systems Mining with Heraklit: The Next Step, 2022. doi:10.48550/
ARXIV.2202.01289.
[3] S. Saller, J. Koehler, Easy, adaptable and high-quality Modelling with domain-specific
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