Process analytics aims to improve processes based on event logs generated by information systems by, among others, automatically discovering models representing the current system. This discovery, however, is typically based on static models, ignoring its underlying trends and shifts. Recently, the modeling and prediction of the full system have been proposed as process model forecasting (PMF). However, the current SOTA lacks the ability to model intricate control flow constructs while also not incorporating extra information, such as resources tied to the process. Besides, by using univariate models, the underlying relations between the diferent elements of the system are ignored. This proposal addresses these issues by firstly extending PMF to richer control flow models that are able to capture relationships between activities in workflows. Secondly, the current PMF techniques will be replaced by a multivariate framework based on state-of-the-art deep learning techniques such as graph neural networks, which form a natural fit for graph-based models such as workflow diagrams and capture both temporal, structural, and multiscale patterns. Next, additional perspectives, such as resources, will be added to obtain fully object-centric process model forecasts, which can incorporate any data related to a process through the forecasting of event knowledge graphs. Finally, two industry cases in finance and logistics will be used to validate the findings in a real-life setting.
Within the field of Process Mining, Predictive Process Monitoring (PPM) entails forecasting future
elements of ongoing process instances or cases, including the most probable next activities, outcomes,
and remaining runtime. Notably, the integration of machine learning and deep learning solutions
into this domain has been extensively explored in academic literature [
The current state-of-the-art in PMF involves depicting the evolution of process behavior through
time series analysis of individual Directly-Follows relations (DFs), which track the frequency of one
activity following another within cases, over a predefined timeframe [
Therefore the first objective of MuDiPMF is to forecast more semantically rich process models. This
entails expanding the feature set beyond DFs, such as the constructs utilized in advanced process
discovery techniques. Furthermore, multimodal predictive models can be used to forecast various
time series of multidimensional feature sets simultaneously, thereby accounting for process related
dependencies and correlations. Next to this, MuDiPMF aims to enhance the forecasted process models
beyond the control-flow aspect, by incorporating additional dimensions such as resource occupation,
execution times, and decision point analyses. This would, among others, allow process owners to
perform timed interventions regarding bottlenecks, and optimize resource allocations. Furthermore,
in recent years Object-Centric Process Mining (OCPM) has emerged as a new family of approaches
tailored to handle event data from processes involving diferent interconnected objects such as orders,
items, and shipments, garnering widespread attention in both academia and industry [
In summary, the Multidimensional Process Model Forecasting (MuDiPMF) project will significantly contribute to the current state-of-the-art in Business Process Management and Process Mining by enhancing the recently proposed PMF framework by broadening the feature set to be predicted with additional dimensions, while exploring the application of more suitable multivariate predictive methods. Finally, the project aims to demonstrate the practical utility of the diferent PMF enhancements using real-life process data from two diferent domains: financial services and logistics.
Figure 1 illustrates the research gaps and research objectives (ROs). Given the recent inception of the
PMF paradigm, the state-of-the-art is currently confined to univariate forecasting of distinct DFs [
Next, the growing literature on PPM remains relevant despite its focus on single objectives such as
sufix prediction [
From an algorithmic perspective underpinning these applications, various data-driven and deep learning approaches for multivariate time series forecasting have been proposed [13]. Given the graphbased structure of process models, together with their emergence as powerful predictors in diferent tasks, Graph Neural Networks (GNNs) are a natural match to learn both temporal and structural properties of process models. Particularly, work incorporating the time dimension into GNNs to investigate both spatial and temporal dependency together could provide PMF with more powerful and lfexible predictors capable of taking into account process-specific dependencies. Diferent approaches for spatial-temporal graph neural networks (STGNNs), such as STGCN [14] and StemGNN [15], have shown potential in domains such as trafic forecasting.
Multidimensional feature set
Multivariate predictive models
Multidimensional Process Model Forecasting (MuDiPMF)
process model representations Engineer data structures for semantically rich control-flow process model forecasting. WP1.1 Develop advanced multivariate and multiscale forecasting algorithms for semantically rich process models.
WP1.2
forecasting to other dimensions Develop time series data transformation techniques for bottleneck, resource, and decision points. WP2.1
Design and implement a GNN-based multidimensional process model forecasting algorithm. WP2.2
model forecasting algorithm Construct event knowledge graphs of object-centric event logs tailored to process model forecasting. WP3.1 Develop heterogeneous graph-based predictive models to forecast objectcentric process models.WP3.2 WP4.1
Case study in financial services Case study in logistics industry WP4.2
Figure 2 presents a schematic overview of the proposed work plan designed based on [16], illustrating the alignment of diferent work packages (WPs) with the four research objectives (ROs).
RO1 aims to extend the feature set of the forecasting techniques beyond DFs by incorporating process representations and dependencies utilized in various widely used process discovery methodologies. For example, we can forecast full dependency graphs, as this would allow us to discover parallel activities or forecast the required metrics for the partition creation used by top-down discovery algorithms. Correspondingly, existing predictive models will be replaced with multivariate models capable of simultaneously forecasting all time series while accounting for cross-dependencies. One promising avenue involves the exploration of multi-scale spatial-temporal graph neural networks.
RO2 extends process model forecasting capabilities to incorporate control-flow orthogonal dimensions, including bottlenecks, resource allocation, and decision points. We aim to integrate resource information into the feature set, considering multiple granularity levels from overall resource occupancy to allocations at specific activities. Finally, we will incorporate the attention mechanism in GNNs to extract more reliable and eficient patterns and leverage the multitask learning (MTL) framework to implement a multidimensional PMF algorithm.
RO3 aims to design and implement a comprehensive object-centric process model forecasting algorithm. To account for the complexity of object-centric event logs, we will develop an appropriate event knowledge graph (EKG) structure on top of which a new process model forecasting algorithm can be built. This would entail the construction of time series features for heterogeneous graph elements within EKGs. Next to it, we aim to explore extra architectures tailored toward heterogeneous graph forecasting. The initial avenue that will be pursued focuses on the use of heterogeneous temporal graph neural networks.
RO4 aims to extend the impact of the developed techniques within MuDiPMF by deploying them in practical applications across diverse industries, specifically targeting the financial services and logistics sectors. The research group’s network will be leveraged to collaborate with two partnering companies. Through these case studies, the objective is to demonstrate how the advancements made can substantially improve the state-of-the-art in Process Model Forecasting (PMF) and highlight their practical efectiveness. By validating our algorithms using real-world problems and data, we do not only aim to emphasize their added value, but refinement and adaptation strategies will be developed to make MuDiPMF algorithms extensible to other application domains.
This study was financed by the Research Foundation Flanders under grant number G039923N and Internal Funds KU Leuven under grant number C14/23/031.
This Ph.D. thesis is supervised by Prof. dr. Johannes De Smedt and Prof. dr. Jochen De Weerdt. [10] I. Teinemaa, M. Dumas, M. L. Rosa, F. M. Maggi, Outcome-oriented predictive process monitoring: Review and benchmark, ACM Transactions on Knowledge Discovery from Data (TKDD) 13 (2019) 1–57. [11] R. Galanti, M. De Leoni, N. Navarin, A. Marazzi, Object-centric process predictive analytics, Expert
Systems with Applications 213 (2023) 119173. [12] D. Fahland, Process mining over multiple behavioral dimensions with event knowledge graphs, in: Process mining handbook, Springer, 2022, pp. 274–319. [13] B. Lim, S. Zohren, Time-series forecasting with deep learning: a survey, Philosophical Transactions of the Royal Society A 379 (2021) 20200209. [14] B. Yu, H. Yin, Z. Zhu, Spatio-temporal graph convolutional networks: A deep learning framework for trafic forecasting, arXiv preprint arXiv:1709.04875 (2017). [15] D. Cao, Y. Wang, J. Duan, C. Zhang, X. Zhu, C. Huang, Y. Tong, B. Xu, J. Bai, J. Tong, et al., Spectral temporal graph neural network for multivariate time-series forecasting, Advances in neural information processing systems 33 (2020) 17766–17778. [16] J. Mendling, H. Leopold, H. Meyerhenke, B. Depaire, Methodology of algorithm engineering, arXiv preprint arXiv:2310.18979 (2023).