Adaptive AI-based causal control: Toward an autonomous factory in solder paste printing - Herchenbach, Marvin; Weinzierl, Sven; Zilker, Sandra; Schwulera, Erik; Matzner, Martin (Computers in Industry, 2025)
In recent years, there has been a remarkable shift from automated plants to intelligent production in the industrial context, accelerated by technologies such as artificial intelligence (AI). The ultimate goal is an autonomous plant that is capable of self-regulation and self-optimization. In electronics production, the first approaches have been proposed for deriving and adjusting machine parameters for solder paste printing in the surface-mount technology production of printed circuit boards. However, these approaches are often static and perform reactive actions since they are either based on expert systems or data-driven models. To reach a dynamic optimization, this work proposes a methodology, called adaptive AI-based causal control, allowing offline and online optimization. Following the principles of the Design for Six Sigma method, customer-oriented key performance indicators were derived, that aimed at a stable soldering process by focusing on the spread of the solder volume and a dedicated overall spread metric. The offline optimization (open-loop control) is based on a surrogate model approach to find optimal initial printing parameters. The online optimization (closed-loop control) employs a data-driven model predictive control to adjust the printing parameters dynamically. In addition, to consider the causal effects of the control variables in the online optimization, a causal graph is exploited in the predictive controller. Regarding the effectiveness of the open-loop control, our evaluation reveals a reduction in spread by 11.3% in production. Furthermore, in terms of the efficacy of the closed-loop control, we obtain a reduction in volume range by 16.7% in a simulated setting of the predictive controller. Thereby, the integration of a causal inference component based on a generated causal graph, achieving a recall of 76.9% by considering process knowledge identified with domain experts, accounts for about 2.8%
Keywords: manufacturing; electronics production; process optimization; control system; causal machine learning; surrogate model

A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis - Zilker, Sandra; Weinzierl, Sven; Kraus, Mathias; Zschech, Patrick; Matzner, Martin (Health Care Management Science, 2024)
Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient’s complete health history to make informed decisions about future events. However, previous work has mostly relied on so-called black-box models, which are unintelligible to humans, making it difficult for clinicians to apply such models. Our work introduces PatWay-Net, an ML framework designed for interpretable predictions of admission to the intensive care unit (ICU) for patients with symptoms of sepsis. We propose a novel type of recurrent neural network and combine it with multi-layer perceptrons to process the patient pathways and produce predictive yet interpretable results. We demonstrate its utility through a comprehensive dashboard that visualizes patient health trajectories, predictive outcomes, and associated risks. Our evaluation includes both predictive performance – where PatWay-Net outperforms standard models such as decision trees, random forests, and gradient-boosted decision trees – and clinical utility, validated through structured interviews with clinicians. By providing improved predictive accuracy along with interpretable and actionable insights, PatWay-Net serves as a valuable tool for healthcare decision support in the critical case of patients with symptoms of sepsis.<br
Keywords: patient pathway; process prediction; sepsis; interpretability; interpretable machine learning; interpretation plots; deep learning

A method for predicting workarounds in business processes - Weinzierl, Sven; Bartelheimer, Christian; Zilker, Sandra; Beverungen, Daniel; Matzner, Martin (Proceedings of the 25th Pacific Asia Conference on Information Systems, 2022)
Workarounds are performed intentionally by employees to bypass obstacles constraining their day-to-day work. These obstacles manifest from latent misfits in the interplay of information systems, organizational structure, and human agency. While workarounds are often mandatory for employees to perform their work, they can yield positive and negative effects on an organization’s performance. Process managers are supposed to identify workarounds early, promoting their positive while reducing their negative consequences. While related research has touched upon detecting workarounds in event logs that include data on completed processes, little is known on how to predict workarounds in a running business process. We set out to design a workaround prediction method using a deep learning approach. The IT artifact enables process managers to proactively intervene if workarounds are about to emerge in a business process, reducing their adverse effects while supporting organizational learning and process innovation.<br
Keywords: workarounds; business processes management; process mining; predictive business process monitoring; deep learning; machine learning

A next click recommender system for web-based service analytics with context-aware LSTMs - Weinzierl, Sven; Stierle, Matthias; Zilker, Sandra; Matzner, Martin (Proceedings of the 53rd Hawaii International Conference on System Sciences, 2020)

A technique for determining relevance scores of process activities using graph-based neural networks - Stierle, Matthias; Weinzierl, Sven; Harl, Maximilian; Matzner, Martin (Decision Support Systems, 2021)
Process models generated through process mining depict the as-is state of a process. Through annotations with metrics such as the frequency or duration of activities, these models provide generic information to the process analyst. To improve business processes with respect to performance measures, process analysts require further guidance from the process model. In this study, we design Graph Relevance Miner (GRM), a technique based on graph neural networks, to determine the relevance scores for process activities with respect to performance measures. Annotating process models with such relevance scores facilitates a problem-focused analysis of the business process, placing these problems at the centre of the analysis. We quantitatively evaluate the predictive quality of our technique using four datasets from different domains, to demonstrate the faithfulness of the relevance scores. Furthermore, we present the results of a case study, which highlight the utility of the technique for organisations. Our work has important implications both for research and business applications, because process model-based analyses feature shortcomings that need to be urgently addressed to realise successful process mining at an enterprise level.
Keywords: Process mining; Process analytics; Business process management; Deep learning; Graph neural networks

A technology-specific process mining maturity grid for manufacturing and logistics - Dunzer, Sebastian; Zilker, Sandra; Weinzierl, Sven; Tang, Willi; Dieckmann, Felix; Stenglein, Simon; Rist, Johannes; Matzner, Martin (International Journal of Production Research, 2025)
Process mining applies data mining to the data produced in the slipstream of the executing process instances. Hence, it enables organisations gain transparency about how exactly the processes are executed. Organisations can use this transparency to find opportunities to improve their business processes, such as manufacturing and logistics. Previous research provides methodological guidance on how process mining projects can be conducted and how process mining capabilities can be assessed from an organisational perspective. However, organisations tend to lack experience with novel technologies, which is why they often miss out on opportunities to improve their operations using the insights gained through process mining. This study is the first to contribute to the existing literature by examining process mining capabilities from a technological perspective, focussing on the manufacturing and logistics domain. We conducted a systematic case study review and 12 semi-structured interviews with different manufacturing organisations to develop and validate our technology-specific process mining maturity grid for manufacturing and logistics. The resulting maturity grid is a tool for demonstrating development paths for the further application of process mining in organisations. The grid details maturity using three focus areas (i.e. data input, process mining utilisation, and integration) structured into five maturity level
Keywords: process mining; maturity grid; process improvement; manufacturing; logistics

Best of both worlds: Combining predictive power with interpretable and explainable results for patient pathway prediction - Zilker, Sandra; Weinzierl, Sven; Zschech, Patrick; Kraus, Mathias; Matzner, Martin (Proceedings of the 31st European Conference on Information Systems, 2023)
Proactively analyzing patient pathways can help healthcare providers to anticipate treatment-related risks, detect undesired outcomes, and allocate resources quickly. For this purpose, modern methods from the field of predictive business process monitoring can be applied to create data-driven models that capture patterns from past behavior to provide predictions about running process instances. Recent methods increasingly focus on deep neural networks (DNN) due to their superior prediction performances and their independence from process knowledge. However, DNNs generally have the disadvantage of showing black-box characteristics, which hampers dissemination in critical environments such as healthcare. To this end, we propose the design of HIXPred, a novel artifact combining predictive power with explainable results for patient pathway predictions. We instantiate HIXPred and apply it to a real-life healthcare use case for evaluation and demonstration purposes and conduct interviews with medical experts. Our results confirm high predictive performance while ensuring sufficient interpretability and explainability to provide comprehensible decision suppor
Keywords: Patient pathway prediction; predictive business process monitoring; explainable artificial intelligence; interpretable artificial intelligence; deep neural networks; business process management; Diskriminierung

Bringing light into the darkness - A systematic literature review on explainable predictive business process monitoring techniques - Stierle, Matthias; Brunk, Jens; Weinzierl, Sven; Zilker, Sandra; Matzner, Martin; Becker, Jörg (Proceedings of the 29th European Conference on Information Systems, 2021)
Predictive business process monitoring (PBPM) provides a set of techniques to perform different prediction tasks in running business processes, such as the next activity, the process outcome, or the remaining time. Nowadays, deep-learning-based techniques provide more accurate predictive models. However, the explainability of these models has long been neglected. The predictive quality is essential for PBPM-based decision support systems, but also its explainability for human stakeholders needs to be considered. Explainable artificial intelligence (XAI) describes different approaches to make machine-learning-based techniques explainable. To examine the current state of explainable PBPM techniques, we perform a structured and descriptive literature review. We identify explainable PBPM techniques of the domain and classify them along with different XAI-related concepts: prediction purpose, intrinsically interpretable or post-hoc, evaluation objective, and evaluation method. Based on our classification, we identify trends in the domain and remaining research gaps.<br
Keywords: business process; prediction; interpretability; explainable artificial intelligence

CareerBERT: Matching resumes to ESCO jobs in a shared embedding space for generic job recommendations (forthcoming) - Rosenberger, Julian; Wolfrum, Lukas; Weinzierl, Sven; Kraus, Mathias; Zschech, Patrick (Expert Systems With Applications, 2025)
The rapidly evolving labor market, driven by technological advancements and economic shifts, presents significant challenges for traditional job matching and consultation services. In response, we introduce an advanced support tool for career counselors and job seekers based on CareerBERT, a novel approach that leverages the power of unstructured textual data sources, such as resumes, to provide more accurate and comprehensive job recommendations. In contrast to previous approaches that primarily focus on job recommendations based on a fixed set of concrete job advertisements, our approach involves the creation of a corpus that combines data from the European Skills, Competences, and Occupations (ESCO) taxonomy and EURopean Employment Services (EURES) job advertisements, ensuring an up-to-date and well-defined representation of general job titles in the labor market. Our two-step evaluation approach, consisting of an application-grounded evaluation using EURES job advertisements and a human-grounded evaluation using real-world resumes and Human Resources (HR) expert feedback, provides a comprehensive assessment of CareerBERT's performance. blueOur experimental results demonstrate that CareerBERT outperforms both traditional and state-of-the-art embedding approaches while showing robust effectiveness in human expert evaluations. These results confirm the effectiveness of CareerBERT in supporting career consultants by generating relevant job recommendations based on resumes, ultimately enhancing the efficiency of job consultations and expanding the perspectives of job seekers. This research contributes to the field of NLP and job recommendation systems, offering valuable insights for both researchers and practitioners in the domain of career consulting and job matching.
Keywords: Job consultation; Job markets; Job recommendation system; BERT; NLP

Challenging the performance-interpretability trade-off: An evaluation of interpretable machine learning models - Kruschel, Sven; Hambauer, Nico; Weinzierl, Sven; Zilker, Sandra; Kraus, Mathias; Zschech, Patrick (Business & Information Systems Engineering, 2025)
Machine learning is permeating every conceivable domain to promote data-driven decision support. The focus is often on advanced black-box models due to their assumed performance advantages, whereas interpretable models are often associated with inferior predictive qualities. More recently, however, a new generation of generalized additive models (GAMs) has been proposed that offer promising properties for capturing complex, non-linear patterns while remaining fully interpretable. To uncover the merits and limitations of these models, this study examines the predictive performance of seven different GAMs in comparison to seven commonly used machine learning models based on a collection of twenty tabular benchmark datasets. To ensure a fair and robust model comparison, an extensive hyperparameter search combined with cross-validation was performed, resulting in 68,500 model runs. In addition, this study qualitatively examines the visual output of the models to assess their level of interpretability. Based on these results, the paper dispels the misconception that only black-box models can achieve high accuracy by demonstrating that there is no strict trade-off between predictive performance and model interpretability for tabular data. Furthermore, the paper discusses the importance of GAMs as powerful interpretable models for the field of information systems and derives implications for future work from a socio-technical perspective.<br
Keywords: Decision support; Predictive analytics; Interpretable machine learning; Generalized additive models; Explainable artificial intelligence

Closing the gap between smart manufacturing applications and data management - Marx, Emanuel; Stierle, Matthias; Weinzierl, Sven; Matzner, Martin (Proceedings of the 15th International Conference on Wirtschaftsinformatik (WI), 2020)
Smart manufacturing refers to the intensified collaboration of machines, products, and people throughout the manufacturing and the supply chain. This facilitates innovative products, services, business models, and processes. Smart manufacturing is premised on emerging technologies such as cloud computing, mobile computing, the Internet of Things, data analytics, and artificial intelligence. A plethora of companies struggles with the implementation of corresponding applications. In research and practice, we see general data management approaches with primary attention on building architectures that are not tailored to fit a particular domain/ application scenario. However, a robust data management concept is vital, as smart manufacturing decisively depends on data. To address this substantial deficit, we conduct a comprehensive literature review, an expert workshop, and semi-structured expert interviews with one of the leading German automotive manufacturers. The result is a catalog of requirements and a framework for data management that fosters the implementation of smart manufacturing applications.<br
Keywords: smart manufacturing; smart factory; data management; data analytics; expert interview

Context-aware explanations of accurate predictions in service processes - Weinzierl, Sven; Zilker, Sandra; Brunk, Jens; Revoredo, Kate; Matzner, Martin; Becker, Jörg (Proceedings of the 57th Hawaii International Conference on System Sciences, 2024)
The performance of a service process can be improved by the early anticipation of future behavior, such as predicting the next activity using predictive business process monitoring (PBPM). Recent PBPM techniques are based on deep neural networks (DNNs) and consider the process context to create accurate predictions. To provide explainability of these predictions, model-agnostic explainable AI (XAI) methods, for example, SHAP, can be used. However, creating these explanations is time-consuming and, therefore, not applicable to service processes where customers are involved. In this paper, we propose a context-aware DNN-based technique to efficiently create meaningful explanations of next activity predictions using layer-wise relevance propagation. We evaluate the predictive quality and the explanation creation time, using three real-life service event logs. Further, we demonstrate its visual output, highlighting its utility for end-users.<br
Keywords: service analytics; predictive process monitoring; machine learning; deep learning; XAI

Detecting temporal workarounds in business processes – A deep-learning-based method for analysing event log data - Weinzierl, Sven; Wolf, Verena; Pauli, Tobias; Beverungen, Daniel; Matzner, Martin (Journal of Business Analytics, 2022)
Business process management distinguishes the actual “as-is” way work is accomplished and a prescribed or desired “to-be” version of a process. In practice, many different causes trigger a process’s drifting away from a once designed to-be state. For instance, employees may “workaround” the proposed systems or protocols in an attempt to increase their effectiveness or efficiency in day-to-day work. Workarounds are often viewed as negative deviations that interfere with security and quality requirements. This article emphasises their role in identifying and restructuring dysfunctional or obsolete processes. So far, ethnography or critical incident techniques are used to identify how and why workarounds emerge. We design a method that helps detect different types of workarounds in event logs using a deep-learning-based approach. Event logs are large data sets documenting the completion of process work based on information systems. Our method tracks indications of potential workarounds in the early stages of their emergence among deviating behaviour. Our evaluation based on four real-life event logs reveals that our method performs well and works best for business processes with fewer variations and a higher number of different activities. The proposed method is one of the first information technology artefacts to bridge the boundaries between the complementing research disciplines of organisational routines and business processes managemen
Keywords: Workaround; Business Process; Deep Learning; Process Mining; Routines

Detecting workarounds in business processes — A deep learning method for analyzing event logs - Weinzierl, Sven; Wolf, Verena; Pauli, Tobias; Beverungen, Daniel; Matzner, Martin (Proceedings of the 28th European Conference on Information Systems, 2020)
Business processes performed in organizations often deviate from the abstract process models issued by designers. Workarounds that are carried out by process participants to increase the effectiveness or efficiency of their tasks are often viewed as negative deviations from prescribed business processes, interfering with their efficiency and quality requirements. But workarounds might also play an important role in identifying and re-structuring inefficient, dysfunctional, or obsolete processes. While ethnography or critical incident techniques can serve to identify how and why workarounds emerge, we need automated methods to detect workarounds in large data sets. We set out to design a method that implements a deep-learning-based approach for detecting workarounds in event logs. An evaluation with three public real-life event logs exhibits that the method can identify workarounds best in standardized business processes that contain fewer variations and a higher number of different activities. Our method is one of the first IT artifacts to bridge boundaries between the complementing research disciplines of organizational routines and business processes management.<br
Keywords: Workarounds; Business Processes; Deep Learning; Process Mining

Driving context into text-to-text privatization - Arnold, Stefan; Yesilbas, Dilara; Weinzierl, Sven (Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing, 2023)
Metric Differential Privacy enables text-to-text privatization by adding calibrated noise to the vector of a word derived from an embedding space and projecting this noisy vector back to a discrete vocabulary using a nearest neighbor search. Since words are substituted without context, this mechanism is expected to fall short at finding substitutes for words with ambiguous meanings, such as bank. To account for these ambiguous words, we leverage a sense embedding and incorporate a sense disambiguation step prior to noise injection. We encompass our modification to the privatization mechanism with an estimation of privacy and utility. For word sense disambiguation on the Words in Context dataset, we demonstrate a substantial increase in classification accuracy by 6.05%.<br

Event Log Construction from Multimodel Data - A Reference Architecture for Exploiting Process Mining in IT Service Management - Reinhard, Philipp; Ließmann, Annina; Weinzierl, Sven; Zilker, Sandra; Li, Mahei M.; Matzner, Martin; Leimeister, Jan Marco (Proceedings of the 33rd European Conference on Information Systems, 2025)
Process mining holds substantial potential to discover and optimize processes utilizing event log data. However, current applications primarily rely on (semi-)structured data from process-aware information systems, limiting their capacity to incorporate multimodal data from diverse sources, particularly in domains like IT service management (ITSM). While existing stand-alone approaches can extract event log data from unstructured sources such as videos, documents, or bot logs, they fall short of leveraging the full range of real-world data available in ITSM. To address this gap, our research focuses on developing a reference architecture for constructing event logs from multimodal data. This architecture integrates diverse data types, construction functions, and process mining use cases. Following a design science research methodology, we aim to evaluate the architecture through a software artifact leveraging real-world ITSM data and incorporating state-of-the-art generative AI. In this study, we present the preliminary reference architecture and share early insights from expert evaluations.<br

Explainable predictive business process monitoring using gated graph neural networks - Harl, Maximilian; Weinzierl, Sven; Stierle, Matthias; Matzner, Martin (Journal of Decision Systems, 2020)
Predictive business process monitoring (PBPM) is a class of techniques designed to forecast future behaviour of a running process instance or the value of process-related metrics like times and frequencies. PBPM systems support process workers and process managers in making operational decisions. State-of-the-art PBPM systems apply deep-learning techniques with multiple hidden layers to infer from data which makes it difficult for system users to understand why a prediction was made. However, the user needs to see deeper causes to identify intervention mechanisms that secure process performance. The main contribution of this paper is a technique that makes a prediction more explainable by visualising how much the different activities included in a process impacted the prediction. This work is the first to use gated graph neural networks (GGNNs) to make decisions more explainable, and it’s also GGNN’s first application to PBPM. We use a process event data set to demonstrate our approach.<br
Keywords: Predictive business process monitoring; explainable artificial intelligence; gated graph neural networks; decision support system; deep learning

Exploring gated graph sequence neural networks for predicting next process activities - Weinzierl, Sven (Proceedings of the BPM 2021 International Workshops., 2021)
A current trend in predictive business process monitoring is to construct predictive models using deep neural networks (DNNs), especially long short-term memory neural networks, convolutional neural networks, or multilayer perceptron neural networks. While these DNN types typically require data defined on the Euclidean space (e.g., grids), graph neural networks (GNNs), a relatively new type of DNNs, can compute data defined on the non-Euclidean space (e.g., graphs). Because GNNs can directly compute graph-oriented data inputs, generally structured into nodes and edges, they can explicitly model event relationships. This paper investigates gated graph sequence neural networks (GGNNs) for the next activity prediction. First results with two real-life event logs show that GGNNs can outperform traditional DNNs regarding predictive quality, especially if nodes of an input graph are assumed as events, and the graph’s adjacency matrix only describes event relationships of a process instance prefix.<br
Keywords: predictive business process monitoring; graph neural networks; deep learning; machine learning

Exploring the effect of context information on deep learning business process predictions - Brunk, Jens; Stottmeister, Johannes; Weinzierl, Sven; Matzner, Martin; Becker, Jörg (Journal of Decision Systems, 2020)
Predictive Process Monitoring (PPM) techniques for predicting the next activity in running business processes developed into an established topic of Business Process Management. Recent research suggests using Deep Neural Networks (DNNs) for PPM because DNNs are good at learning the intricate structure of business processes. Most of these works use Long Short-Term Memory Neural Networks (LSTMs) and consider only the control flow information of an event log. Beyond control flow information, context information can add valuable information to a predictive model. However, the effects of context attributes on the predictive quality have not yet been sufficiently analyzed. This work addresses this gap and provides two insights. First, a context-sensitive prediction capability can improve the predictive quality of an LSTM-based technique. Second, the added value of context information to the quality of predicting the next activity varies in the course of a running process instance.<br
Keywords: Predictive process monitoring; deep learning; context-sensitivity

From predictive to prescriptive process monitoring: Recommending the next best actions instead of calculating the next most likely events - Weinzierl, Sven; Zilker, Sandra; Stierle, Matthias; Park, Gyunam; Matzner, Martin (Proceedings of the 15th International Conference on Wirtschaftsinformatik, 2020)
Predictive business process monitoring (PBPM) deals with predicting a process’s future behavior based on historical event logs to support a process’s execution. Many of the recent techniques utilize a machine-learned model to predict which event type is the next most likely. Beyond PBPM, prescriptive BPM aims at finding optimal actions based on considering relevant key performance indicators. Existing techniques are geared towards the outcome prediction and deal with alarms for interventions or interventions that do not represent process events. In this paper, we argue that the next event prediction is insufficient for practitioners. Accordingly, this research-in-progress paper proposes a technique for determining next best actions that represent process events. We conducted an intermediate evaluation to test the usefulness and the quality of our technique compared to the most frequently cited technique for predicting next events. The results show a higher usefulness for process participants than a next most likely event.<br
Keywords: Prescriptive business process monitoring; predictive business process monitoring; business process management; design science research

GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints - Zschech, Patrick; Weinzierl, Sven; Hambauer, Nico; Zilker, Sandra; Kraus, Mathias (Proceedings of the 30th European Conference on Information Systems, 2022)
The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of machine learning (ML) models. However, most techniques subsumed under XAI provide post-hoc-analytical explanations, which have to be considered with caution as they only use approximations of the underlying ML model. Therefore, our paper investigates a series of intrinsically interpretable ML models and discusses their suitability for the IS community. More specifically, our focus is on advanced extensions of generalized additive models (GAM) in which predictors are modeled independently in a non-linear way to generate shape functions that can capture arbitrary patterns but remain fully interpretable. In our study, we evaluate the prediction qualities of five GAMs as compared to six traditional ML models and assess their visual outputs for model interpretability. On this basis, we investigate their merits and limitations and derive design implications for further improvements.<br
Keywords: Predictive Analytics; Interpretable Machine Learning; Generalized Additive Models; Chancengleichheit

Guiding text-to-text privatization by syntax - Arnold, Stefan; Yesilbas, Dilara; Weinzierl, Sven (Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing, 2023)
Metric Differential Privacy is a generalization of differential privacy tailored to address the unique challenges of text-to-text privatization. By adding noise to the representation of words in the geometric space of embeddings, words are replaced with words located in the proximity of the noisy representation. Since embeddings are trained based on word co-occurrences, this mechanism ensures that substitutions stem from a common semantic context. Without considering the grammatical category of words, however, this mechanism cannot guarantee that substitutions play similar syntactic roles. We analyze the capability of text-to-text privatization to preserve the grammatical category of words after substitution and find that surrogate texts consist almost exclusively of nouns. Lacking the capability to produce surrogate texts that correlate with the structure of the sensitive texts, we encompass our analysis by transforming the privatization step into a candidate selection problem in which substitutions are directed to words with matching grammatical properties. We demonstrate a substantial improvement in the performance of downstream tasks by up to 4.66% while retaining comparative privacy guarantees.<br

How risky is my AI system? A method for transparent classification of AI system descriptions by regulated AI risk categories - Weinzierl, Sven; Zilker, Sandra; Zschech, Patrick; Kraus, Mathias; Leibelt, Tobias; Matzner, Martin (Proceedings of the 45th International Conference on Information Systems, 2024)
Risk-based artificial intelligence (AI) regulations define risk categories for AI-enabled systems. The operators of such systems must determine the risk category applicable to their AI systems. This requires detailed knowledge of the classification rules defined in the regulations. Only a few supporting tools have been developed to facilitate the task of risk classification. This paper presents a novel method that describes all the necessary steps to develop such a tool. To demonstrate and evaluate the method, it is instantiated for the European Union’s AI Act. The evaluation shows i) that the classification model achieves promising performance in predicting the risk categories for AI systems, ii) that users can effectively use the web application to carry out a risk classification, and iii) that users find SHAP text plots integrated into the web application helpful for understanding the reasons of a classification prediction.<br
Keywords: artificial intelligence regularization; EU AI Act; large language model; explainable AI; risk assessment

Interpretable generalized additive neural networks - Kraus, Mathias; Tschernutter, Daniel; Weinzierl, Sven; Zschech, Patrick (European Journal of Operational Research, 2024)
We propose Interpretable Generalized Additive Neural Networks (IGANN), a novel machine learning model that uses gradient boosting and tailored neural networks to obtain high predictive performance while being interpretable to humans. We derive an efficient training algorithm based on the theory of extreme learning machines, that allows reducing the training process to solving a sequence of regularized linear regressions. We analyze the algorithm theoretically, provide insights into the rate of change of so-called shape functions, and show that the computational complexity of the training process scales linearly with the number of samples in the training dataset. We implement IGANN in PyTorch, which allows the model to be trained on graphics processing units (GPUs) to speed up training. We demonstrate favorable results in a variety of numerical experiments and showcase IGANN’s value in three real-world case studies for productivity prediction, credit scoring, and criminal recidivism prediction.<br
Keywords: analytics; generalized additive models; gradient boosting; interpretable machine learning; neural networks; Chancengleichheit

Machine learning in business process management: A systematic literature review - Weinzierl, Sven; Zilker, Sandra; Dunzer, Sebastian; Matzner, Martin (Expert Systems With Applications, 2024)
Machine learning (ML) provides algorithms to create computer programs based on data without explicitly programming them. In business process management (BPM), ML applications are used to analyse and improve processes efficiently. Three frequent examples of using ML are providing decision support through predictions, discovering accurate process models, and improving resource allocation. This paper organises the body of knowledge on ML in BPM. We extract BPM tasks from different literature streams, summarise them under the phases of a process's lifecycle, explain how ML helps perform these tasks and identify technical commonalities in ML implementations across tasks. This study is the first exhaustive review of how ML has been used in BPM. We hope that it can open the door for a new era of cumulative research by helping researchers to identify relevant preliminary work and then combine and further develop existing approaches in a focused fashion. Our paper helps managers and consultants to find ML applications that are relevant in the current project phase of a BPM initiative, like redesigning a business process. We also offer – as a synthesis of our review – a research agenda that spreads ten avenues for future research, including applying novel ML concepts like federated learning, addressing less regarded BPM lifecycle phases like process identification, and delivering ML applications with a focus on end-users.
CRIS-Team Scopus Importer:2024-06-14
Keywords: BPM lifecycle; Business process management; Deep learning; Literature review; Machine learning

Predicting Customer Satisfaction in Service Processes Using Multilingual Large Language Models - Ließmann, Annina; Zilker, Sandra; Weinzierl, Sven; Sukhareva, Maria; Matzner, Martin (Proceedings of the 57th Hawaii International Conference on System Sciences, 2024)
The huge amount of data recorded during business process executions in today’s organizations creates the need to leverage this data. While most existing business process monitoring methods are capable of including structured context information, the incorporation of unstructured information, for example, text, has rarely been researched. Recent advances in natural language processing offer tools to generate contextualized sentence embeddings, which capture more information in human language than ever before. Especially in service processes, such as the incident management process, a variety of unstructured information is created throughout the execution, representing a relevant use case for incorporating (multilingual) text. To close this research gap, a method exploiting multilingual text for predicting the outcome of a service process is presented. Multilingual large language models are used to generate sentence embeddings for unstructured text created during process execution. After instantiating the method, an evaluation was performed using a real-life event log to predict customer satisfaction in the incident management process at a German multinational corporation. We show that text incorporation improves predictive performanc
Keywords: predictive process monitoring; natural language processing; machine learning; service analytics; customer satisfaction

Predictive business process deviation monitoring - Weinzierl, Sven; Dunzer, Sebastian; Tenschert, Johannes; Zilker, Sandra; Matzner, Martin (Proceedings of the 29th European Conference on Information Systems, 2021)
Business processes run at the core of an organisation’s value creation and are often the target of optimisation efforts. Organisations aim at adhering to their optimised processes. However, deviations from the optimised process still occur and may potentially impede efficiency in process executions. Conformance checking can provide valuable insights regarding past process deviations, but it cannot identify deviations before they occur. Outcome-oriented predictive business process monitoring (PBPM) provides a set of methods to predict process outcomes, e. g. key performance indicators. We propose an outcome-oriented PBPM method for predictive deviation monitoring using conformance checking and deep learning to draw the most out of the two domains. By leveraging early intervention, the method supports the proactive handling of deviations, i.e., inserted and missing events in process instances, to reduce their potential harm. Our evaluation shows that the method can predict business process deviations with high predictive quality, particularly for processes with fewer variants. <br
Keywords: predictive business process monitoring; conformance checking; deep learning; process mining

Predictive business process monitoring with context information from documents - Weinzierl, Sven; Revoredo, Kate; Matzner, Martin (Proceedings of the 27th European Conference on Information Systems, 2019)
Predictive business process monitoring deals with predicting a process’s future behavior or the value of process-related performance indicators based on process event data. A variety of prototypical predictive business process monitoring techniques has been proposed by researchers in order to help process participants performing business processes better. In practical settings, these techniques have a low predictive quality that is often close to random, so that predictive business process monitoring applications are rare in practice. The inclusion of process-context data has been discussed as a way to improve the predictive quality. Existing approaches have considered only structured data as context. In this paper, we argue that process-related unstructured documents are also a promising source for extracting process- context data. Accordingly, this research-in-progress paper outlines a design-science research process for creating a predictive business process monitoring technique that utilizes context data from process-related documents to predict a process instance’s next activity more accurately.
Keywords: Predictive business process monitoring; context information; documents

Predictive end-to-end enterprise process network monitoring - Oberdorf, Felix; Schaschek, Myriam; Weinzierl, Sven; Stein, Nikolai; Matzner, Martin; Flath, Christoph (Business & Information Systems Engineering, 2023)
Ever-growing data availability combined with rapid progress in analytics has laid the foundation for the emergence of business process analytics. Organizations strive to leverage predictive process analytics to obtain insights. However, current implementations are designed to deal with homogeneous data. Consequently, there is limited practical use in an organization with heterogeneous data sources. This paper proposes a method for predictive end-to-end enterprise process network monitoring leveraging multi-headed deep neural networks to overcome this limitation. A case study performed with a medium-sized German manufacturing company highlights the method’s utility for organization
Keywords: Predictive Process Analytics; Predictive Process Monitoring; Deep Learning; Machine Learning; Neural Network; Business Process Management; Process Mining

Predictive Recommining: Learning relations between event log characteristics and machine learning approaches for supporting predictive process monitoring - Drodt, Christoph; Weinzierl, Sven; Matzner, Martin; Delfmann, Patrick (Proceedings of the 35th International Conference on Advanced Information Systems Engineering Forum, 2023)
A variety of predictive process monitoring techniques based on machine learning (ML) have been proposed to improve the performance of operational processes. Existing techniques suggest different ML algorithms for training predictive models and are often optimized based on a small set of event logs. Consequently, practitioners face the challenge of finding an appropriate ML algorithm for a given event log. To overcome this challenge, this paper proposes Predictive Recommining, a framework for suggesting an ML algorithm and a sequence encoding technique for creating process predictions based on a new event log's characteristics (e.g., loops, number of traces, number of joins/splits). We show that our instantiated framework can create correct recommendations for the next activity prediction task.<br
Keywords: Predictive Process Monitoring; Machine Learning; Business Process Management; Process Mining; Decision Support

Prescriptive business process monitoring for recommending next best actions - Weinzierl, Sven; Dunzer, Sebastian; Zilker, Sandra; Matzner, Martin (Proceedings of the 18th International Conference on Business Process Management Forum, 2020)
Predictive business process monitoring (PBPM) techniques predict future process behaviour based on historical event log data to improve operational business processes. Concerning the next activity prediction, recent PBPM techniques use state-of-the-art deep neural networks (DNNs) to learn predictive models for producing more accurate predictions in running process instances. Even though organisations measure process performance by key performance indicators (KPIs), the DNN’s learning procedure is not directly affected by them. Therefore, the resulting next most likely activity predictions can be less beneficial in practice. Prescriptive business process monitoring (PrBPM) approaches assess predictions regarding their impact on the process performance (typically measured by KPIs) to prevent undesired process activities by raising alarms or recommending actions. However, none of these approaches recommends actual process activities as actions that are optimised according to a given KPI. We present a PrBPM technique that transforms the next most likely activities into the next best actions regarding a given KPI. Thereby, our technique uses business process simulation to ensure the control-flow conformance of the recommended actions. Based on our evaluation with two real-life event logs, we show that our technique’s next best actions can outperform next activity predictions regarding the optimisation of a KPI and the distance to the actual process instances. <br
Keywords: Prescriptive business process monitoring; predictive business process monitoring; business process management

Recent Advances in Data-Driven Business Process Management - Ackermann, Lars; Käppel, Martin; Marcus, Laura; Moder, Linda; Dunzer, Sebastian; Hornsteiner, Markus; Ließmann, Annina; Zisgen, Yorck; Empl, Philip; Herm, Lukas-Valentin; Neis, Nicolas; Neuberger, Julian; et al. (2024)
The rapid development of cutting-edge technologies, the increasing volume of data and also the availability and processability of new types of data sources has led to a paradigm shift in data-based management and decision-making. Since business processes are at the core of organizational work, these developments heavily impact business process management (BPM) as a crucial success factor for organizations. In view of this emerging potential, data-driven business process management has become a relevant and vibrant research area. Given the complexity and interdisciplinarity of the research field, this position paper therefore presents research insights regarding data-driven BP
Keywords: BPM; Process Mining; Research Agenda; Data-centric

Text-aware predictive process monitoring with contextualized word embeddings - Cabrera Pérez, Lena; Weinzierl, Sven; Zilker, Sandra; Matzner, Martin (Proceedings of the BPM 2022 International Workshops, 2022)
Predictive process monitoring (PPM) is the discipline of exploiting event logs of business processes to construct predictive models for anticipating different properties of running business processes. The event logs used contain control flow information of past process executions and, often, additional information about the context in which a process ran. As the process context can add valuable information to a predictive model, recent PPM techniques often incorporate it to improve process predictions. While most techniques incorporate context information as well-structured numerical and categorical context features, only a few utilize unstructured text from process-related comment fields, emails, or documents. The few existing text-aware PPM approaches are limited in capturing semantic information, as different meanings of the same word occurring in different contexts, i.e., sentences, are ignored. This paper addresses this limitation by proposing a text-aware PPM technique using contextualized word embeddings to predict the next activity and the next timestamp of running process instances. An experimental evaluation with a text-enriched real-life event log shows that our technique can outperform text-aware PPM approaches relying on non-contextualized word embeddings in terms of predictive performanc
Keywords: business process management; predictive process monitoring; machine learning; deep learning; natural language processing

The recomminder: A decision support tool for predictive business process monitoring - Drodt, Christoph; Weinzierl, Sven; Matzner, Martin; Delfmann, Patrick (Proceedings of the BPM 2021 Demonstration & Resources Track, Best BPM Dissertation Award, and Doctoral Consortium, 2021)
Predictive business process monitoring (PBPM) provides a set of techniques to optimize the performance of operational business processes. Most recent PBPM techniques learn predictive models from historical event log data using machine learning algorithms (ML). However, there is no silver bullet approach for different event logs, and their performance depends on the characteristics of the underlying event logs. This paper demonstrates the decision support tool Recomminder. The main idea of our tool is to recommend an appropriate pre-processing procedure, an ML algorithm, and the hyper-parameter configuration for a new event log based on its characteristics. While our tool can support researchers to better understand the relation between event log characteristics and ML-driven PBPM techniques, it supports practitioners in developing effective PBPM techniques.<br
Keywords: predictive business process monitoring; machine learning; business process management; process mining; decision support

Time matters: Time-aware LSTMs for predictive business process monitoring - Nguyen, An; Chatterjee, Srijeet; Weinzierl, Sven; Schwinn, Leo; Matzner, Martin; Eskofier, Björn (Proceedings of the ICPM 2020 International Workshops, 2020)
Predictive business process monitoring (PBPM) aims to predict future process behavior during ongoing process executions based on event log data. Especially, techniques for the next activity and timestamp prediction can help to improve the performance of operational business processes. Recently, many PBPM solutions based on deep learning were proposed by researchers. Due to the sequential nature of event log data, a common choice is to apply recurrent neural networks with long short-term memory (LSTM) cells. We argue, that the elapsed time between events is informative. However, current PBPM techniques mainly use “vanilla” LSTM cells and hand-crafted time-related control flow features. To better model the time dependencies between events, we propose a new PBPM technique based on time-aware LSTM (T-LSTM) cells. T-LSTM cells incorporate the elapsed time between consecutive events inherently to adjust the cell memory. Furthermore, we introduce costsensitive learning to account for the common class imbalance in event logs. Our experiments on publicly available benchmark event logs indicate the effectiveness of the introduced techniques.<br
Keywords: Predictive Business Process Monitoring; Deep Learning; Recurrent Neural Network; LSTM; Time-Awareness

Towards automated business process redesign in runtime using generative machine learning - Harl, Maximilian; Zilker, Sandra; Weinzierl, Sven (Proceedings of the 32nd European Conference on Information Systems, 2024)
In business process management, business process redesign (BPR) aims to improve business processes. In the past, BPR was mainly a manual task, with little computational power and typically high labor and time intensity. The increasing amount of stored process data and great advancements in generative machine learning (GML) and other analytical approaches have paved the way for automated BPR. However, existing BPR approaches are designed for offline applications and therefore restricted to computing historical data samples of business processes. In this paper, we argue performing BPR in runtime and leveraging prediction capabilities via GML achieves a higher degree of BPR automation, allowing organizations to improve their processes proactively. Accordingly, this research-in-progress paper outlines a design-science research process for designing a GML-based technique for automated BPR in runtime. In our preliminary evaluation, we present promising results for the proposed technique’s first online task, namely process model prediction, based on real-life event data. <br
Keywords: Business process redesign; business process management; generative artificial intelligence

Transfer Learning for Predictive Process Monitoring - Ließmann, Annina; Wang, Weixin; Weinzierl, Sven; Zilker, Sandra; Matzner, Martin (Proceedings of the 32nd European Conference on Information Systems, 2024)
Event log data reflects the behavior of business processes that are mapped in organizational information systems. Predictive process monitoring (PPM) transforms this data into value; it creates process-related predictions to provide insights required for proactive interventions at process runtime. Existing PPM techniques require sufficient amounts of event data or other relevant resources that might not be readily available, preventing some organizations from taking advantage of PPM. In this paper, we present a transfer-learning-based technique for PM, allowing organizations without suitable data or other relevant resources to implement PPM for effective decision support. We instantiate our artifact, and apply and evaluate it on a real-life use case. The use case includes two event logs for purchase-to-pay processes of two organizations. Our results provide evidence that knowledge of a process of one organization can be transferred to a similar process of another organization to enable PPM in the target organizatio
Keywords: predictive process monitoring; process mining; transfer learning; language model

XNAP: Making LSTM-based next activity predictions explainable by using LRP - Weinzierl, Sven; Zilker, Sandra; Brunk, Jens; Revoredo, Kate; Matzner, Martin; Becker, Jörg (Proceedings of the BPM 2020 International Workshops., 2020)
Predictive business process monitoring (PBPM) is a class of techniques designed to predict behaviour, such as next activities, in running traces. PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making. However, the PBPM techniques' limited predictive quality was considered as the essential obstacle for establishing such techniques in practice. With the use of deep neural networks (DNNs), the techniques' predictive quality could be improved for tasks like the next activity prediction. While DNNs achieve a promising predictive quality, they still lack comprehensibility due to their hierarchical approach of learning representations. Nevertheless, process analysts need to comprehend the cause of a prediction to identify intervention mechanisms that might affect the decision making to secure process performance. In this paper, we propose XNAP, the first explainable, DNN-based PBPM technique for the next activity prediction. XNAP integrates a layer-wise relevance propagation method from the field of explainable artificial intelligence to make predictions of a long short-term memory DNN explainable by providing relevance values for activities. We show the benefit of our approach through two real-life event logs.
Keywords: predictive business process monitoring; explainable artificial intelligence; layer-wise relevance propagation; deep neural networks.