A Light in the Dark: Deep Learning Practices for Industrial Computer Vision - Harl, Maximilian; Herchenbach, Marvin; Kruschel, Sven; Hambauer, Nico; Zschech, Patrick; Kraus, Mathias (Proceedings of the 17th International Conference on Wirtschaftsinformatik (WI), 2022)
In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV). Although these DNNs have been shown to be very well suited for general image recognition tasks, application in industry is often precluded for three reasons: 1) large pre-trained DNNs are built on hundreds of millions of parameters, making deployment on many devices impossible, 2) the underlying dataset for pre training consists of general objects, while industrial cases often consist of very specific objects, such as structures on solar wafers, 3) potentially biased pre-trained DNNs raise legal issues for companies. As a remedy, we study neural networks for CV that we train from scratch. For this purpose, we use a real-world case from a solar wafer manufacturer. We find that our neural networks achieve similar performances as pre-trained DNNs, even though they consist of far fewer parameters and do not rely on third-party datasets.<br
Keywords: Computer Vision; Deep Learning; Convolutional Neural Networks; Transfer Learning; Quality Inspection

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

Analyzing Influences on Pivotal ITO Contract Features: A Quantitative Multi-Study Design with Evidence from Western Europe - Könning, Michael; Heinrich, Kai; Zschech, Patrick; Leyh, Christian (Proceedings of the 24th Americas Conference on Information Systems, 2018)
As information technology in private and public organizations continues to gain importance, IT outsourcing (ITO) has become a critical component of corporate strategy for many institutions. Consequently, substantial research has investigated topics around ITO decisions, outcomes, and contractual governance. Despite decades of academic research, however, quantitative analyses of key contract features are still scarce. By applying three statistical lenses on a dataset of more than 1,000 ITO deals from the ASG region, we shed light on three pivotal ITO contract characteristics. Our findings are twofold: Contextually, they point to declining contract lengths and run rates, identify significant influence factors on contract value, contract length, and pricing method, and show high prediction rates in binary classification of pricing method and contract value. Methodically, they serve as a proof of concept for IS theory building by applying quantitative mixed-method approaches to similar datasets with global scope and additional variables.<br
Keywords: IT Outsourcing; ITO; Sourcing Strategy; Explanatory Analysis; Predictive Modeling

Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models - Zipfel, Justus; Verworner, Felix; Fischer, Marco; Wieland, Uwe; Kraus, Mathias; Zschech, Patrick (Computers & Industrial Engineering, 2023)
Across many industries, visual quality assurance has transitioned from a manual, labor-intensive, and error-prone task to a fully automated and precise assessment of industrial quality. This transition has been made possible due to advances in machine learning in general, and supervised learning in particular. However, the majority of supervised learning approaches only allow to identify pre-defined categories, such as certain error types on manufactured objects. New, unseen error types are unlikely to be detected by supervised models. As a remedy, this work studies unsupervised models based on deep neural networks which are not limited to a fixed set of categories but can generally assess the overall quality of objects. More specifically, we use a quality inspection case from a European car manufacturer and assess the detection performance of three unsupervised models (i.e., Skip-GANomaly, PaDiM, PatchCore). Based on an in-depth evaluation study, we demonstrate that reliable results can be achieved with fully unsupervised approaches that are even competitive with those of a supervised counterpart.
Keywords: Computer vision; Quality control; Industrial inspection; Anomaly detection; Deep learning; Unsupervised machine learning

A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems - Zschech, Patrick; Walk, Jannis; Heinrich, Kai; Vössing, Michael; Kühl, Niklas (Proceedings of the 29th European Conference on Information Systems (ECIS), 2021)
Computer vision (CV) techniques try to mimic human capabilities of visual perception to support labor-intensive and time-consuming tasks like the recognition and localization of critical objects. Nowadays, CV increasingly relies on artificial intelligence (AI) to automatically extract useful information from images that can be utilized for decision support and business process automation. However, the focus of extant research is often exclusively on technical aspects when designing AI-based CV systems while neglecting socio-technical facets, such as trust, control, and autonomy. For this purpose, we consider the design of such systems from a hybrid intelligence (HI) perspective and aim to derive prescriptive design knowledge for CV-based HI systems. We apply a reflective, practice-inspired design science approach and accumulate design knowledge from six comprehensive CV projects. As a result, we identify four design-related mechanisms (i.e., automation, signaling, modification, and collaboration) that inform our derived meta-requirements and design principles. This can serve as a basis for further socio-technical research on CV-based HI systems.
Keywords: Computer Vision; Artificial Intelligence; Hybrid Intelligence; Design Science Research

Application of Process Mining Techniques to Support Maintenance-Related Objectives - Horn, Richard; Zschech, Patrick (Proceedings of the 14th International Conference on Wirtschaftsinformatik, 2019)
The variety of data types generated in manufacturing environments leads to a situation where data-driven approaches for analytical maintenance support no longer have to be limited to the equipment level, but rather can be extended to further perspectives. To this end, this paper examines how process mining(PM) as an approach to extract knowledge about process-related relationships can be applied to support maintenance-related objectives. Our research is carried out by using exemplary data from a manufacturing company, where we successively take different data attributes from various source systems into account and apply selected PM techniques to demonstrate their applicability. As a result, we showcase how different insights can be provided, such as the analysis of a machine's internal behavior, examination of error dependencies across multiple production steps, determination of a machine’s relevance within the equipment network or the discovery of bottlenecks regarding frequencies, cycle times and costs.<br
Keywords: Data-Driven Maintenance; Maintenance Objectives; Process Mining; Data Mining; Business Analytics

Are You Up for the Challenge? Towards the Development of a Big Data Capability Assessment Model - Zschech, Patrick; Heinrich, Kai; Pfitzner, Marcus; Hilbert, Andreas (Proceedings of the 25th European Conference on Information Systems, 2017)
Utilizing Big Data scenarios that are generated from increasing digitization and data availability is a core topic in IS research. There are prospective advantages in generating business value from those scenarios through improved decision support and new business models. In order to harvest those potential advantages Big Data capabilities are required, including not only technological aspects of data management and analysis but also strategic and organisational aspects. To assess these capabilities, one can use capability assessment models. Employing a qualitative meta-analysis on existing capability assessment models, it can be revealed that the existing approaches greatly differ in their fundamental structure due to heterogeneous model elements. The heterogeneous elements are therefore synthesized and transformed into consistent assessment dimensions to fulfil the requirements of exhaustive and mutually exclusive aspects of a capability assessment model. As part of a broader research project to develop a consistent and harmonized Big Data Capability Assessment Model (BDCAM) a new design for a capability matrix is proposed including not only capability dimensions but also Big Data life cycle tasks in order to measure specific weaknesses along the process of data-driven value creation.<br
Keywords: Big Data Capabilities; Assessment Model; Capability Matrix; Business IT-Alignment

Artificial Intelligence for Sustainability—A Systematic Review of Information Systems Literature - Schoormann, Thorsten; Strobel, Gero; Möller, Frederik; Petrik, Dimitri; Zschech, Patrick (Communications of the Association for Information Systems, 2023)
The booming adoption of Artificial Intelligence (AI) likewise poses benefits and challenges. In this paper, we particularly focus on the bright side of AI and its promising potential to face our society’s grand challenges. Given this potential, different studies have already conducted valuable work by conceptualizing specific facets of AI and sustainability, including reviews on AI and Information Systems (IS) research or AI and business values. Nonetheless, there is still little holistic knowledge at the intersection of IS, AI, and sustainability. This is problematic because the IS discipline, with its socio-technical nature, has the ability to integrate perspectives beyond the currently dominant technological one as well as can advance both theory and the development of purposeful artifacts. To bridge this gap, we disclose how IS research currently makes use of AI to boost sustainable development. Based on a systematically collected corpus of 95 articles, we examine sustainability goals, data inputs, technologies and algorithms, and evaluation approaches that coin the current state of the art within the IS discipline. This comprehensive overview enables us to make more informed investments (e.g., policy and practice) as well as to discuss blind spots and possible directions for future research.<br
Keywords: Sustainable Development Goals; Sustainability; Artificial Intelligence; Information Systems; Chancengleichheit

A Survey of Image Labelling for Computer Vision Applications - Sager, Christoph; Janiesch, Christian; Zschech, Patrick (Journal of Business Analytics, 2021)
Supervised machine learning methods for image analysis require large amounts of labelled training data to solve computer vision problems. The recent rise of deep learning algorithms for recognising image content has led to the emergence of many ad-hoc labelling tools. With this survey, we capture and systematise the commonalities as well as the distinctions between existing image labelling software. We perform a structured literature review to compile the underlying concepts and features of image labelling software such as annotation expressiveness and degree of automation. We structure the manual labelling task by its organisation of work, user interface design options, and user support techniques to derive a systematisation schema for this survey. Applying it to available software and the body of literature, enabled us to uncover several application archetypes and key domains such as image retrieval or instance identification in healthcare or television.<br
Keywords: Survey; image annotation; image labelling software; supervised machine learning; methodologies and tools

A Survey of Text Representation Methods and Their Genealogy - Siebers, Philipp; Janiesch, Christian; Zschech, Patrick (IEEE Access, 2022)
In recent years, with the advent of highly scalable artificial-neural-network-based text representation methods the field of natural language processing has seen unprecedented growth and sophistication. It has become possible to distill complex linguistic information of text into multidimensional dense numeric vectors with the use of the distributional hypothesis. As a consequence, text representation methods have been evolving at such a quick pace that the research community is struggling to retain knowledge of the methods and their interrelations. We contribute threefold to this lack of compilation, composition, and systematization by providing a survey of current approaches, by arranging them in a genealogy, and by conceptualizing a taxonomy of text representation methods to examine and explain the state-of-the-art. Our research is a valuable guide and reference for artificial intelligence researchers and practitioners interested in natural language processing applications such as recommender systems, chatbots, and sentiment analysis.
CRIS-Team WoS Importer:2022-10-07

A Taxonomy of Recurring Data Analysis Problems in Maintenance Analytics - Zschech, Patrick (Proceedings of the 26th European Conference on Information Systems, 2018)
Modern maintenance strategies increasingly focus on vast amounts of diverse data and multifaceted analytical approaches in order to make efficient use of given resources and unveil hidden potentials. While there is often no universal solution approach to a specific case at hand, it is still possible to observe recurring problem classes for which generic solution templates can be applied and thus the establishment of a reusable knowledge base appears beneficial. To this end, we apply a taxonomy development approach to identify and systematize dimensions and characteristics of recurring data analysis problems in data-driven maintenance scenarios. Our research method integrates findings from a systematic literature review and expert interviews with data scientists from industry. Thus, we add descriptive theory to the field of maintenance analytics and propose a taxonomy that distinguishes between analytical maintenance objectives, data characteristics and analytical techniques.<br
Keywords: Big Data Analytics; Data Analysis Pattern; Condition-Based Maintenance; Predictive Maintenance

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

Beyond descriptive taxonomies in data analytics: a systematic evaluation approach for data-driven method pipelines - Zschech, Patrick (Information Systems and E-Business Management, 2022)
Taxonomies can serve as a valuable tool to capture dimensions and characteristics of data analytics solutions in a structured manner and thus create transparency about different design options of the technical solution space. However, previous taxonomic approaches often remain at a purely descriptive level without leveraging morphological structures to investigate the mechanisms between different combinatorial options given in data analytics pipelines. To this end, we propose a taxonomic evaluation approach to evaluate and construct the technical core of analytical information systems more systematically. Specifically, we present a rough guidance model consisting of four steps, which we subsequently instantiate with two application scenarios from the fields of industrial maintenance and predictive business process monitoring. In this way, we demonstrate how taxonomic frameworks can guide the creation of structured evaluation studies to consider the construction and assessment of data analytics pipelines in a multi-perspective and holistic manner. Our approach is sufficiently generic to be applied to various domains, scenarios, and decision support tasks.<br
Keywords: Data analytics; Taxonomy development; Evaluation framework; Ablation and substitution studies; Predictive maintenance; Predictive business process monitoring

Beyond descriptive taxonomies in data analytics: a systematic evaluation approach for data-driven method pipelines - Zschech, Patrick (Information Systems and E-Business Management, 2022)
Taxonomies can serve as a valuable tool to capture dimensions and characteristics of data analytics solutions in a structured manner and thus create transparency about different design options of the technical solution space. However, previous taxonomic approaches often remain at a purely descriptive level without leveraging morphological structures to investigate the mechanisms between different combinatorial options given in data analytics pipelines. To this end, we propose a taxonomic evaluation approach to evaluate and construct the technical core of analytical information systems more systematically. Specifically, we present a rough guidance model consisting of four steps, which we subsequently instantiate with two application scenarios from the fields of industrial maintenance and predictive business process monitoring. In this way, we demonstrate how taxonomic frameworks can guide the creation of structured evaluation studies to consider the construction and assessment of data analytics pipelines in a multi-perspective and holistic manner. Our approach is sufficiently generic to be applied to various domains, scenarios, and decision support tasks.
CRIS-Team Scopus Importer:2022-11-25
Keywords: Ablation and substitution studies; Data analytics; Evaluation framework; Predictive business process monitoring; Predictive maintenance; Taxonomy development

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

Constituent Elements for Prescriptive Analytics Systems - Stefani, Karolin; Zschech, Patrick (Proceedings of the 26th European Conference on Information Systems, 2018)
Prescriptive analytics has emerged as a technological driver in data-intensive enterprise environments, as it tries to transform valuable insights into actionable recommendations and act upon them in order to meet business objectives. The basic idea is to go beyond the findings of descriptive data analysis and predictive modeling to answer the questions “What should be done?” and “Why should it be done?”. However, there is often an inconsistent understanding about constituent elements of prescriptive analytics, which may hinder the development of adequate information systems. For this reason, the paper deals with a conceptualization by conducting a systematic literature review. The research goal is to extract fundamental aspects and facets from different perspectives and consolidate them into a coherent view towards a common understanding of a prescriptive analytics system.<br
Keywords: Decision Science; Big Data Analytics; Prescriptive Analytics; Decision Theory

Das aufstrebende Berufsbild des Data Scientist: Vom Kompetenzwirrwarr zu spezifischen Anforderungsprofilen - Schumann, Conny; Zschech, Patrick; Hilbert, Andreas (HMD : Praxis der Wirtschaftsinformatik, 2016)
To transform the variety of heterogeneous data streams into enterprise decision-relevant information, not just modern business analytics approaches have been developed in recent years. In addition, a new job profile called for attention within the rising era of Big Data: the Data Scientist. The variety of skills that come along with this new profession has been described in various technical papers and is now summarized in this article through a systematic literature review. For this purpose, the identified competences are not only enumerated, but also classified within a competency model using a content analysis. The result of this examination is that according to the literature a Data Scientist should provide an extensive skill set – including professional skills such as statistics or KDD-relevant skills for the selection, preprocessing, analysis and interpretation of data, but also social skills such as teamwork and communication, as well as personal skills such as curiosity or creativity. Here it becomes evident that a Data Scientist alone cannot meet all these competencies. Rather, it requires individual types of Data Scientists with different major focus depending on the roles and duties within the enterprise. For this purpose the article provides recommendations for the development of specific Data Scientist profiles based on the results of the literature and content analysis.<br
Keywords: Data Science; Kompetenzprofil; Handlungskompetenz; Business Analytics; Knowledge Discovery in Databases (KDD); Big Data

Data Science and Analytics in Industrial Maintenance: Selection, Evaluation, and Application of Data-Driven Methods - Zschech, Patrick (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2020)
Data-driven maintenance bears the potential to realize various benefits based on multifaceted data assets generated in increasingly digitized industrial environments. By taking advantage of modern methods and technologies from the field of data science and analytics (DSA), it is possible, for example, to gain a better understanding of complex technical processes and to anticipate impending machine faults and failures at an early stage. However, successful implementation of DSA projects requires multidisciplinary expertise, which can rarely be covered by individual employees or single units within an organization. This expertise covers, for example, a solid understanding of the domain, analytical method and modeling skills, experience in dealing with different source systems and data structures, and the ability to transfer suitable solution approaches into information systems. Against this background, various approaches have emerged in recent years to make the implementation of DSA projects more accessible to broader user groups. These include structured procedure models, systematization and modeling frameworks, domain-specific benchmark studies to illustrate best practices, standardized DSA software solutions, and intelligent assistance systems. The present thesis ties in with previous efforts and provides further contributions for their continuation. More specifically, it aims to create supportive artifacts for the selection, evaluation, and application of data-driven methods in the field of industrial maintenance. For this purpose, the thesis covers four artifacts, which were developed in several publications. These artifacts include (i) a comprehensive systematization framework for the description of central properties of recurring data analysis problems in the field of industrial maintenance, (ii) a text-based assistance system that offers advice regarding the most suitable class of analysis methods based on natural language and domain-specific problem descriptions, (iii) a taxonomic evaluation framework for the systematic assessment of data-driven methods under varying conditions, and (iv) a novel solution approach for the development of prognostic decision models in cases of missing label information. Individual research objectives guide the construction of the artifacts as part of a systematic research design. The findings are presented in a structured manner by summarizing the results of the corresponding publications. Moreover, the connections between the developed artifacts as well as related work are discussed. Subsequently, a critical reflection is offered concerning the generalization and transferability of the achieved results. Thus, the thesis not only provides a contribution based on the proposed artifacts; it also paves the way for future opportunities, for which a detailed research agenda is outline
Keywords: Industrial Maintenance; Data Science; Analytics; Data-Driven Methods; Decision Support

Data Science Skills and Enabling Enterprise Systems: Eine Erhebung von Kompetenzanforderungen und Weiterbildungsangeboten - Zschech, Patrick; Fleißner, Vera; Baumgärtel, Nicole; Hilbert, Andreas (HMD : Praxis der Wirtschaftsinformatik, 2018)
Enterprise information systems are the backbone of modern business activities. Due to the extensive use of information and communication technology as well as the increasing digitization of business processes, vast amounts of data are generated that promise a wide range of potentials for generating new insights. However, there is a lack of specialists, who have the necessary skills to conquer this situation and unveil hidden potentials from those vast amounts of data. As a result, the profession of the so-called “data scientist” has evolved as one of the most wanted job profiles of the 21st century. At the same time, numerous qualification programs have been developed in order to address the necessary skill requirements. Against this background, this paper deals with the comparison of data science competence requirements and the coverage of current qualification programs in order to outline the status quo and identify differences between demand side and supply side. For the identification of data science skill requirements from a demand perspective, existing competence profiles are systematized and consolidated into a generic scheme. The resulting categories are then used to carry out an in-depth content analysis of qualification programs currently available on the market from a supply perspective. Based on the results of both surveys, recommendations are derived for business practice and related research.<br
Keywords: Data Science; Analytics; Big Data; Business Intelligence; Competence Profile; Qualification; Education

Deep Learning in der Landwirtschaft – Analyse eines Weinbergs - Zschech, Patrick; Heinrich, Kai; Möller, Björn; Breithaupt, Lukas; Maresch, Johannes; Roth, Andreas (Big Data Analytics, 2021)
Der ubiquitäre Einsatz moderner Informations- und Kommunikationstechnologie verändert sämtliche Wirtschaftszweige und revolutioniert somit auch die Landwirtschaft. Der vorliegende Beitrag präsentiert dahingehend eine Big-Data-Analytics-Fallstudie aus dem Bereich des Weinanbaus, wo mithilfe von mobilen Aufnahmegeräten umfangreiches Bildmaterial aufgezeichnet wurde, um eine automatisierte Objekterkennung zur Unterstützung von operativen Winzertätigkeiten realisieren zu können. Dazu gehören zum Beispiel das Zählen von Reben, die Identifikation von Rebfehlstellen und die Prognose von potenziellem Erntegut. Hierbei besteht die Herausforderung unter anderem darin, landwirtschaftlich relevante Weinobjekte wie Reben, Trauben und Beeren über die einzelnen Hierarchieebenen hinweg erkennen zu können und diese auch in Bezug auf bewegtes Bildmaterial folgerichtig zu zählen. Zur Realisierung werden einige Lösungsansätze vorgestellt, die auf modernen Deep-Learning-Verfahren der bildbasierten Objekterkennung aufbauen. Der Beitrag wird abgerundet mit einer Diskussion und Implikationen für analytische Anwendungen in der landwirtschaftlichen Praxis.<br
Keywords: Agrarwirtschaft; Deep Learning; Tiefe Neuronale Netzwerke; Maschinelle Bildverarbeitung; Maschinelles Lernen; Objekterkennung; Objektverfolgung; Objektzählung; Weinanbau

Demystifying the Black Box: A Classification Scheme for Interpretation and Visualization of Deep Intelligent Systems - Heinrich, Kai; Zschech, Patrick; Skouti, Tarek; Griebenow, Jakob; Riechert, Sebastian (Proceedings of the 25th Americas Conference on Information Systems, 2019)
The evolution regarding Intelligent Systems Development, especially the advances in the field of Deep Learning Networks facilitate the design of complex prediction systems in multiple fields like image recognition or time series prediction spanning over different sectors like manufacturing or service industry. The accuracy achieved in those complex systems comes at the price of total in non-transparency of model results. In order to tackle that research gap we propose a systematic scheme that proposes several methods for interpreting and visualizing deep neural network results. We then characterize the methods and give a comprehensive overview over the current state of the art and provide limitations and further research proposals.
CRIS-Team Scopus Importer:2021-01-15
Keywords: Deep learning; Intelligent systems; Interpretation; Visualization

Ein Vergleich aktueller Deep-Learning-Architekturen zur Prognose von Prozessverhalten - Heinrich, Kai; Zschech, Patrick; Janiesch, Christian; Bonin, Markus (Proceedings of the 15th International Conference on Wirtschaftsinformatik, 2020)
Vor dem Hintergrund einer zunehmenden Datenverfügbarkeit sowie der Motivation von Unternehmen, Geschäftsprozesse kontinuierlich zu verbessern, untersucht der Beitrag verschiedene Deep-Learning-Architekturen, die im Kontext der Prozessprognose angewendet werden können, um dadurch kritische Prozessrisiken früher und besser erkennen zu können. Das Forschungsziel ist dabei die Identifikation und Gegenüberstellung aktueller Architekturkonzepte für die Prognose von Folgeereignissen in laufenden Prozessinstanzen. Dazu werden zunächst Ansätze aus dem Bereich Deep Learning identifiziert, welche ein klares Anwendungspotenzial in der Prozessprognose aufweisen. Anschließend wird ein Testdesign entwickelt, anhand dessen die identifizierten Ansätze evaluiert und systematisch gegenübergestellt werden. Die Ergebnisse zeigen, dass neuartige Deep-Learning-Architekturen konkurrenzfähige und teilweise bessere Prognosequalitäten aufweisen als die bisher in der Literatur verwendeten Ansätze.<br
Keywords: Process Prediction; Process Mining; Process Analytics; Deep Neural Networks; Deep Learning

Everything Counts: A Taxonomy of Deep Learning Approaches for Object Counting - Heinrich, Kai; Roth, Andreas; Zschech, Patrick (Proceedings of the 27th European Conference on Information Systems, 2019)
Many tasks, like process fault detection, disease diagnostic or maintaining security in public places are connected to the process of counting objects from observing image data like camera footage or micro-scopic images. However, the pivotal process of counting objects based on image data is subject to a lot of error sources when done manually. Therefore, a lot of approaches exist that aim to aid the automation of the counting process. Since most of those methods are of black box nature and have to be applied to real world scenarios, we provide a taxonomy that reflects both: the method characteristic as well as the counting problem characteristics in order to derive a systematization of the scientific field and to provide a tool that acts as a set of guidelines for choosing appropriate counting methods for different situations. We first conduct a literature review, where counting problem characteristics and solutions are extracted and later transformed into a taxonomy. We finally showcase the taxonomy using four case studies that are based on publicly available datasets for the sake of scientific comprehension.<br
Keywords: Deep Learning; Counting; Survey; Object Detection; Taxonomy

Existieren Wissensmanagement-Schulen? Eine Clusteranalyse von Wissensmanagement-Beiträgen aus den letzten 10 Jahren - Kruse, Paul; Kummer, Christian; Zschech, Patrick (17. Workshop Gemeinschaften in Neuen Medien: Virtual Enterprises, Research Communities and Social Media Networks - Online Communities: Technologies and Analyses for Networks in Industry, Research and Education, (GeNeMe), 2014)
Die Kommunikation in Forschungsgemeinschaften ist seit jeher ein vielschichtiger und komplexer Prozess. In den meisten Communities kommt es daher häufig zu Missverständnissen oder widersprüchlichen Auffassungen. Trotz seiner über 20-jährigen Historie ist besonders das Forschungsfeld Wissensmanagement mit einer Reihe von Schwierigkeiten und tiefgehenden Missverständnissen konfrontiert. Allem voran fällt die mangelnde Kumulativität der Debatte auf. Statt von einer interdisziplinär-synergetischen Betrachtung zu profitieren, liegt eine heterogene Landschaft an Grundbegriffen, Modellen, Theorien und Instrumenten vor, welche in ihren Aussagen teilweise unvereinbar und konkurrierend zueinander stehen – ein Hinweis auf die noch geringe wissenschaftliche Reife des Forschungsfeldes Wissensmanagement.<br
Keywords: Knowledge management; Education

Fool Me Once, Shame On You, Fool Me Twice, Shame On Me: A Taxonomy of Attack and Defense Patterns for AI Security - Heinrich, Kai; Graf, Johannes; Chen, Ji; Laurisch, Jakob; Zschech, Patrick (Proceedings of the 28th European Conference on Information Systems, 2020)
Advances in the area of AI systems lead to the application of complex deep neural networks (DNN) that outperform other algorithms in critical applications like predictive maintenance, healthcare or autonomous driving. Unfortunately, the properties that render them so successful also lead to vulnerabilities that can make them the subject of adversarial attacks. While these systems try to mimic human behavior when transforming large amounts of data into decision recommendations, they remain black-box models so that humans often fail to detect adversarial behavior patterns in the model training process. Therefore, we derive a taxonomy from an extensive literature review to structure the knowledge of possible attack and defense patterns to create a basis for the analysis and implementation of AI security for scientists and practitioners alike. Furthermore, we use the taxonomy to expose the most common attack pattern and, in addition, we demonstrate the application of the taxonomy by projecting two real-world cases onto the taxonomy space and discuss applicable attack and defense patterns.<br
Keywords: Adversarial Machine Learning; Deep Neural Networks; Cybersecurity; Taxonomy

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

Generative AI - Feuerriegel, Stefan; Hartmann, Jochen; Janiesch, Christian; Zschech, Patrick (Business & Information Systems Engineering, 2023)
CRIS-Team Scopus Importer:2023-09-22
Keywords: Artificial intelligence; Content creation; Decision support; Generative AI; Information systems; Chancengleichheit

How Much AI Do You Require? Decision Factors for Adopting AI Technology - Wanner, Jonas; Heinrich, Kai; Janiesch, Christian; Zschech, Patrick (Proceedings of the 41st International Conference on Information Systems, 2020)
Artificial intelligence (AI) based on machine learning technology disrupts how knowledge is gained. Nevertheless, ML’s improved accuracy of prediction comes at the cost of low traceability due to its black-box nature. The field of explainable AI tries to counter this. However, for practical use in IT projects, these two research streams offer only partial advice for AI adoption as the trade-off between accuracy and explainability has not been adequately discussed yet. Thus, we simulate a decision process by implementing three best practice AI-based decision support systems for a high-stake maintenance decision scenario and evaluate the decision and attitude factors using the Analytical Hierarchy Process (AHP) through an expert survey. The combined results indicate that system performance is still the most important factor and that implementation effort and explainability are relatively even factors. Further, we found that systems using similarity-based matching or direct modeling for remaining useful life estimation performed best.<br
Keywords: Artificial Intelligence; AI Adoption; Explainable AI; Analytical Hierarchy Process

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

Intelligent User Assistance for Automated Data Mining Method Selection - Zschech, Patrick; Horn, Richard; Hoeschele, Daniel; Janiesch, Christian; Heinrich, Kai (Business & Information Systems Engineering, 2020)
In any data science and analytics project, the task of mapping a domain-specific problem to an adequate set of data mining methods by experts of the field is a crucial step. However, these experts are not always available and data mining novices may be required to perform the task. While there are several research efforts for automated method selection as a means of support, only a few approaches consider the particularities of problems expressed in the natural and domain-specific language of the novice. The study proposes the design of an intelligent assistance system that takes problem descriptions articulated in natural language as an input and offers advice regarding the most suitable class of data mining methods. Following a design science research approach, the paper (i) outlines the problem setting with an exemplary scenario from industrial practice, (ii) derives design requirements, (iii) develops design principles and proposes design features, (iv) develops and implements the IT artifact using several methods such as embeddings, keyword extractions, topic models, and text classifiers, (v) demonstrates and evaluates the implemented prototype based on different classification pipelines, and (vi) discusses the results’ practical and theoretical contributions. The best performing classification pipelines show high accuracies when applied to validation data and are capable of creating a suitable mapping that exceeds the performance of joint novice assessments and simpler means of text mining. The research provides a promising foundation for further enhancements, either as a stand-alone intelligent assistance system or as an add-on to already existing data science and analytics platforms.
Created from Fastlane, Scopus look-up
Keywords: Automated method selection; Data science; Design science research; Intelligent user assistance system; Natural language processing

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

Is Bigger Always Better? Lessons Learnt from the Evolution of Deep Learning Architectures for Image Classification - Heinrich, Kai; Möller, Björn; Janiesch, Christian; Zschech, Patrick (Proceedings of the Pre-ICIS SIGDSA Symposium, 2019)
There exist numerous scientific contributions to the design of deep learning networks. However, using the right architecture that is suited for a given business problem with all constraints such as memory and inference time requirements can be cumbersome. We reflect on the evolution of the state-of-the-art architectures for convolutional neural networks(CNN) for the case of image classification. We compare architectures regarding classification results, model size, and inference time to discuss the choices of designs for CNN architectures. To maintain scientific comprehensibility, the established ILSVRC benchmark is used as a basis for model selection and benchmark data. The quantitative comparison shows that while the model size and the required inference time correlate with result accuracy across all architectures, there are major trade-offs between those factors. The qualitative analysis further depicts that published models always build on previous research and adopt improved components in either evolutionary or revolutionary ways. Finally, we discuss design and result improvement during the evolution of CNN architectures. Further, we derive practical implications for designing deep learning networks<br
Keywords: Deep Learning Architecture; Convolutional Neural Network; Image Classification; Evaluation; Modeling

labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds - Sager, Christoph; Zschech, Patrick; Kühl, Niklas (Proceedings of CAD’21, 2021)
Keywords: 3D Object Detection; Labeling Tool; Point Clouds; Bounding Boxes

labelCloud: A Lightweight Labeling Tool for Domain-Agnostic 3D Object Detection in Point Clouds - Sager, Christoph; Zschech, Patrick; Kühl, Niklas (Computer-Aided Design and Applications, 2022)
The rapid development of 3D sensors and object detection methods based on 3D point clouds has led to increasing demand for labeling tools that provide suitable training data. However, existing labeling tools mostly focus on a single use case and generate bounding boxes only indirectly from a selection of points, which often impairs the label quality. Therefore, this work describes labelCloud, a generic point cloud labeling tool that can process all common file formats and provides 3D bounding boxes in multiple label formats. labelCloud offers two labeling methods that let users draw rotated bounding boxes directly inside the point cloud. Compared to a labeling tool based on indirect labeling, labelCloud could significantly increase the label precision while slightly reducing the labeling time. Due to its modular architecture, researchers and practitioners can adapt the software to their individual needs. With labelCloud, we contribute to enabling convenient 3D vision research in novel application domains.<br
Keywords: 3D object detection; labeling tool; point clouds; bounding boxes

Leveraging interpretable machine learning in intensive care - Bohlen, Lasse; Rosenberger, Julian; Zschech, Patrick; Kraus, Mathias (Annals of Operations Research, 2024)
In healthcare, especially within intensive care units (ICU), informed decision-making by medical professionals is crucial due to the complexity of medical data. Healthcare analytics seeks to support these decisions by generating accurate predictions through advanced machine learning (ML) models, such as boosted decision trees and random forests. While these models frequently exhibit accurate predictions across various medical tasks, they often lack interpretability. To address this challenge, researchers have developed interpretable ML models that balance accuracy and interpretability. In this study, we evaluate the performance gap between interpretable and black-box models in two healthcare prediction tasks, mortality and length-of-stay prediction in ICU settings. We focus specifically on the family of generalized additive models (GAMs) as powerful interpretable ML models. Our assessment uses the publicly available Medical Information Mart for Intensive Care dataset, and we analyze the models based on (i) predictive performance, (ii) the influence of compact feature sets (i.e., only few features) on predictive performance, and (iii) interpretability and consistency with medical knowledge. Our results show that interpretable models achieve competitive performance, with a minor decrease of 0.2–0.9 percentage points in area under the receiver operating characteristic relative to state-of-the-art black-box models, while preserving complete interpretability. This remains true even for parsimonious models that use only 2.2 % of patient features. Our study highlights the potential of interpretable models to improve decision-making in ICUs by providing medical professionals with easily understandable and verifiable predictions.
CRIS-Team Scopus Importer:2024-09-27
Keywords: Generalized additive models; Healthcare analytics; Interpretable machine learning; Length-of-stay prediction; Mortality prediction

Machine Learning and Deep Learning - Janiesch, Christian; Zschech, Patrick; Heinrich, Kai (Electronic Markets, 2021)
Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.<br
Keywords: Machine learning; Deep learning; Artificial intelligence; Artificial neural networks; Analytical model building

Mit Computer Vision zur automatisierten Qualitätssicherung in der industriellen Fertigung: Eine Fallstudie zur Klassifizierung von Fehlern in Solarzellen mittels Elektrolumineszenz-Bildern - Zschech, Patrick; Sager, Christoph; Siebers, Philipp; Pertermann, Maik (HMD : Praxis der Wirtschaftsinformatik, 2021)
Quality assurance in the production of solar cells is a decisive factor for long-term performance guarantees on solar panels. This work contributes to this area in developing computer vision models to automatically detect defects on wafers by classifying electroluminescence images from a real manufacturing scenario. The challenge is not only to separate defective wafers from flawless ones but also to distinguish between specific types of defects while ensuring low inference times. For this purpose, simple statistical models, as well as different kinds of deep learning architectures based on convolutional neural networks (CNNs), are tested and compared with each other. Therefore, this work aims to evaluate multiple classification approaches of varying complexity levels while examining their practical applicability under real industrial conditions. The case study shows that all models have their right to exist and achieve excellent results in combination. While statistical models and simple CNNs provide reliable statements with accuracies up to 99% for defect types of simple to medium detectability, more advanced approaches based on region proposal networks are required once the defect images become more diffuse. The more advanced approaches allow a precise object localization of defects; however, they are also associated with increased labeling effort when annotating wafer images. Since the implementation of all models is based exclusively on open source tools such as TensorFlow, Keras, and OpenCV, the case study also demonstrates the possibilities offered by freely accessible solutions in the field of computer vision.<br
Keywords: Deep learning; Industry; Computer vision; Object detection; Photovoltaic; Quality assurance

Objekterkennung im Weinanbau – Eine Fallstudie zur Unterstützung von Winzertätigkeiten mithilfe von Deep Learning - Heinrich, Kai; Zschech, Patrick; Möller, Björn; Breithaupt, Lukas; Maresch, Johannes (HMD : Praxis der Wirtschaftsinformatik, 2019)
The transformation towards a digitized world introduces major changes to all economic sectors, among them the sector of agriculture, where intelligent information systems help to gather and analyze vast amounts of data to provide new business functions and models. Given this background, this article describes a big data analytics case study from the field of viticulture, where extensive image material was recorded using mobile recording devices in order to implement automated object detection to support operational vineyard activities, such as counting vines, identifying missing plants or predicting potential harvests. One of the challenges here was to correctly identify relevant wine objects such as vines, grapes and berries across their different hierarchical levels and to consistently count them in relation to moving image material. The authors provide a solution to those challenges by designing a data analysis process based on a deep learning framework for object detection. Additionally, the results as well as implications for the application of the proposed models in the field of agrarian management are discussed at the end of the article.<br
Keywords: Agriculture; Computer Vision; Deep Learning; Object Counting; Object Detection; Object Tracking; Winemaking Industry

Predictive Maintenance in der industriellen Praxis: Entwicklung eines Prognoseansatzes unter eingeschränkter Informationslage - Bink, Raphael; Zschech, Patrick (HMD : Praxis der Wirtschaftsinformatik, 2018)
Predictive maintenance is an anticipatory maintenance strategy designed to predict impending errors or the remaining useful lifetime of a technical system. With its predictions, maintenance actions can be taken condition-based and proactively leading to an efficient use of resources. The basis for this approach is the recording of extensive machine data and their analysis with means of machine learning to learn complex relationships between condition data and related target variables, e. g. the remaining useful life. However, the existence of numerous monitoring data does not imply that there is a good information base for the development of meaningful predictive models. In this paper, for example, a study is presented where the challenge was to predict the optimal time to replace a wear-induced tool of a milling machine. Since in the past the milling tools were often replaced way before the end of their actual lifetime, a predictive model based on this training data would describe a risk averse and thus non-optimal maintenance strategy. Against this background, the present paper deals with the development of a methodology for optimizing a maintenance strategy based on incomplete information from industrial practice. The proposed methodology is based on a broad range of analytical techniques, including feature extraction for the preprocessing of time series data, unsupervised learning for clustering condition monitoring data and a recurrent neural network for the development of a predictive model to determine the remaining useful life. With the approach developed, it is possible to replace decisions which were so far taken based on subjective criteria with data-driven decisions to increase the milling tool lifetime. Additionally, this work contributes to the question how to develop decision support systems despite vague and insufficient information with the means of machine learning.<br
Keywords: Data Science; Maintenance Analytics; Condition-Based Maintenance; Prognostics; Censored Data; Imperfect Maintenance

Prescriptive analytics systems revised: a systematic literature review from an information systems perspective - Wissuchek, Christopher; Zschech, Patrick (Information Systems and E-Business Management, 2024)
Prescriptive Analytics Systems (PAS) represent the most mature iteration of business analytics, significantly enhancing organizational decision-making. Recently, research has gained traction, with various technological innovations, including machine learning and artificial intelligence, significantly influencing the design of PAS. Although recent studies highlight these developments, the rising trend focuses on broader implications, such as the synergies and delegation between systems and users in organizational decision-making environments. Against this backdrop, we utilized a systematic literature review of 262 articles to build on this evolving perspective. Guided by general systems theory and socio-technical thinking, the concept of an information systems artifact directed this review. Our first objective was to clarify the essential subsystems, identifying 23 constituent components of PAS. Subsequently, we delved into the meta-level design of PAS, emphasizing the synergy and delegation between the human decision-maker and prescriptive analytics in supporting organizational decisions. From this exploration, four distinct system archetypes emerged: advisory, executive, adaptive, and self-governing PAS. Lastly, we engaged with affordance theory, illuminating the action potential of PAS. Our study advances the perspective on PAS, specifically from a broader socio-technical and information systems viewpoint, highlighting six distinct research directions, acting as a launchpad for future research in the domain.
CRIS-Team Scopus Importer:2024-09-06
Keywords: Artificial intelligence; Business analytics; Decision support system; Decision-making; Prescriptive analytics; Systematic literature review

Process Analytics - Hilbert, Andreas; Zschech, Patrick (Das Wirtschaftsstudium, 2016)
Prozessanalysen sind ein zentrales Mittel, um Prozesse kontinuierlich zu verbessern und so den wechselnden Anforderungen und dynamischen Wechselwirkungen im Geschäftsalltag zu begegnen. Konventionelle Analysetechniken wie die Prozessmodellierung stoßen jedoch bei hohen Datenaufkommen zunehmend an ihre Grenzen. Deshalb sind weiterentwickelte Analytics-Ansätze erforderlich, die explorativ und hypothesenfrei mit großen Datenmengen umgehen können. Dafür eignen sich vor allem Data Mining und Process Mining. <br

Process Data Properties Matter: Introducing Gated Convolutional Neural Networks (GCNN) and Key-Value-Predict Attention Networks (KVP) for Next Event Prediction with Deep Learning - Heinrich, Kai; Zschech, Patrick; Janiesch, Christian; Bonin, Markus (Decision Support Systems, 2021)
Predicting next events in predictive process monitoring enables companies to manage and control processes at an early stage and reduce their action distance. In recent years, approaches have steadily moved from classical statistical methods towards the application of deep neural network architectures, which outperform the former and enable analysis without explicit knowledge of the underlying process model. While the focus of prior research was on the long short-term memory network architecture, more deep learning architectures offer promising extensions that have proven useful for other applications of sequential data. In our work, we introduce a gated convolutional neural network and a key-value-predict attention network to the task of next event prediction. In a comprehensive evaluation study on 11 real-life benchmark datasets, we show that these two novel architectures surpass prior work in 34 out of 44 metric-dataset combinations. For our evaluation, we consider the effects of process data properties, such as sparsity, variation, and repetitiveness, and discuss their impact on the prediction quality of the different deep learning architectures. Similarly, we evaluate their classification properties in terms of generalization and handling class imbalance. Our results provide guidance for researchers and practitioners alike on how to select, validate, and comprehensively benchmark (novel) predictive process monitoring models. In particular, we highlight the importance of sufficiently diverse process data properties in event logs and the comprehensive reporting of multiple performance indicators to achieve meaningful results.<br
Keywords: Process mining; Predictive process monitoring; Machine learning; Deep learning; Gated convolutional neural network; Key-value-predict attention network

Prognostic Model Development with Missing Labels: A Condition-Based Maintenance Approach Using Machine Learning - Zschech, Patrick; Heinrich, Kai; Bink, Raphael; Neufeld, Janis S. (Business & Information Systems Engineering, 2019)
Condition-based maintenance (CBM) has emerged as a proactive strategy for determining the best time for maintenance activities. In this paper, a case of a milling process with imperfect maintenance at a German automotive manufacturer is considered. Its major challenge is that only data with missing labels are available, which does not provide a sufficient basis for classical prognostic maintenance models. To overcome this shortcoming, a data science study is carried out that combines several analytical methods, especially from the field of machine learning (ML). These include time-domain and time–frequency domain techniques for feature extraction, agglomerative hierarchical clustering and time series clustering for unsupervised pattern detection, as well as a recurrent neural network for prognostic model training. With the approach developed, it is possible to replace decisions that were made based on subjective criteria with data-driven decisions to increase the tool life of the milling machines. The solution can be employed beyond the presented case to similar maintenance scenarios as the basis for decision support and prognostic model development. Moreover, it helps to further close the gap between ML research and the practical implementation of CBM.
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Keywords: Big data analytics; Condition-based maintenance; Data science study; Machine learning; Missing labels; Predictive maintenance; Prognostics; Unsupervised learning

Review and Systematization of Solutions for 3D Object Detection - Friederich, Jonas; Zschech, Patrick (Proceedings of the 15th International Conference on Wirtschaftsinformatik, 2020)
Since 2017 there has been an exponential growth in scientific publications regarding the field of 3D object detection (3DOD). On the one hand, this growth can be explained by the strong demand for autonomous vehicles, and on the other hand, by the wide availability of 3D sensors. Due to the strong heterogeneity of developed approaches, this paper aims to identify, analyze and systematize publications that propose end-to-end solutions for 3DOD towards the goal to provide a structured framework which can guide future development, evaluation and application activities. To carry out the research, a systematic literature review is combined with a taxonomy development approach. The resulting framework consists of six dimensions, covering the addressed domains, applied datasets, sensor properties, data representation formats, modeling techniques, and evaluation criteria. The taxonomy can help researchers and practitioners to get a quick overview about the field by decomposing 3DOD solutions into more manageable pieces.<br
Keywords: Data Science; Computer Vision; 3D Object Detection; Taxonomy

Survey and systematization of 3D object detection models and methods - Drobnitzky, Moritz; Friederich, Jonas; Egger, Bernhard; Zschech, Patrick (Visual Computer, 2024)
Strong demand for autonomous vehicles and the wide availability of 3D sensors are continuously fueling the proposal of novel methods for 3D object detection. In this paper, we provide a comprehensive survey of recent developments from 2012–2021 in 3D object detection covering the full pipeline from input data, over data representation and feature extraction to the actual detection modules. We introduce fundamental concepts, focus on a broad range of different approaches that have emerged over the past decade, and propose a systematization that provides a practical framework for comparing these approaches with the goal of guiding future development, evaluation, and application activities. Specifically, our survey and systematization of 3D object detection models and methods can help researchers and practitioners to get a quick overview of the field by decomposing 3DOD solutions into more manageable pieces.
CRIS-Team Scopus Importer:2024-02-23
Keywords: 3D object detection; 6-DoF; Survey; Tracking

The Impact of Transparency in AI Systems on Users’ Data-Sharing Intentions: A Scenario-Based Experiment - Rosenberger, Julian; Kuhlemann, Sophie; Tiefenbeck, Verena; Kraus, Mathias; Zschech, Patrick (19. Internationale Tagung Wirtschaftsinformatik, 2024)

Towards a Taxonomic Benchmarking Framework for Predictive Maintenance: The Case of NASA’s Turbofan Degradation - Zschech, Patrick; Bernien, Jonas; Heinrich, Kai (Proceedings of the 40th International Conference on Information Systems, 2019)
The availability of datasets for analytical solution development is a common bottleneck in data-driven predictive maintenance. Therefore, novel solutions are mostly based on synthetic benchmarking examples, such as NASA’s C-MAPSS datasets, where researchers from various disciplines like artificial intelligence and statistics apply and test their methodical approaches. The majority of studies, however, only evaluate the overall solution against a final prediction score, where we argue that a more fine-grained consideration is required distinguishing between detailed method components to measure their particular impact along the prognostic development process. To address this issue, we first conduct a literature review resulting in more than one hundred studies using the C-MAPSS datasets. Subsequently, we apply a taxonomy approach to receive dimensions and characteristics that decompose complex analytical solutions into more manageable components. The result is a first draft of a systematic benchmarking framework as a more comparable basis for future development and evaluation purposes.<br
Keywords: Data Science; Maintenance; Evaluation Framework; Review; Prognostics; Taxonomy

Towards a Text-based Recommender System for Data Mining Method Selection - Zschech, Patrick; Heinrich, Kai; Horn, Richard; Hoeschele, Daniel (Proceedings of the 25th Americas Conference on Information Systems, 2019)
The task of mapping a domain-specific problem space to an adequate set of data mining (DM) methods is a crucial step in data science projects. While there have been several efforts for automated method selection in general, only few approaches consider the particularities of problem contexts expressed in domain-specific language. Therefore, we propose the concept of a text-based recommender system (TBRS) which takes problem descriptions articulated in domain language as inputs and then recommends the best suitable class of DM methods. Following a design science research methodology, the current focus is on the initial steps of motivating the problem and conducting a requirements analysis. In particular, we outline the problem setting using an exemplary scenario from industrial practice and derive requirements towards an adequate solution artifact. Subsequently, we discuss potential TBRS methods with regard to requirement fulfillment while organizing both methods and requirements in a structured framework. Finally, we conclude the paper, discuss limitations and draw an outlook.<br
Keywords: Automated method selection; Design science research; Data mining; Intelligent information system; Domain knowledge; Recommendation system; Natural language processing; Text mining

Tweeting in IIoT Ecosystems – Empirical Insights from Social Media Analytics about IIoT Platforms - Petrik, Dimitri; Pantow, Katharina; Zschech, Patrick; Herzwurm, Georg (Proceedings of the 16th International Conference on Wirtschaftsinformatik (WI), 2021)
The market for the Industrial Internet of Things (IIoT) platforms remains highly dynamic and is rapidly evolving regarding the growth of the platform-based ecosystems. However, digital platforms, used in the industrial business-to-business setting, differ significantly from the established platforms in the business-to-consumer domains and remain little researched. In this study, we apply a data-driven approach and conduct bottom-up and top-down content analysis, exploring social media data on the current state of IIoT platforms. For a top-down analysis, we draw on the theoretical concept of platform boundary resources. Specifically, we apply descriptive analytics and topic modeling on the Twitter data regarding the market-ready IIoT platforms Adamos, Cumulocity, Watson IoT, MindSphere, Leonardo, and ThingWorx, thus conducting an exploratory multiple case study. Our findings generate descriptive insights on the currently discussed topics in the area of IIoT platforms, contributing to the knowledge of the current state of digital platforms used in IIoT.<br
Keywords: Industrial IoT; IoT Platform; Platform Strategy; Boundary Resources; Twitter Analytics

Vom Controller zum Prozessanalysten - Zschech, Patrick; Pfitzner, Marcus; Hilbert, Andreas (Controlling & Management Review, 2017)
Geschäftsprozesse im digitalen Zeitalter basieren nahezu vollständig auf einer Unterstützung durch IT-Systeme. Dabei hinterlassen sie jede Menge Spuren, die es im Rahmen einer ganzheitlichen Prozesssteuerung aufzugreifen und auszuwerten gilt. Process Mining erweist sich hierbei als hilfreiches Controlling-Werkzeug, um Missstände aufzudecken und Abläufe zu verbessern.<br

Where Was COVID-19 First Discovered? Designing a Question-Answering System for Pandemic Situations - Graf, Johannes; Lancho, Gino; Zschech, Patrick; Heinrich, Kai (Proceedings of the 30th European Conference on Information Systems, 2022)
The COVID-19 pandemic is accompanied by a massive “infodemic” that makes it hard to identify concise and credible information for COVID-19-related questions, like incubation time, infection rates, or the effectiveness of vaccines. As a novel solution, our paper is concerned with designing a question-answering system based on modern technologies from natural language processing to overcome information overload and misinformation in pandemic situations. To carry out our research, we followed a design science research approach and applied Ingwersen’s cognitive model of information retrieval interaction to inform our design process from a socio-technical lens. On this basis, we derived prescriptive design knowledge in terms of design requirements and design principles, which we translated into the construction of a prototypical instantiation. Our implementation is based on the comprehensive CORD-19 dataset, and we demonstrate our artifact’s usefulness by evaluating its answer quality based on a sample of COVID-19 questions labeled by biomedical experts.<br
Keywords: COVID-19; Information Retrieval; Question-Answering; Design Science Research; Inklusion

White, Grey, Black: Effects of XAI Augmentation on the Confidence in AI-based Decision Support Systems - Wanner, Jonas; Herm, Lukas-Valentin; Heinrich, Kai; Janiesch, Christian; Zschech, Patrick (Proceedings of the 40th International Conference on Information Systems, 2020)
AI-based decision support systems (DSS) have become increasingly popular for solving a variety of tasks in both, low-stake, and high-stake situations. However, due to their complexity, they often lack transparency into their decision process. Therefore, the field of explainable AI (XAI) has emerged to provide explanations for these black-box systems. While XAI research assumes an increase in confidence when using their augmented grey-box systems, test designs for this proposition are scarce. Therefore, we propose an empirical study to test the effect of black-box, grey-box, and white-box explanations on a domain expert’s confidence in the system, and subsequently on the effectiveness of the overall decision process. For this purpose, we derive hypotheses from theory and implement AI-based DSS with XAI augmentations for low-stake and high-stake situations. Further, we provide detailed information on a future survey-based study, which we will conduct to complete this research-in-progress.<br
Keywords: Explainable AI; Confidence; Decision Support Systems; User-study; Maintenance