Multivariate time series classification has been broadly applied in diverse domains over the past few decades. However, before applying the classification algorithms, the vast majority of current ...studies extract hand-engineered features that are assumed to detect local patterns in the time series. Therefore, the efficiency and precision of these classification approaches are heavily dependent on the quality of variables defined by domain experts. Recent improvements in the deep learning domain offer opportunities to avoid such an intensive hand-crafted feature engineering which is particularly important for managing the processes based on time-series data obtained from various sensor networks. In our paper, we propose a framework to extract the features in an unsupervised (or self-supervised) manner using deep learning, particularly stacked LSTM Autoencoder Networks. The compressed representation of the time-series data obtained from LSTM Autoencoders are then provided to Deep Feedforward Neural Networks for classification. We apply the proposed framework on sensor time series data from the process industry to detect the quality of the semi-finished products and accordingly predict the next production process step. To validate the efficiency of the proposed approach, we used real-world data from the steel industry.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Artificial ants are “small” units, moving autonomously on a shared, dynamically changing “space”, directly or indirectly exchanging some kind of information. Artificial ants are frequently conceived ...as a paradigm for collective adaptive systems. In this paper, we discuss means to represent continuous moves of “ants” in discrete models. More generally, we challenge the role of the notion of “time” in artificial ant systems and models. We suggest a modeling framework that structures behavior along causal dependencies rather than temporal relations. We present all arguments with the help of a simple example. As a modeling framework we employ
Heraklit
; an emerging framework that has already proven its worth in many contexts. Different concrete collective systems share similar features, despite differences in the size of basic sets, concrete values of functions, etc., and can therefore be conceived as instantiations of a single schema. Hence, we need a representation of systems on the schematic level.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Abstract In the rapidly evolving landscape of manufacturing, the ability to make accurate predictions is crucial for optimizing processes. This study introduces a novel framework that combines ...predictive uncertainty with explanatory mechanisms to enhance decision-making in complex systems. The approach leverages Quantile Regression Forests for reliable predictive process monitoring and incorporates Shapley Additive Explanations (SHAP) to identify the drivers of predictive uncertainty. This dual-faceted strategy serves as a valuable tool for domain experts engaged in process planning activities. Supported by a real-world case study involving a medium-sized German manufacturing firm, the article validates the model’s effectiveness through rigorous evaluations, including sensitivity analyses and tests for statistical significance. By seamlessly integrating uncertainty quantification with explainable artificial intelligence, this research makes a novel contribution to the evolving discourse on intelligent decision-making in complex systems.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
4.
Industry 4.0 Lasi, Heiner; Fettke, Peter; Kemper, Hans-Georg ...
Business & information systems engineering,
08/2014, Volume:
6, Issue:
4
Journal Article
How Conceptual Modeling Is Used Fettke, Peter
Communications of the Association for Information Systems,
2009, Volume:
25
Journal Article
Peer reviewed
Open access
Conceptual models play an increasingly important role for business process engineering, information systems development, and customizing of Enterprise Resource Planning (ERP) systems. Despite the ...widespread interest in conceptual modeling, relatively little is known to date on the level and nature of conceptual modeling use in practice. Therefore our study investigates how practitioners use conceptual modeling. In particular, we address the following three key questions: To what extent do practitioners use conceptual modeling techniques and tools? How relevant is conceptual modeling for certain purposes? Are there barriers and success factors in using conceptual modeling? This paper informs information systems professionals about recent trends in the area of conceptual modeling. The results of our study should be considered when developing syllabuses for modeling courses as well as when judging the relevance of various research streams in the area of conceptual modeling.
The ability to proactively monitor business processes is a main competitive differentiator for firms. Process execution logs generated by process aware information systems help to make process ...specific predictions for enabling a proactive situational awareness. The goal of the proposed approach is to predict the next process event from the completed activities of the running process instance, based on the execution log data from previously completed process instances. By predicting process events, companies can initiate timely interventions to address undesired deviations from the desired workflow. The paper proposes a multi-stage deep learning approach that formulates the next event prediction problem as a classification problem. Following a feature pre-processing stage with n-grams and feature hashing, a deep learning model consisting of an unsupervised pre-training component with stacked autoencoders and a supervised fine-tuning component is applied. Experiments on a variety of business process log datasets show that the multi-stage deep learning approach provides promising results. The study also compared the results to existing deep recurrent neural networks and conventional classification approaches. Furthermore, the paper addresses the identification of suitable hyperparameters for the proposed approach, and the handling of the imbalanced nature of business process event datasets.
Business process models play an important role in today’s enterprises, hence, model repositories may contain hundreds of models. These models are, for example, reused during process modeling ...activities or utilized to check the conformance of processes with legal regulations. With respect to the amount of models, such applications benefit from or even require detailed insights into the correspondences between process models or between process models’ nodes. Therefore, various process similarity and matching measures have been proposed during the past few years. This article provides an overview of the state-of-the-art regarding business process model similarity measures and aims at analyzing which similarity measures exist, how they are characterized, and what kind of calculations are typically applied to determine similarity values. Finally, the analysis of 123 similarity measures results in the suggestions to conduct further comparative analyses of similarity measures, to investigate the integration of human input into similarity measurement, and to further analyze the requirements of similarity measurement usage scenarios as future research opportunities.
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IZUM, KILJ, NUK, PILJ, SAZU, UL, UM, UPUK
In this study, we propose a pioneering framework for generating multi-objective counterfactual explanations in job-shop scheduling contexts, combining predictive process monitoring with advanced ...mathematical optimization techniques. Using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization, our approach enhances the generation of counterfactual explanations that illuminate potential enhancements at both the operational and systemic levels. Validated with real-world data, our methodology underscores the superiority of NSGA-II in crafting pertinent and actionable counterfactual explanations, surpassing traditional methods in both efficiency and practical relevance. This work advances the domains of explainable artificial intelligence (XAI), predictive process monitoring, and combinatorial optimization, providing an effective tool for improving automated scheduling systems’ clarity, and decision-making capabilities.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Purpose - The paper aims at providing a survey of the development of empirical research in business process management (BPM). It seeks to study trends in empirical BPM research and applied ...methodologies by means of a developed framework in order to identify the status quo and to assess the probable future development of the research field.Design methodology approach - In order to analyse the development of the research field a systematic literature review of empirical journal articles in the BPM context is conducted. The retrieved literature is analyzed by means of scientometric methods and a developed reference framework.Findings - The steadily growing number of published articles in empirical BPM research shows an increase in interest in the research field. Research interests, applied methodologies, the underlying research paradigm and the level of maturity of empirical BPM research differ depending on regional aspects. BPM gains importance in the industry as well as in the public administration context.Research limitations implications - The findings are based on a sample of 355 articles and not on an exhaustive amount of available empirical research contributions. Nevertheless, significant analyses can be conducted. Future research could apply the developed reference framework for further literature reviews in order to be able to compare the findings and to measure progress.Originality value - The presented literature review gives an overview of trends in empirical BPM research. The developed and strictly applied reference framework supports a systematic analysis of contributions and can thus draw a significant picture of the state-of-the-art of the research field. To the best knowledge of the authors no such survey has currently been undertaken.
This paper proposes a multi‐stage approach consisting of deep learning‐based image classification, process trace clustering, and visual/statistical knowledge discovery of process data. The proposed ...decision augmentation solution aims to facilitate the production planners in estimating the process‐specific production parameters such as activity duration, idle time, or machine utilization. This study focuses on ‘one‐of‐a‐kind production’ (OKP). Planning in OKP is especially challenging due to the increasing individualization of customer requirements. Furthermore, the uniqueness of products adds complexity to data and information structuring. To tackle this issue, we first train deep convolutional neural networks (CNN) with image data of production parts obtained from computer‐aided design (CAD) systems to extract meaningful features. After cross‐validation, uncertainty, and robustness assessment of the adopted deep learning approach, we use the data representation from the penultimate layer as input for clustering production parts. The goodness of clustering results is evaluated using a series of internal clustering validation indices. Finally, process event log data provided by manufacturing execution systems (MES) is mapped to each production part, allowing us to conduct statistical and visual knowledge discovery of process parameters for each cluster. The relevance of our proposed approach has been validated by studying a real‐world use case in a small, medium‐sized enterprise (SME) operating in the fixture and jig manufacturing industry.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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