Manfred Reichert

Stefan Gaugel, Manfred Reichert

The fourth industrial revolution creates ubiquitous sensor data in production plants. To generate maximum value out of these data, reliable and precise time series-based machine learning methods like temporal neural networks are needed. This paper proposes a novel sequence-to-sequence deep learning architecture for time series segmentation called PrecTime which tries to combine the concepts and advantages of sliding window and dense labeling approaches. The general-purpose architecture is evaluated on a real-world industry dataset containing the End-of-Line testing sensor data of hydraulic pumps. We are able to show that PrecTime outperforms five implemented state-of-the-art baseline networks based on multiple metrics. The achieved segmentation accuracy of around 96% shows that PrecTime can achieve results close to human intelligence in operational state segmentation within a testing cycle.

Juergen Mangler, Ronny Seiger, Janik-Vasily Benzin et al.

The IoT and Business Process Management (BPM) communities co-exist in many shared application domains, such as manufacturing and healthcare. The IoT community has a strong focus on hardware, connectivity and data; the BPM community focuses mainly on finding, controlling, and enhancing the structured interactions among the IoT devices in processes. While the field of Process Mining deals with the extraction of process models and process analytics from process event logs, the data produced by IoT sensors often is at a lower granularity than these process-level events. The fundamental questions about extracting and abstracting process-related data from streams of IoT sensor values are: (1) Which sensor values can be clustered together as part of process events?, (2) Which sensor values signify the start and end of such events?, (3) Which sensor values are related but not essential? This work proposes a framework to semi-automatically perform a set of structured steps to convert low-level IoT sensor data into higher-level process events that are suitable for process mining. The framework is meant to provide a generic sequence of abstract steps to guide the event extraction, abstraction, and correlation, with variation points for plugging in specific analysis techniques and algorithms for each step. To assess the completeness of the framework, we present a set of challenges, how they can be tackled through the framework, and an example on how to instantiate the framework in a real-world demonstration from the field of smart manufacturing. Based on this framework, future research can be conducted in a structured manner through refining and improving individual steps.

Ghada Elkhawaga, Mervat Abuelkheir, Manfred Reichert

Predictive Process Monitoring (PPM) has been integrated into process mining tools as a value-adding task. PPM provides useful predictions on the further execution of the running business processes. To this end, machine learning-based techniques are widely employed in the context of PPM. In order to gain stakeholders trust and advocacy of PPM predictions, eXplainable Artificial Intelligence (XAI) methods are employed in order to compensate for the lack of transparency of most efficient predictive models. Even when employed under the same settings regarding data, preprocessing techniques, and ML models, explanations generated by multiple XAI methods differ profoundly. A comparison is missing to distinguish XAI characteristics or underlying conditions that are deterministic to an explanation. To address this gap, we provide a framework to enable studying the effect of different PPM-related settings and ML model-related choices on characteristics and expressiveness of resulting explanations. In addition, we compare how different explainability methods characteristics can shape resulting explanations and enable reflecting underlying model reasoning process

Ghada Elkhawaga, Mervat Abuelkheir, Manfred Reichert

Predictive business process monitoring (PPM) has been around for several years as a use case of process mining. PPM enables foreseeing the future of a business process through predicting relevant information about how a running process instance might end, related performance indicators, and other predictable aspects. A big share of PPM approaches adopts a Machine Learning (ML) technique to address a prediction task, especially non-process-aware PPM approaches. Consequently, PPM inherits the challenges faced by ML approaches. One of these challenges concerns the need to gain user trust in the predictions generated. The field of explainable artificial intelligence (XAI) addresses this issue. However, the choices made, and the techniques employed in a PPM task, in addition to ML model characteristics, influence resulting explanations. A comparison of the influence of different settings on the generated explanations is missing. To address this gap, we investigate the effect of different PPM settings on resulting data fed into an ML model and consequently to a XAI method. We study how differences in resulting explanations may indicate several issues in underlying data. We construct a framework for our experiments including different settings at each stage of PPM with XAI integrated as a fundamental part. Our experiments reveal several inconsistencies, as well as agreements, between data characteristics (and hence expectations about these data), important data used by the ML model as a result of querying it, and explanations of predictions of the investigated ML model.

Michael Winter, Heiko Neumann, Rüdiger Pryss et al.

Process models depict crucial artifacts for organizations regarding documentation, communication, and collaboration. The proper comprehension of such models is essential for an effective application. An important aspect in process model literacy constitutes the question how the information presented in process models is extracted and processed by the human visual system? For such visuospatial tasks, the visual system deploys a set of elemental operations, from whose compositions different visual routines are produced. This paper provides insights from an exploratory eye tracking study, in which visual routines during process model comprehension were contemplated. More specifically, n = 29 participants were asked to comprehend n = 18 process models expressed in the Business Process Model and Notation 2.0 reflecting diverse mappings (i.e., straight, upward, downward) and complexity levels. The performance measures indicated that even less complex process models pose a challenge regarding their comprehension. The upward mapping confronted participants' attention with more challenges, whereas the downward mapping was comprehended more effectively. Based on recorded eye movements, three gaze patterns applied during model comprehension were derived. Thereupon, we defined a general model which identifies visual routines and corresponding elemental operations during process model comprehension. Finally, implications for practice as well as research and directions for future work are discussed in this paper.

Kevin Andrews, Manfred Reichert

Business process management systems from various vendors are used by companies around the globe. Most of these systems allow for the full or partial automation of business processes by ensuring that tasks and data are presented to the right person at the right time during process execution. However, almost all established BPMS employ the activity-centric process support paradigm, in which the various forms, i.e., the main way for users to input data into the process, have to be created by hand. Furthermore, traditional activity-centric process management systems are limited in their flexibility as all possible execution variants have to be taken into account by the process modeler. Therefore, large amounts of research have gone into developing alternative process support paradigms, with a large focus on enabling more flexibly executable processes. This article takes one of these paradigms, object-aware process management, and presents the concepts we developed while researching the possibility of bringing the power and flexibility of non-activity-centric process support paradigms to the people that matter: the end-users working with the processes. The contribution of this article are the concepts, ideas, and lessons learned during the development and evaluation of the PHILharmonicFlows runtime user interface, which allows for the generation of an entire user interface, complete with navigation and forms, based on an object-aware process model. This novel approach allows for the generation of entire information systems, complete with data storage, process logic, and now fully functional user interfaces in a fully generic fashion from data-centric object-aware process models.

Michael Winter, Rüdiger Pryss, Thomas Probst et al.

The comprehension of business process models is crucial for enterprises. Prior research has shown that children as well as adolescents perceive and interpret graphical representations in a different manner compared to grown-ups. To evaluate this, observations in the context of business process models are presented in this paper obtained from a study on visual literacy in cultural education. We demonstrate that adolescents without expertise in process model comprehension are able to correctly interpret business process models expressed in terms of BPMN 2.0. In a comprehensive study, n = 205 learners (i.e., pupils at the age of 15) needed to answer questions related to process models they were confronted with, reflecting different levels of complexity. In addition, process models were created with varying styles of element labels. Study results indicate that an abstract description (i.e., using only alphabetic letters) of process models is understood more easily compared to concrete or pseudo} descriptions. As benchmark, results are compared with the ones of modeling experts (n = 40). Amongst others, study findings suggest using abstract descriptions in order to introduce novices to process modeling notations. With the obtained insights, we highlight that process models can be properly comprehended by novices.

Michael Winter, Rüdiger Pryss, Matthias Fink et al.

Process models constitute crucial artifacts in modern information systems and, hence, the proper comprehension of these models is of utmost importance in the utilization of such systems. Generally, process models are considered from two different perspectives: process modelers and readers. Both perspectives share similarities and differences in the comprehension of process models (e.g., diverse experiences when working with process models). The literature proposed many rules and guidelines to ensure a proper comprehension of process models for both perspectives. As a novel contribution in this context, this paper introduces the Process Model Comprehension Framework (PMCF) as a first step towards the measurement and quantification of the perspectives of process modelers and readers as well as the interaction of both regarding the comprehension of process models. Therefore, the PMCF describes an Evaluation Theory Tree based on the Communication Theory as well as the Conceptual Modeling Quality Framework and considers a total of 96 quality metrics in order to quantify process model comprehension. Furthermore, the PMCF was evaluated in a survey with 131 participants and has been implemented as well as applied successfully in a practical case study including 33 participants. To conclude, the PMCF allows for the identification of pitfalls and provides related information about how to assist process modelers as well as readers in order to foster and enable a proper comprehension of process models.

Florian Wirthmüller, Jochen Hipp, Christian Reichenbächer et al.

The prediction of surrounding traffic participants behavior is a crucial and challenging task for driver assistance and autonomous driving systems. Today's approaches mainly focus on modeling dynamic aspects of the traffic situation and try to predict traffic participants behavior based on this. In this article we take a first step towards extending this common practice by calculating location-specific a-priori lane change probabilities. The idea behind this is straight forward: The driving behavior of humans may vary in exactly the same traffic situation depending on the respective location. E.g. drivers may ask themselves: Should I pass the truck in front of me immediately or should I wait until reaching the less curvy part of my route lying only a few kilometers ahead? Although, such information is far away from allowing behavior prediction on its own, it is obvious that today's approaches will greatly benefit when incorporating such location-specific a-priori probabilities into their predictions. For example, our investigations show that highway interchanges tend to enhance driver's motivation to perform lane changes, whereas curves seem to have lane change-dampening effects. Nevertheless, the investigation of all considered local conditions shows that superposition of various effects can lead to unexpected probabilities at some locations. We thus suggest dynamically constructing and maintaining a lane change probability map based on customer fleet data in order to support onboard prediction systems with additional information. For deriving reliable lane change probabilities a broad customer fleet is the key to success.

Florian Wirthmüller, Marvin Klimke, Julian Schlechtriemen et al.

To plan safe and comfortable trajectories for automated vehicles on highways, accurate predictions of traffic situations are needed. So far, a lot of research effort has been spent on detecting lane change maneuvers rather than on estimating the point in time a lane change actually happens. In practice, however, this temporal information might be even more useful. This paper deals with the development of a system that accurately predicts the time to the next lane change of surrounding vehicles on highways using long short-term memory-based recurrent neural networks. An extensive evaluation based on a large real-world data set shows that our approach is able to make reliable predictions, even in the most challenging situations, with a root mean squared error around 0.7 seconds. Already 3.5 seconds prior to lane changes the predictions become highly accurate, showing a median error of less than 0.25 seconds. In summary, this article forms a fundamental step towards downstreamed highly accurate position predictions.

Judith Wewerka, Manfred Reichert

Robotic Process Automation (RPA) is the automation of rule-based routine processes to increase efficiency and to reduce costs. Due to the utmost importance of process automation in industry, RPA attracts increasing attention in the scientific field as well. This paper presents the state-of-the-art in the RPA field by means of a Systematic Literature Review (SLR). In this SLR, 63 publications are identified, categorised, and analysed along well-defined research questions. From the SLR findings, moreover, a framework for systematically analysing, assessing, and comparing existing as well as upcoming RPA works is derived. The discovered thematic clusters advise further investigations in order to develop an even more detailed structural research approach for RPA.

Florian Wirthmüller, Marvin Klimke, Julian Schlechtriemen et al.

Already today, driver assistance systems help to make daily traffic more comfortable and safer. However, there are still situations that are quite rare but are hard to handle at the same time. In order to cope with these situations and to bridge the gap towards fully automated driving, it becomes necessary to not only collect enormous amounts of data but rather the right ones. This data can be used to develop and validate the systems through machine learning and simulation pipelines. Along this line this paper presents a fleet learning-based architecture that enables continuous improvements of systems predicting the movement of surrounding traffic participants. Moreover, the presented architecture is applied to a testing vehicle in order to prove the fundamental feasibility of the system. Finally, it is shown that the system collects meaningful data which are helpful to improve the underlying prediction systems.

Ghada Elkhawaga, Mervat Abuelkheir, Sherif I. Barakat et al.

Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (SLR) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts.

Florian Wirthmüller, Julian Schlechtriemen, Jochen Hipp et al.

Predicting the behavior of surrounding traffic participants is crucial for advanced driver assistance systems and autonomous driving. Most researchers however do not consider contextual knowledge when predicting vehicle motion. Extending former studies, we investigate how predictions are affected by external conditions. To do so, we categorize different kinds of contextual information and provide a carefully chosen definition as well as examples for external conditions. More precisely, we investigate how a state-of-the-art approach for lateral motion prediction is influenced by one selected external condition, namely the traffic density. Our investigations demonstrate that this kind of information is highly relevant in order to improve the performance of prediction algorithms. Therefore, this study constitutes the first step towards the integration of such information into automated vehicles. Moreover, our motion prediction approach is evaluated based on the public highD data set showing a maneuver prediction performance with areas under the ROC curve above 97% and a median lateral prediction error of only 0.18m on a prediction horizon of 5s.

Burkhard Hoppenstedt, Manfred Reichert, Ghada El-Khawaga et al.

In the context of Industry 4.0, the knowledge extraction from sensor information plays an important role. Often, information gathered from sensor values reveals meaningful insights for production levels, such as anomalies or machine states. In our use case, we identify production phases through the inspection of sensor values with the help of convolutional neural networks. The data set stems from a tempering furnace used for metal heat treating. Our supervised learning approach unveils a promising accuracy for the chosen neural network that was used for the detection of production phases. We consider solutions like shown in this work as salient pillars in the field of predictive maintenance.

Florian Wirthmüller, Julian Schlechtriemen, Jochen Hipp et al.

By observing their environment as well as other traffic participants, humans are enabled to drive road vehicles safely. Vehicle passengers, however, perceive a notable difference between non-experienced and experienced drivers. In particular, they may get the impression that the latter ones anticipate what will happen in the next few moments and consider these foresights in their driving behavior. To make the driving style of automated vehicles comparable to the one of human drivers with respect to comfort and perceived safety, the aforementioned anticipation skills need to become a built-in feature of self-driving vehicles. This article provides a systematic comparison of methods and strategies to generate this intention for self-driving cars using machine learning techniques. To implement and test these algorithms we use a large data set collected over more than 30000 km of highway driving and containing approximately 40000 real-world driving situations. We further show that it is possible to classify driving maneuvers upcoming within the next 5 s with an Area Under the ROC Curve (AUC) above 0.92 for all defined maneuver classes. This enables us to predict the lateral position with a prediction horizon of 5 s with a median lateral error of less than 0.21 m.

Jan Mendling, Ingo Weber, Wil van der Aalst et al.

Blockchain technology promises a sizable potential for executing inter-organizational business processes without requiring a central party serving as a single point of trust (and failure). This paper analyzes its impact on business process management (BPM). We structure the discussion using two BPM frameworks, namely the six BPM core capabilities and the BPM lifecycle. This paper provides research directions for investigating the application of blockchain technology to BPM.

Barbara Weber, Jakob Pinggera, Victoria Torres et al.

Process model quality has been an area of considerable research efforts. In this context, the correctness-by-construction principle of change patterns provides promising perspectives. However, using change patterns for model creation imposes a more structured way of modeling. While the process of process modeling (PPM) based on change primitives has been investigated, little is known about this process based on change patterns. To obtain a better understanding of the PPM when using change patterns, the arising challenges, and the subjective perceptions of process designers, we conduct an exploratory study. The results indicate that process designers face little problems as long as control-flow is simple, but have considerable problems with the usage of change patterns when complex, nested models have to be created. Finally, we outline how effective tool support for change patterns should be realized.

Barbara Weber, Sarah Zeitelhofer, Jakob Pinggera et al.

Process model quality has been an area of considerable research efforts. In this context, correctness-by-construction as enabled by change patterns provides promising perspectives. While the process of process modeling (PPM) based on change primitives has been thoroughly investigated, only little is known about the PPM based on change patterns. In particular, it is unclear what set of change patterns should be provided and how the available change pattern set impacts the PPM. To obtain a better understanding of the latter as well as the (subjective) perceptions of process modelers, the arising challenges, and the pros and cons of different change pattern sets we conduct a controlled experiment. Our results indicate that process modelers face similar challenges irrespective of the used change pattern set (core pattern set versus extended pattern set, which adds two advanced change patterns to the core patterns set). An extended change pattern set, however, is perceived as more difficult to use, yielding a higher mental effort. Moreover, our results indicate that more advanced patterns were only used to a limited extent and frequently applied incorrectly, thus, lowering the potential benefits of an extended pattern set.

Barbara Weber, Jakob Pinggera, Victoria Torres et al.

Process model quality has been an area of considerable research efforts. In this context, the correctness-by-construction principle of change patterns offers a promising perspective. However, using change patterns for model creation imposes a more structured way of modeling. While the process of process modeling (PPM) based on change primitives has been investigated, little is known about this process based on change patterns and factors that impact the cognitive complexity of pattern usage. Insights from the field of cognitive psychology as well as observations from a pilot study suggest that the nesting depth of the model to be created has a significant impact on cognitive complexity. This paper proposes a research design to test the impact of nesting depth on the cognitive complexity of change pattern usage in an experiment.

Cornelia Haisjackl, Stefan Zugal, Pnina Soffer et al.

Declarative approaches to process modeling are regarded as well suited for highly volatile environments as they provide a high degree of flexibility. However, problems in understanding and maintaining declarative business process models impede often their usage. In particular, how declarative models are understood has not been investigated yet. This paper takes a first step toward addressing this question and reports on an exploratory study investigating how analysts make sense of declarative process models. We have handed out real-world declarative process models to subjects and asked them to describe the illustrated process. Our qualitative analysis shows that subjects tried to describe the processes in a sequential way although the models represent circumstantial information, namely, conditions that produce an outcome, rather than a sequence of activities. Finally, we observed difficulties with single building blocks and combinations of relations between activities.