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get_appChristian Janisch, Agnes Koschmider, Massimo Mecella, Barbara Weber, Andrea Burattin, Claudio Di Ciccio, Giancarlo Fortino, Avigdor Gal, Udo Kannengiesser, Francesco Leotta, Felix Mannhardt, Andrea Marrella, Jan Mendling, Anderas Oberweis, Manfred Reichert, Stefanie Rinderle-Ma, Estefania Serral, WenZhan Song, Jianwen Su, Victoria Torres, Matthias Weidlich, Mathias Weske, Liang Zhang
Journal paper
The Internet of Things (IoT) refers to a network of connected devices collecting and exchanging data over the Internet. These things can be artificial or natural and interact as autonomous agents forming a complex system. In turn, Business Process Management (BPM) was established to analyze, discover, design, implement, execute, monitor and evolve collaborative business processes within and across organizations. While the IoT and BPM have been regarded as separate topics in research and practice, we strongly believe that the management of IoT applications will strongly benefit from BPM concepts, methods and technologies on the one hand; on the other one, the IoT poses challenges that will require enhancements and extensions of the current state-of-the-art in the BPM field. In this paper, we question to what extent these two paradigms can be combined and we discuss the emerging challenges and intersections from a research and practitioner’s point of view in terms of complex software systems development.

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This demo paper presents an infrastructure to enable realtime monitoring of process events (i.e., telemetry). The infrastructure relies on the MQTT protocol which ensures minimum logging overhead. The paper presents a Java library for producing (i.e., logging) and consuming events, built on top of HiveMQ. Additionally, a prototype dashboard to display basic statistics is reported and described.

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The Internet of Things (IoT) enables software-based access to vast amounts of data streams from sensors measuring physical and virtual properties of smart devices and their surroundings. While sophisticated means for the control and data analysis of single IoT devices exist, a more process-oriented view of IoT systems is often missing. Such a lack of process awareness hinders the development of process-based systems on top of IoT environments and the application of process mining techniques for process analysis and optimization in IoT. We propose a framework for the stepwise correlation and composition of raw IoT sensor streams with events and activities on a process level based on Complex Event Processing (CEP). From this correlation we derive refined process event logs–possibly with ambiguities–that can be used for process analysis at runtime (i. e., online). We discuss the framework using examples from a smart factory.

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Journal paper
Process design artifacts have been increasingly used to guide the modeling of business processes. To support users in designing and understanding process models, different process artifacts have been combined in several ways leading to the emergence of the so-called “hybrid process artifacts”. While many hybrid artifacts have been proposed in the literature, little is known about how they can actually support users in practice. To address this gap, this work investigates the way users engage with hybrid process artifacts during comprehension tasks. In particular, we focus on a hybrid representation of DCR Graphs (DCR-HR) combining a process model, textual annotations and an interactive simulation. Following a qualitative approach, we conduct a multi-granular analysis exploiting process mining, eye-tracking techniques, and verbal data analysis to scrutinize the reading patterns and the strategies adopted by users when being confronted with DCR-HR. The findings of the coarse-grained analysis provide important insights about the behavior of domain experts and IT specialists and show how user’s background and task type change the use of hybrid process artifacts. As for the fine-grained analysis, user’s behavior was classified into goal-directed and exploratory and different strategies of using the interactive simulation were identified. In addition, a progressive switch from an exploratory behavior to a goal-directed behavior was observed. These insights pave the way for an improved development of hybrid process artifacts and delineate several directions for future work.

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Process modeling plays a central role in the development of today’s process-aware information systems both on the management level (e.g., providing input for requirements elicitation and fostering communication) and on the enactment level (providing a blue-print for process execution and enabling simulation). The literature comprises a variety of process modeling approaches proposing different modeling languages (i.e., imperative and declarative languages) and different types of process artifact support (i.e., process models, textual process descriptions, and guided simulations). However, the use of an individual modeling language or a single type of process artifact is usually not enough to provide a clear and concise understanding of the process. To overcome this limitation, a set of so-called “hybrid” approaches combining languages and artifacts have been proposed, but no common grounds have been set to define and categorize them. This work aims at providing a fundamental understanding of these hybrid approaches by defining a unified terminology, providing a conceptual framework and proposing an overarching overview to identify and analyze them. Since no common terminology has been used in the literature, we combined existing concepts and ontologies to define a “Hybrid Business Process Representation” (HBPR). Afterwards, we conducted a Systematic Literature Review (SLR) to identify and investigate the characteristics of HBPRs combining imperative and declarative languages or artifacts. The SLR resulted in 30 articles which were analyzed. The results indicate the presence of two distinct research lines and show common motivations driving the emergence of HBPRs, a limited maturity of existing approaches, and diverse application domains. Moreover, the results are synthesized into a taxonomy classifying different types of representations. Finally, the outcome of the study is used to provide a research agenda delineating the directions for future work.

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Conference or Workshop Item
Process models provide a blueprint for process execution and an indispensable tool for process management. Bearing in mind their trending use for requirement elicitation, communication and improvement of business processes, the need for understandable process models becomes a must. In this paper, we propose a research model to investigate the impact of modularization on the understandability of declarative process models. We design a controlled experiment supported by eye-tracking, electroencephalography (EEG) and galvanic skin response (GSR) to appraise the understandability of hierarchical process models through measures such as comprehension accuracy, response time, attention, cognitive load and cognitive integration.

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Data visualizations are versatile tools for gaining cognitive access to large amounts of data and for making complex relation-ships in data understandable. This paper proposes a method for assessing data visualizations according to the purposes theyfulfill in domain-specific data analysis settings. We introduce a framework that gets configured for a given analysis domainand allows to choose data visualizations in a methodically justified way, based on analysis questions that address differentaspects of data to be analyzed. Based on the concepts addressed by the analysis questions, the framework provides systematicguidance for determining which data visualizations are able to serve which conceptual analysis interests. In a second step ofthe method, we propose to follow a data-driven approach and to experimentally compare alternative data visualizations fora particular analytical purpose. More specifically, we propose to use eye tracking to support justified decisions about whichof the data visualizations selected with the help of the framework are most suitable for assessing the analysis domain in acognitively efficient way. We demonstrate our approach of how to come from analytical purposes to data visualizations usingthe example domain of Process Modeling Behavior Analysis. The analyses are performed on the background of representativeanalysis questions from this domain.

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get_appThomas Hildebrandt, Amine Abbad Andaloussi, Lars Rune Christensen, Søren Debois, Nicklas Pape Healy, Hugo A. López, Morten Marquard, Naja L. Holten Møller, Anette Chelina Møller Petersen, Tijs Slaats, Barbara Weber
Conference or Workshop Item
We report on a new approach to co-creating adaptive case management systems jointly with end-users, developed in the context of the Effective co-created and compliant adaptive case Management Systems for Knowledge Workers (EcoKnow.org) research project. The approach is based on knowledge from prior ethnographic field studies and research in the declarative Dynamic Condition Response (DCR) technology for model-driven design of case management systems. The approach was tested in an operational environment jointly with the danish municipality of Syddjurs by conducting a service-design project and implementing an open source case manager tool and a new highlighter tool for mapping between textual specifications and the DCR notation. The design method and technologies were evaluated by understandability studies with endusers. The study showed that the development could be done in just 6 months, and that the new highlighter tool in combination with the traditional design and simulation tools, supports domain experts formalise and provide traceability between their interpretations of textual specifications and the formal models.

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Understanding how developers interact with different software artifacts when performing comprehension tasks has a potential to improve developers' productivity. In this paper, we propose a method to analyze eye-tracking data using process mining to find distinct reading patterns of how developers interacted with the different artifacts. To validate our approach, we conducted an exploratory study using eye-tracking involving 11 participants. We applied our method to investigate how developers interact with different artifacts during domain and code understanding tasks. To contextualize the reading patterns and to better understand the perceived benefits and challenges participants associated with the different artifacts and their choice of reading patterns, we complemented the eye-tracking data with the data obtained from think aloud. The study used behavior-driven development, a development practice that is increasingly used in Agile software development contexts, as a setting. The study shows that our method can be used to explore developers' behavior at an aggregated level and identify behavioral patterns at varying levels of granularity.

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get_appJosep Sanchez-Ferreres, Luis Delicado, Amine Abbad Andaloussi, Andrea Burattin, Guillermo Calderon-Ruiz, Barbara Weber, Josep Carmona, Lluís Padró
Journal paper
The creation of a process model is primarily a formalization task that faces the challenge of constructing a syntactically correct entity which accurately reflects the semantics of reality, and is understandable to the model reader. This paper proposes a framework called Model Judge, focused towards the two main actors in the process of learning process model creation: novice modelers and instructors. For modelers, the platform enables the automatic validation of the process models created from a textual description, providing explanations about quality issues in the model. Model Judge can provide diagnostics regarding model structure, writing style, and semantics by aligning annotated textual descriptions to models. For instructors, the platform facilitates the creation of modeling exercises by providing an editor to annotate the main parts of a textual description, that is empowered with natural language processing (NLP) capabilities so that the annotation effort is minimized. So far around 300 students, in process modeling courses of five different universities around the world have used the platform. The feedback gathered from some of these courses shows good potential in helping students to improve their learning experience, which might, in turn, impact process model quality and understandability. Moreover, our results show that instructors can benefit from getting insights into the evolution of modeling processes including arising quality issues of single students, but also discovering tendencies in groups of students. Although the framework has been applied to process model creation, it could be extrapolated to other contexts where the creation of models based on a textual description plays an important role.

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Imperative process models have become immensely popular. However, their use is usually limited to rigid and repetitive processes. Considering the inherent flexibility in most processes in the real-world and the increased need for managing knowledge-intensive processes, the adoption of declarative languages becomes more pertinent than ever. While the quality of imperative models has been extensively investigated in the literature, little is known about the dimensions affecting the quality of declarative models. This work takes an advanced stride to investigate the quality of declarative models. Following the theory of Personal Construct Psychology (PCT), our research introduces a novel method within the Business Process Management (BPM) field to explore quality in the eyes of expert modelers. The findings of this work summarize the dimensions defining the quality of declarative models. The outcome shows the potential of PCT as a basis to discover quality dimensions and advances our understanding of quality in declarative process models.

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Process design artifacts (e.g., process models, textual process descriptions and simulations) are increasingly used to provide input for requirements elicitation and to facilitate the design of business processes. To support the understandability of process models and make them accessible for end-users with different backgrounds, several hybrid representations combining different design artifacts have been proposed in the literature. This paper investigates the understandability of DCR-HR, a new hybrid process design artifact based on DCR graphs. Using eye-tracking and think-aloud techniques, this paper explores the benefits and challenges associated with the use of different design artifacts and investigates the way end-users engage with them. The results motivate the use of DCR-HR and provide insights about the support it provides to end-users with different backgrounds.

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Conceptual models play an important role in many organizations. They serve as tools for communication and documentation, are often a central part in process improvement initiatives, and are key to the development and evolution of information systems. Existing modeling tools typically support end users in a rather generic and non-personalized manner. However, users not only differ in their modeling expertise and the challenges they encounter while modeling, but also in their preferences. Therefore, they would benefit from a new generation of modeling environments that are highly personalized and adapt themselves to users’ needs. This keynote presents a vision of such modeling environments with a focus on process modeling. It highlights this potential with several examples from our research and touches upon challenges that come with the development of next generation modeling environments.

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The creation of a process model is a process consisting of five distinct phases, i.e., problem understanding, method finding, modeling, reconciliation, and validation. To enable a fine-grained analysis of process model creation based on phases or the development of phase-specific modeling support, an automatic approach to detect phases is needed. While approaches exist to automatically detect modeling and reconciliation phases based on user interactions, the detection of phases without user interactions (i.e., problem understanding, method finding, and validation) is still a problem. Exploiting a combination of user interactions and eye tracking data, this paper presents a two-step approach that is able to automatically detect the sequence of phases a modeler is engaged in during model creation. The evaluation of our approach shows promising results both in terms of quality as well as computation time demonstrating its feasibility."

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get_appConstantina Ioannou, Ekkart Kindler, Per Bækgaard, Shazia Saqid, Barbara Weber
Conference or Workshop Item
Despite their wide adoption for conducting experiments in numerous domains, neurophysiological measurements often are time consuming and challenging to interpret because of the inherent complexity of deriving measures from raw signal data and mapping measures to theoretical constructs. While significantefforts have been undertaken to support neurophysiological experiments, the existing software solutions are non-trivial to use because often these solutions aredomain specific or their analysis processes are opaque to the researcher. This paper proposes an architecture for a software platform that supports experimentswith multi-modal neurophysiological tools through extensible, transparent and repeatable data analysis and enables the comparison between data analysis processes to develop more robust measures. The identified requirements and the proposed architecture are intended to form a basis of a software platform capable of conducting experiments using neurophysiological tools applicable to various domains.

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