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Textual descriptions of processes are ubiquitous in organizations, so that documentation of the important processes can be accessible to anyone involved. Unfortunately, the value of this rich data source is hampered by the challenge of analyzing unstructured information. In this paper we propose a framework to overcome the current limitations on dealing with textual descriptions of processes. This framework considers extraction and analysis and connects to process mining via simulation. The framework is grounded in the notion of annotated textual descriptions of processes, which represents a middle-ground between formalization and accessibility, and which accounts for different modeling styles, ranging from purely imperative to purely declarative. The contributions of this paper are implemented in several tools, and case studies are highlighted. [ABSTRACT FROM AUTHOR]

Complex process models can hinder the comprehension of the underlying business processes. While several metrics have been suggested in the literature to evaluate the complexity of imperative process models, little is known about their declarative counterparts. In this paper, we address this gap through a suite of metrics that we propose to capture the complexity of declarative process models. Following this, we empirically investigate the impact of complexity, as measured by the suggested metrics, on users' cognitive load when comprehending declarative process models. Therein, we use a multi-modal approach including eye-tracking and electrodermal activity. The findings of the empirical study provide evidence about the cognitive load emerging as a result of increased model complexity. Overall, the outcome of this paper presents empirically validated metrics to evaluate the complexity of declarative process models. Implementing these metrics and incorporating them in intelligent modeling tools would help assessing the complexity of declarative process models before being deployed. Furthermore, our empirical approach can be adopted by researchers in upcoming empirical studies to provide a multi-perspective assessment of users' cognitive load when engaging with process models. • A suite of complexity metrics for declarative process models. • A validation using multi-modal measurements of cognitive load. • Empirical evidence showing that increased model complexity increases cognitive load. [ABSTRACT FROM AUTHOR]

Data visualizations are versatile tools for gaining cognitive access to large amounts of data and for making complex relationships in data understandable. This paper proposes a method for assessing data visualizations according to the purposes they fulfill in domain-specific data analysis settings. We introduce a framework that gets configured for a given analysis domain and allows to choose data visualizations in a methodically justified way, based on analysis questions that address different aspects of data to be analyzed. Based on the concepts addressed by the analysis questions, the framework provides systematic guidance for determining which data visualizations are able to serve which conceptual analysis interests. In a second step of the method, we propose to follow a data-driven approach and to experimentally compare alternative data visualizations for a particular analytical purpose. More specifically, we propose to use eye tracking to support justified decisions about which of the data visualizations selected with the help of the framework are most suitable for assessing the analysis domain in a cognitively efficient way. We demonstrate our approach of how to come from analytical purposes to data visualizations using the example domain of Process Modeling Behavior Analysis. The analyses are performed on the background of representative analysis questions from this domain. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Business process models abstract complex business processes by representing them as graphical models. Their layout, as determined by the modeler, may have an effect when these models are used. However, this effect is currently not fully understood. In order to systematically study this effect, a basic set of measurable key visual features is proposed, depicting the layout properties that are meaningful to the human user. The aim of this research is thus twofold: first, to empirically identify key visual features of business process models which are perceived as meaningful to the user and second, to show how such features can be quantified into computational metrics, which are applicable to business process models. We focus on one particular feature, consistency of flow direction, and show the challenges that arise when transforming it into a precise metric. We propose three different metrics addressing these challenges, each following a different view of flow consistency. We then report the results of an empirical evaluation, which indicates which metric is more effective in predicting the human perception of this feature. Moreover, two other automatic evaluations describing the performance and the computational capabilities of our metrics are reported as well. [ABSTRACT FROM AUTHOR]

Process mining is a family of techniques that aim at analyzing business process execution data recorded in event logs. Conformance checking is a branch of this discipline embracing approaches for verifying whether the behavior of a process, as recorded in a log, is in line with some expected behavior provided in the form of a process model. Recently, techniques for conformance checking based on declarative specifications have been developed. Such specifications are suitable to describe processes characterized by high variability. However, an open challenge in the context of conformance checking with declarative models is the capability of supporting multi-perspective specifications. This means that declarative models used for conformance checking should not only describe the process behavior from the control flow point of view, but also from other perspectives like data or time. In this paper, we close this gap by presenting an approach for conformance checking based on MP-Declare, a multi-perspective version of the declarative process modeling language Declare. The approach has been implemented in the process mining tool ProM and has been experimented using artificial and real-life event logs. [ABSTRACT FROM AUTHOR]

Online conformance checking comes with new challenges, especially in terms of time and space constraints. One fundamental challenge of explaining the conformance of a running case is in balancing between making sense at the process level as the case reaches completion and putting emphasis on the current information at the same time. In this paper, we propose an online conformance checking framework that tackles this problem by incorporating the step of estimating the "location" of the case within the scope of the modeled process before conformance computation. This means that conformance checking is broken down into two steps: orientation and conformance. The two steps are related: knowing "where" the case is with respect to the process allows a conformance explanation that is more accurate and coherent at the process level and such conformance information in turn allows better orientations. Based on Hidden Markov Models (HMM), the approach works by alternating between orienting the running case within the process and conformance computation. An implementation is available as a Python package and experimental results show that the approach yields results that correlate with prefix alignment costs under both conforming and non-conforming scenarios while maintaining constant time and space complexity per event. • An online conformance checking technique is proposed. • Proposed technique balances between making sense at the process level and putting emphasis on the current information. • Proposed technique maintains constant time and space complexity per event given the process model. • Experimental results on benchmark synthetic dataset shows a F1-score of 0.923 in the classification of whether if a case is conforming. • Experimental results on a real-life dataset identifies specific conformance issues. [ABSTRACT FROM AUTHOR]

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. • A novel conceptual framework for Hybrid Business Process Representations (HBPRs). • A clear-cut distinction between hybrid languages and hybrid process artifacts. • A systematic literature of 30 articles covering two distinct research lines. • A descriptive taxonomy classifying different types of HBPRs. • A research agenda delineating the directions for future work. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the ability to accurately predict future features of running business process instances would be a very helpful aid when managing processes, especially under service level agreement constraints. However, making such accurate forecasts is not easy: many factors may influence the predicted features. Many approaches have been proposed to cope with this problem but, generally, they assume that the underlying process is stationary. However, in real cases this assumption is not always true. In this work we present new methods for predicting the remaining time of running cases. In particular we propose a method, assuming process stationarity, which achieves state-of-the-art performances and two other methods which are able to make predictions even with non-stationary processes. We also describe an approach able to predict the full sequence of activities that a running case is going to take. All these methods are extensively evaluated on different real case studies. [ABSTRACT FROM AUTHOR]