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With the onset of the Internet of Things (IoT) everyday objects suddenly become data sources equipped with sensors measuring the object’s properties and surroundings. However, the lack of process-awareness in IoT environments (e.g., smart factories) prevents the adoption of more sophisticated process analysis and optimization. One hurdle is the differing abstraction level of low-level sensor data and process-level activities. We propose a method to identify activities step-by-step from raw IoT data using visualizations. By relying on minimal process knowledge, we discover process activities from sensor events. These activities are represented by specific sequences of sensor events–Activity Signatures–that serve as a basis for finding similar activities. We demonstrate the method’s validity with a proof of concept in a smart factory.

The developments of new concepts for an increased digitization of manufacturing industries in the context of Industry 4.0 have brought about novel system architectures and frameworks for smart production systems. These range from generic frameworks for Industry 4.0 to domain-specific architectures for Industrial Internet of Things (IIoT). While most of the approaches include a service-based architecture for selective integration with enterprise systems, a close two-way integration of the production control systems and IIoT sensors and actuators with Process-Aware Information Systems (PAIS) on the management level for automation and mining of production processes is rarely discussed. This fusion of Business Process Management (BPM) with IIoT can be mutually beneficial for both research areas, but is still in its infancy. We propose a systems architecture for IIoT that shows how to integrate the low-level hardware components–sensors and actuators–of a smart factory with BPM systems. We discuss the software components and their interactions to address challenges of device encapsulation, integration of sensor events, and interaction with existing BPM systems. This integration is demonstrated within several use cases regarding process modeling, automation and mining for a smart factory model, showing benefits of using BPM technologies to analyze, control, and adapt discrete production processes in IIoT.

Online appendix for the results.

The comprehension of source code is a task inherent to many software development activities. Code change, code review and debugging are examples of these activities that depend heavily on developers' understanding of the source code. This ability is threatened when developers' cognitive load approaches the limits of their working memory, which in turn affects their understanding and makes them more prone to errors. Measures capturing humans' behavior and changes in their physiological state have been proposed in a number of studies to investigate developers' cognitive load. However, the majority of the existing approaches operate at a coarse-grained task level estimating the difficulty of the source code as a whole. Hence, they cannot be used to pinpoint the mentally demanding parts of it. We address this limitation in this paper through a non-intrusive approach based on eye-tracking. We collect users' behavioral and physiological features while they are engaging with source code and train a set of machine learning models to estimate the mentally demanding parts of code. The evaluation of our models returns F1, recall, accuracy and precision scores up to 85.65%, 84.25%, 86.24% and 88.61%, respectively, when estimating the mental demanding fragments of code. Our approach enables a fine-grained analysis of cognitive load and allows identifying the parts challenging the comprehension of source code. Such an approach provides the means to test new hypotheses addressing the characteristics of specific parts within the source code and paves the road for novel techniques for code review and adaptive e-learning.

Process mining analysts need to work with event data to discover (business) processes, interpret results and report meaningful conclusions. Although process mining tools are constantly enhanced and advanced techniques are developed to enrich the functional scope in the field, little is known about the individual needs of analysts and the issues they face while conducting process mining projects. This paper aims to close this gap by uncovering perceived challenges occurring in practice. Based on an interview study with 41 participants, we identify and describe 23 challenges, spanning different project phases and directly affecting the work of process mining analysts. We discuss whether methods and techniques exist that can help to overcome these challenges and where further research is needed to devise new solutions and integrate existing ones better into process mining practice.

The Internet of Things (IoT) refers to a network of connected devices that collects and exchanges data through the Internet. These things can be artificial or natural and interact as autonomous agents that form 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 in practice, we strongly believe that, on the one hand, the management of IoT applications will greatly benefit from BPM concepts, methods, and technologies. On the other hand, the IoT poses challenges that will require enhancements and extensions of the current state of the art in the BPM field. In this article, we question the extent to which these two paradigms can be combined, and we discuss emerging challenges and intersections from a research and practitioner's point of view in terms of complex software systems development.

The creation of a process model faces the challenge of constructing a syntactically correct entity which accurately reflects the semantics of the reality, and is understandable. This paper proposes a framework called ModelJudge , focused towards the two main actors in the process of learning process model creation: novice modellers and instructors. For modellers, the platform enables the automatic validation of the process models created from the textual description, providing explanations about quality issues in the model. ModelJudge can provide diagnostics regarding model structure, writing style, and seman- tics by aligning annotated textual descriptions to models. For instructors, the platform facilitates the creation of modelling 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 modelling 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 discover 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. This work has been partially supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R.

Collaborative brainstorming is often followed by a convergence activity where teams extract the most promising ideas on a useful level of detail from the brainstorming results. Contrary to the wealth of research on electronic brainstorming, there is a dearth of research on convergence. We used experimental methods for an in-depth exploration of two facilitation-based interventions in a convergence activity: attention guidance (focusing participants on procedures to execute a convergence task) and discussion encouragement (engaging participants in conversations to combine knowledge on ideas). Our findings show that both attention guidance and discussion encouragement are correlated with higher convergence quality. We argue that attention guidance’s contribution is in its support of coordination, information processing, and goal specification. Similar, we argue that discussion encouragement’s contribution is in its stimulation of idea clarification and idea combination. Contrary to past research, our findings further show that satisfaction was higher after convergence than after brainstorming.

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.

Recent eye tracking research in the field of software engineering has proposed novel visualizations linking developer’s gazes with the source code artifacts to better understand how developers comprehend source code artifacts potentially consisting of several different files. In addition, it is well established that cognitive processes can be monitored by recording the change in pupil dilation. Recent pupillometry studies in the software engineering field have shown that pupil dilation can be used either as an indicator of cognitive load or task difficulty. We envision to create a tool for visualizing pupil dilation linked to source code artifacts that can help to better understand the cognitive processes of a developer during code comprehension tasks in terms of cognitive load. In this paper, we describe a feasibility study we conducted to enable a more fine-grained analysis of pupil dilation and we demonstrate some preliminary results.

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.

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.

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.

Process mining aims at exploring the data produced by executable business processes to mine the underlying control-flow and data-flow. Most of the process mining algorithms assume the existence of an event log with a certain maturity level. Unfortunately, the logs provided by process unaware information systems often do not comply with the required maturity level, since they lack the notion of process instance, also referred in process mining as “case id”. Without a proper identification of the case id attribute in log files, the outcome of process mining algorithms is unpredictable. This paper proposes a new approach that aims to overcome this challenge by automatically inferring the case id attribute from log files. The approach has been implemented as a ProM plugin and evaluated with several real-world event logs. The results demonstrate a high accuracy in inferring the case id attribute.

Process-aware information systems (PAISs) are increasingly used to provide flexible support for business processes. The support given through a PAIS is greatly enhanced when it is able to provide accurate time predictions which is typically a very challenging task. Predictions should be (1) multi-dimensional and (2) not based on a single process instance. Furthermore, the prediction system should be able to (3) adapt to changing circumstances and (4) deal with multi-perspective declarative languages (e.g., models which consider time, resource, data and control flow perspectives). In this work, a novel approach for generating time predictions considering the aforementioned characteristics is proposed. For this, first, a multi-perspective constraint-based language is used to model the scenario. Thereafter, an optimized enactment plan (representing a potential execution alternative) is generated from such a model considering the current execution state of the process instances. Finally, pre-dictions are performed by evaluating a desired function over this enactment plan. To evaluate the applicability of our approach in practical settings we apply it to a real process scenario. Despite the high complexity of the considered problems, results indicate that our approach produces a satisfactory number of good predictions in a reasonable time. Ministerio de Economía y Competitividad TIN2016-76956-C3-2-R Ministerio de Economía y Competitividad TIN2015-71618-R

The construction of business process models has become an important requisite in the analysis and optimization of processes. The success of the analysis and optimization efforts heavily depends on the quality of the models. Therefore, a research domain emerged that studies the process of process modeling. This paper contributes to this research by presenting a way of visualizing the different steps a modeler undertakes to construct a process model, in a so-called process of process modeling Chart. The graphical representation lowers the cognitive efforts to discover properties of the modeling process, which facilitates the research and the development of theory, training and tool support for improving model quality. The paper contains an extensive overview of applications of the tool that demonstrate its usefulness for research and practice and discusses the observations from the visualization in relation to other work. The visualization was evaluated through a qualitative study that confirmed its usefulness and added value compared to the Dotted Chart on which the visualization was inspired. Keywords: Business process management Process model quality Process of process modeling Visualization

Demand for more environmentally friendly practices have led to the adoption of several feed supplements by the fish farming industry. In the present study, we investigated a commercially available formula that includes a mixture of three compounds: organic acids, a phytochemical and Biomin® Permeabilizing Complex. This mixture demonstrated antimicrobial properties in vitro and was able to inhibit growth of multiple species of aquatic bacterial pathogens, including Aeromonas salmonicida. Bacterial challenge was performed using A. salmonicida and three exposure routes: intra-peritoneal injection, immersion, and cohabitation. Mortality rates following infection by injection were significantly decreased in the fish that had received the supplemented feed. Fish infected through the other routes did not show a significant difference in mortality. In term of farming performance, while the fish that had received the feed supplement showed an improvement in weight gain and final weight, these changes were not found to be statistically significant. Similarly, no significant difference was observed in the feed conversion ratio. The results of this study suggest that this feed supplement may be effective at protecting rainbow trout from fish furunculosis.

Recently, researchers started using cognitive load in various settings, e.g., educational psychology, cognitive load theory, or human–computer interaction. Cognitive load characterizes a tasks’ demand on the limited information processing capacity of the brain. The widespread adoption of eye–tracking devices led to increased attention for objectively measuring cognitive load via pupil dilation. However, this approach requires a standardized data processing routine to reliably measure cognitive load. This technical report presents CEP–Web, an open source platform to providing state of the art data processing routines for cleaning pupillary data combined with a graphical user interface, enabling the management of studies and subjects. Future developments will include the support for analyzing the cleaned data as well as support forTask–Evoked Pupillary Response (TEPR) studies.

Business process models abstract complex business processes by representing them as graphical models. Their layout, solely determined by the modeler, affects their understandability. To support the construction of understandable models it would be beneficial to systematically study this effect. However, this requires a basic set of measurable key visual features, 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. 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.

Social media allow crowds to generate many ideas to swiftly respond to events like crises, public policy discourse, or online town hall meetings. This allows organizations and governments to harness the innovative power of the crowd. As part of this setting, teams that process crowd ideas must engage in social exchange processes to converge on a few promising ideas. Traditionally, teams work on self-generated ideas. However, in a crowdsourcing scenario, such as public participation in crisis response, teams may have to process crowd-generated ideas. To better understand this new practice, it is important to investigate how converging on crowdsourced ideas affects the social exchange processes of teams and resulting outcomes. We conducted a laboratory experiment in which small teams working in a crisis response setting converged on self-generated or crowdsourced ideas in an emergency response context. Our findings suggest that teams converging on self-generated ideas have better social exchange processes in terms of dominance and coordination. We found support that evaluation and coordination positively affect team member satisfaction under both experimental conditions. Implications for research and practice are discussed.

OA platinum Data visualizations are helpful tools to cognitively access large amounts of data and make complex relationships in data under- standable. This paper shows how results from neuro-physiological mea- surements, more specifically eye-tracking, can support justified design decisions about improving existing data visualizations for exploring pro- cess execution data. This is achieved by gaining insight into how vi- sualizations are used for decision-making. The presented examination is embedded in the domain of process modeling behavior analysis, and the analyses are performed on the background of representative analyt- ical questions from the domain of process model behavior analysis. We present initial findings on one out of three visualization types we have examined, which is the Rhythm-Eye visualization.

With the increasing adoption of process-aware information systems, large process model repositories have emerged. Typically, the models in such repositories are re-aligned to real-world events and demands through adaptation on a day-to-day basis. This bears the risk of introducing model redundancies and of unnecessarily increasing model complexity. If no continuous investment is made in keeping process models simple, changes will become more difficult and error-prone over time. Although refactoring techniques are widely used in software engineering to address similar problems, so far, no comparable state-of-the-art has evolved in the business process management domain. Process designers either have to refactor process models by hand or are simply unable to apply respective techniques at all. This paper proposes a catalogue of process model ''smells'' for identifying refactoring opportunities. In addition, it introduces a set of behavior-preserving techniques for refactoring large process repositories. The proposed refactorings enable process designers to effectively deal with model complexity by making process models better understandable and easier to maintain. The refactorings have been evaluated using large process repositories from the healthcare and automotive domain. To demonstrate the feasibility of the refactoring techniques, a proof-of-concept prototype has been implemented.

Declarative process models are becoming increasingly popular due to the high flexibility they offer to process participants. Based on a declarative process model, there exist numerous possible enactment plans, each one with specific values for relevant objective functions (e.g., overall completion time). How to actually execute such a model is quite challenging due to several reasons: (1) proper objective functions must be considered to find optimized enactment plans, (2) users often do not have an understanding of the overall process, (3) the presence of a variety of temporal constraints to be met during process enactment, and (4) the need to coordinate multiple instances of a process concurrently exe- cuted (which compete for shared resources). This is further complicated by the fact that the enactment of new process instances may continuously start over time and many organizations do not exactly know their future demands. In such con- text, to properly support users in enacting declarative process models, this paper suggests generating optimized enactment plans from declarative process models. The generated enactment plans may be used for different purposes, e.g., to pro- vide personal schedules to users. Moreover, they may be dynamically adapted if required. To evaluate the applicability of our approach in practical settings we apply it to a real process scenario from the healthcare domain.

Even though considerable progress regarding the technical perspective on modeling and supporting business processes has been achieved, it appears that the human perspective is still often left aside. In particular, we do not have an in-depth understanding of how process models are inspected by humans, what strategies are taken, what challenges arise, and what cognitive processes are involved. This paper contributes toward such an understanding and reports an exploratory study investigating how humans identify and classify quality issues in BPMN process models. Providing preliminary answers to initial research questions, we also indicate other research questions that can be investigated using this approach. Our qualitative analysis shows that humans adapt different strategies on how to identify quality issues. In addition, we observed several challenges appearing when humans inspect process models. Finally, we present different manners in which classification of quality issues was addressed.

In recent years, feed additives have increasingly been adopted by the aquaculture industry. These supplements not only offer an alternative to antibiotics but have also been linked to enhanced growth performance. However, the literature is still limited and provides contradictory information on their effectiveness. This is mainly due to the wide variety of available products and their complex mechanisms of action. Phytogenic feed additives have been shown to have antimicrobial effects and can improve growth performance. In the present study, we investigated the susceptibility of several fish pathogenic bacteria to a phytogenic essential oil product in vitro. In addition, we determined the protective effect of a commercial phytogenic feed additive containing oregano, anis and citrus oils on the resistance of rainbow trout Oncorhynchus mykiss to infection by Aeromonas salmonicida. The bacterium was administered through 3 different routes: intra-peritoneal injection, immersion in a bacterial solution and cohabitation with infected fish. Mortality rates were significantly lower in infected rainbow trout that had received the feed additive: the overall mortality rate across all routes of infection was 18% in fish fed a diet containing the additive compared to 37% in fish that received unsupplemented feed. The route of infection also significantly impacted mortality, with average mortality rates of 60, 17.5 and 5% for intraperitoneal injection, immersion and cohabitation, respectively. In general, fish were better protected against infection by immersion than infection by injection.

Context: The increasing adoption of process-aware information systems (PAISs) such as workflow management systems, enterprise resource planning systems, or case management systems, together with the high variability in business processes (e.g., sales processes may vary depending on the respective products and countries), has resulted in large industrial process model repositories. To cope with this business process variability, the proper management of process variants along the entire process lifecycle becomes crucial. Objective: The goal of this paper is to develop a fundamental understanding of business process variability. In particular, the paper will provide a framework for assessing and comparing process variability approaches and the support they provide for the different phases of the business process lifecycle (i.e., process analysis and design, configuration, enactment, diagnosis, and evolution). Method: We conducted a systematic literature review (SLR) in order to discover how process variability is supported by existing approaches. Results: The SLR resulted in 63 primary studies which were deeply analyzed. Based on this analysis, we derived the VIVACE framework. VIVACE allows assessing the expressiveness of a process modeling language regarding the explicit specification of process variability. Furthermore, the support provided by a process-aware information system to properly deal with process model variants can be assessed with VIVACE as well. Conclusions: VIVACE provides an empirically-grounded framework for process engineers that enables them to evaluate existing process variability approaches as well as to select that variability approach meeting their requirements best. Finally, it helps process engineers in implementing PAISs supporting process variability along the entire process lifecycle. (C) 2014 Elsevier B.V. All rights reserved., This work has been developed with the support of MICINN under the project EVERYWARE TIN2010-18011.