Enabling Personalized Business Process Modeling: The Clavii BPM Platform

Increasing adoption of business process management systems has resulted in large business process models comprising hundreds of activities. Particularly, such process models are hard to understand and maintain. This issue requires innovative approaches to simplify and personalize process models. Therefore, this thesis introduces fundamentals for process views offering personalized perspectives for process participants by abstracting not necessary information. Furthermore, an approach for a domain-specific process modeling language, so-called Process Query Language, is presented. The latter offers process modeling notation independent abilities to define, search, and modify process models as well as process views. The proof-of-concept implementation, so-called Clavii BPM platform, shows up as integrated solution for simple, web-based business process modeling and execution. Thus, it implements basic concepts for process views and the PQL language

PQL - A Descriptive Language for Querying, Abstracting and Changing Process Models

The increasing adoption of process-aware information systems (PAISs) has resulted in large process repositories comprising large and complex process models. To enable context-specific perspectives on these process models and related data, a PAIS should provide techniques for the flexible creation and change of process model abstractions. However, existing approaches focus on the formal model transformations required in this context rather than on techniques for querying, abstracting and changing the process models in process repositories. This paper presents a domain-specific language for querying process models, describing abstractions on them, and defining process model changes in a generic way. Due to the generic approach taken, the definition of process model abstractions and changes on any graph-based process notation becomes possible. Overall, the presented language provides a key component for process model repositories

Context-Aware Querying and Injection of Process Fragments in Process-Aware Information Systems

Cyber-physical systems (CPS) are often customized to meet customer needs and, hence, exhibit a large number of hard-/software configuration variants. Consequently, the processes deployed on a CPS need to be configured to the respective CPS variant. This includes both configuration at design time (i.e., before deploying the implemented processes on the CPS) and runtime configuration taking the current context of the CPS into account. Such runtime process configuration is by far not trivial, e.g., alternative process fragments may have to be selected at certain points during process execution of which one fragment is then dynamically applied to the process at hand. Contemporary approaches focus on the design time configuration of processes, while neglecting runtime configuration to cope with process variability. In this paper, a generic approach enabling context-aware process configuration at runtime is presented. With the Process Query Language process fragments can be flexibly selected from a process repository, and then be dynamically injected into running process instances depending on the respective contextual situations. The latter can be automatically derived from context factors, e.g., sensor data or configuration parameters of the given CPS. Altogether, the presented approach allows for a flexible configuration and late composition of process instances at runtime, as required in many application domains and scenarios

Demonstrating Context-aware Process Injection with the CaPI Tool

Today's enterprises face individual customer expectations, high product variability, and an abundance of regulations. Consequently, they must cope with numerous business process variants, whose design and execution depend on a multiplicity of influencing factors, like, e.g., customer requests, resource availability, compliance rules, or process data. Moreover, already running processes need to be also adjustable to respond to contextual changes, emerging regulations, or new customer requests. With the goal to provide support for process variant management at both design and run time, this demo paper presents the prototype of the context-aware process framework (CaPI). The latter, in particular, enables the sophisticated modeling of process variants based on the context-aware injection of process fragments into a base process. Thus, executed process variants may dynamically evolve during run time, considering the current context of the respective process instance. The CaPI tool was developed based on existing adaptive process management technology. Overall, CaPI enables context-aware process injection, and, thus, the specification of varying processes while providing high process flexibility at run time

Updatable Process Views for User-centered Adaption of Large Process Models

The increasing adoption of process-aware information systems (PAISs) has resulted in large process model collections. To support users having different perspectives on these processes and related data, a PAIS should provide personalized views on process models. Existing PAISs, however, do not provide mechanisms for creating or even changing such process views. Especially, changing process models is a frequent use case in PAISs due to changing needs or unplanned situations. While process views have been used as abstractions for visualizing large process models, no work exists on how to change process models based on respective views. This paper presents an approach for changing large process models through updates of corresponding process views, while ensuring up-to-dateness and consistency of all other process views on the process model changed. Respective update operations can be applied to a process view and corresponding changes be correctly propagated to the underlying process model. Furthermore, all other views related to this process model are then migrated to the new version of the process model as well. Overall, our view framework enables domain experts to evolve large process models over time based on appropriate model abstractions

Collaborative Process Modeling with Tablets and Touch Tables — A Controlled Experiment

Collaborative process modeling involves business analysts and subject matter experts in order to properly capture and document process knowledge. In this context, appropriate tool support is required to motivate these user groups to actively participate in collaborative process modeling. This paper presents a collaborative process modeling tool that enables the experts to create, visualize and evolve process models based on multi-touch devices (e.g., tablets and touch tables). In particular, users may edit process models on their tablets and share the created or changed process models with other team members on a common touch table. For this purpose, a sophisticated and intuitive interaction concept is provided. Furthermore, results of a controlled experiment, evaluating the influence the use of tablets has on collaborative process modeling based on touch tables, are presented. Altogether the experimental results emphasize the high potential of multi-touch tools for collaborative process modeling

CONSENSORS: A Neural Network Framework for Sensor Data Analysis

Machine breakdowns in industrial plants cause production delays and financial damage. In the era of cyber-physical systems, ma- chines are equipped with a variety of sensors to monitor their status. For example, changes to sensor values might indicate an abnormal behav- ior and, in some cases, detected anomalies can be even used to predict machine breakdowns. This procedure is called predictive maintenance, which pursues the goal to increase machine productivity by reducing down times. Thereby, anomalies can be either detected by training data models based on historic data or by implementing a self-learning ap- proach. In this work, the use of neural networks for detecting anomalies is evaluated. In the considered scenarios, anomaly detection is based on temperature data from a press of a machine manufacturer. Based on this, a framework was developed for dfferent types of neural networks as well as a high-order linear regression approach. We use the proposed neural networks for restoring missing sensor values and to improve over- all anomaly detection. An evaluation of the used techniques revealed that the high-order linear regression and an autoencoder constitute best practices for data recovery. Moreover, deep neural networks, especially convolutional neural networks, provide the best results with respect to overall anomaly detection

Towards Context-aware Process Guidance in Cyber-Physical Systems with Augmented Reality

Assembly, configuration, maintenance, and repair processes in cyber-physical systems (e.g., a press line in a plant) comprise a multitude of complex tasks, whose execution needs to be controlled, coordinated and monitored. Amongst others, a process-centric guidance of users (e.g. service operators) is required, taking the high variability in the assembly of cyber-physical systems (e.g. press line variability) into account. Moreover, the tasks to be performed along these processes may be related to physical components, sensors and actuators, which need to be properly recognized, integrated and operated. In order to digitize cyber-physical processes as well as to guide users in a process-centric way, therefore, we suggest integrating process management technology, sensor/actuator interfaces, and augmented reality techniques. The paper discusses fundamental requirements for such an integration and presents an approach for process-centric user guidance that combines context and process management with augmented reality enhanced tasks. For evaluation purposes, we analyzed the cyber-physical processes of pharmaceutical packaging machines and implemented selected ones based on the approach. Overall, we are able to demonstrate the usefulness of context-aware process management for the flexible support of cyber-physical processes in the Industrial Internet of Things

Convolutional Neural Networks for Image Recognition in Mixed Reality Using Voice Command Labeling

In the context of the Industrial Internet of Things (IIoT), image and object recognition has become an important factor. Camera systems provide information to realize sophisticated monitoring applications, quality control solutions, or reliable prediction approaches. During the last years, the evolution of smart glasses has enabled new technical solutions as they can be seen as mobile and ubiquitous cameras. As an important aspect in this context, the recognition of objects from images must be reliably solved to realize the previously mentioned solutions. Therefore, algorithms need to be trained with labeled input to recognize differences in input images. We simplify this labeling process using voice commands in Mixed Reality. The generated input from the mixed- reality labeling is put into a convolutional neural network. The latter is trained to classify the images with different objects. In this work, we describe the development of this mixed-reality prototype with its backend architecture. Furthermore, we test the classification robustness with im- age distortion filters. We validated our approach with format parts from a blister machine provided by a pharmaceutical packaging company in Germany. Our results indicate that the proposed architecture is at least suitable for small classification problems and not sensitive to distortions

Towards a Hierarchical Approach for Outlier Detection in Industrial Production Settings

In the context of Industry 4.0, the degree of cross-linking between machines, sensors, and production lines increases rapidly.However, this trend also offers the potential for the improve-ment of outlier scores, especially by combining outlier detectioninformation between different production levels. The latter, in turn, offer various other useful aspects like different time series resolutions or context variables. When utilizing these aspects, valuable outlier information can be extracted, which can be then used for condition-based monitoring, alert management, or predictive maintenance. In this work, we compare different types of outlier detection methods and scores in the light of the aforementioned production levels with the goal to develop a modelfor outlier detection that incorporates these production levels.The proposed model, in turn, is basically inspired by a use casefrom the field of additive manufacturing, which is also known asindustrial 3D-printing. Altogether, our model shall improve the detection of outliers by the use of a hierarchical structure that utilizes production levels in industrial scenarios