The importance of high-performance computers in car networks increases to realize software assistance functions. These computers offer new opportunities for use cases in car testing and diagnostics towards vehicle software and services. Therefore, car testing and diagnostics are no longer limited to the testing and functional checking of electric and electronic components. Proven concepts from computer science, such as business process management, can be integrated into the car network to provide a uniform basis for automotive use cases. In this paper, (i) the Business Process Model and Notation (BPMN) standard is used to model diagnostic processes in future car production lines and (ii) a car-tailored BPMN workflow engine is introduced to execute the processes in a high-performance vehicular computer. The research on executable BPMN models in car networks bridges the gap between interdisciplinary business processes and execution in automotive IT systems.

Cars with software-intensive features such as autonomous driving bring new opportunities to Original Equipment Manufacturers (OEMs) and their manufacturing processes. The complexity of internal engineering processes is increasing as software-intensive functions are connected to electric and electronic car architectures, which need to be tested and enabled for functionality in car manufacturing. However, internal engineering processes can be simplified in car manufacturing by automating previously manual activities and flexibly managing variants. In this paper, we present a concept for managing the online scheduling of diagnostic tests during dynamic car manufacturing. We introduce the online scheduling of diagnostic tests on a high-performance car computer and compare the concept with state-of-the-art concepts for offline scheduling on remote production servers. To evaluate our concept, we conduct an OEM case study that shows that online scheduling not only reduces the total testing time in manufacturing while managing schedule variants more flexibly, but also increases the overall value added in car production by simplifying existing employee role models. We illustrate a scenario of how new car architectures can facilitate digital manufacturing processes.

Scientists are increasingly using the workflow technology as a means for modeling and execution of scientific experiments. Despite being a very powerful paradigm workflows still lack support for trial-and-error modeling, as well as flexibility mechanisms that enable the ad hoc repetition of experiment logic to enable, for example, the convergence of results or to handle errors. In this respect, in our work on enabling multi-scale/field (multi-*) experiments using choreographies of scientific workflows, we contribute a method comprising all necessary steps to conduct the repetition of choreography logic across all workflow instances participating in a multi-* experiment. To realize the method, we contribute i) a formal model representing choreography models and instances, including the re-execute and iterate operations for choreographies, and based on it ii) algorithms for determining the rewinding points, i.e., the activity instances where the rewinding has to stop and iii) enable the actual rewinding to a previous execution state and repetition of the choreography. We present the implementation of our approach in a message-based, service-oriented system that allows scientists to model, control, and execute scientific choreographies as well as perform the rewinding and repeating of choreography logic. We also provide an evaluation of the performance of our approach.

Blockchains are distributed systems that facilitate the interaction of autonomous entities with limited mutual trust. Many of them support transactional applications known as smart contracts, which access and modify the shared world state. Permissionless blockchains are completely decentralized and do not require mutual trust between interacting peers, but at the expense of having low performance and limited data confidentiality capabilities. On the other hand, permissioned blockchains solve these issues, but sacrifice complete decentralization and involve more trust assumptions. Therefore, there is no single blockchain system suitable for all use-cases. However, this becomes a serious integration challenge for enterprises that need to interact with multiple permissioned and permissionless blockchains in the same context. To facilitate this, we propose an approach that enables composing smart contract functions of various permissioned and permissionless blockchain systems by providing the ability to invoke them directly from business process models using a new task type. To keep this task blockchain-agnostic, we designed a generic technique to identify smart contract functions, as well as a generic metric to describe the degree-of-confidence in the finality of blockchain transactions. Thereby, the proposed approach extends our previous work, BlockME, which provides business modeling extensions only suitable for interacting with permissionless blockchains. To validate the practical feasibility of our approach, we provide a detailed system architecture and a prototypical implementation supporting multiple blockchains.

Function-as-a-Service (FaaS) is an emerging cloud service model that enables composing applications using arbitrary, small, and event-driven code snippets managed by cloud providers and that can be scaled to zero. The scalability properties of FaaS look attractive for handling rare or unexpected high loads that affect only particular functionalities of the application. However, deciding on the component granularity upfront or reengineering the architecture of an entire application for rare workloads is often a very difficult challenge or even infeasible. In this work, we introduce a method that prepares annotated functionalities for handling rare workloads by automatically extracting them from the source code of the application and additionally deploying them as FaaS functions, while keeping the original application's functionalities and architecture unchanged. In this way, the benefits of FaaS can be leveraged without the need to reengineer the application only for rare cases. We validate our method by means of a prototype, evaluate its feasibility in a set of experiments, and discuss limitations and future work.

The importance of data is steadily increasing in the domain of business process management due to recent advances in data science, IoT, and Big Data. To reflect this paradigm shift towards data-awareness in service choreographies, we introduced the notion of data-aware choreographies based on concepts for Transparent Data Exchange (TraDE) in our previous works. The goal is to simplify the modeling of business-relevant data and its exchange in choreography models while increasing their run time flexibility. To further improve and simplify the modeling of data-related aspects in service choreographies, in this paper, we focus on the extension of our TraDE concepts to support the modeling of data transformations in service choreographies. Such data transformation capabilities are of dire need to mediate between different data formats, structures and representations of the collaborating participants within service choreographies. Therefore, the paper presents a modeling extension as means for specifying and executing heterogeneous data transformations in service choreographies based on our TraDE concepts.

Utility, defined as the perceived satisfaction with a service, provides the ideal means for decision making on the level of individual entities and collectives participating in a large-scale dynamic system.Previous works have already introduced the concept into the area of collective adaptive systems, and have discussed what is the necessary infrastructure to support the realization of the involved theoretical concepts into actual decision making.In this work we focus on two aspects.First, we provide a concrete utility model for a case study that is part of a larger research project.Second, we incorporate this model into our implementation of the proposed architecture.More importantly, we design and execute an experiment that aims to empirically evaluate the use of utility for decision making by comparing it against simpler decision making mechanisms.

This work is motivated by the increasing importance and business value of data in the fields of business process management, scientific workflows as a field in eScience, and Internet of Things, all of which profiting from the recent advances in data science and Big data. We introduce a management life cycle that renders data as first-class citizen in service choreographies and defines the functions and artifacts necessary for enabling transparent and efficient data exchange among choreography participants. The inherent goal of the life cycle, functions and artifacts is to help decouple the data flow, data exchange and management from the control flow in service compositions and choreographies. This decoupling enables peer-to-peer data exchange in choreographies and provides the means for more sophisticated data management and exchange, as well as data exchange and provisioning optimization.

The success of the Cloud computing paradigm, together with the increase of Cloud providers and optimized Infrastructure-as-a-Service (IaaS) offerings have contributed to a raise in the number of research and industry communities that are strong supporters of migrating and running their applications in the Cloud. Focusing on eScience simulation-based applications, scientific workflows have been widely adopted in the last years, and the scientific workflow management systems have become strong candidates for being migrated to the Cloud. In this research work we aim at empirically evaluating multiple Cloud providers and their corresponding optimized and nonoptimized IaaS offerings with respect to their offered performance, and its impact on the incurred monetary costs when migrating and executing a workflow-based simulation environment. The experiments show significant performance improvements and reduced monetary costs when executing the simulation environment in off-premise Clouds.

Reuse of service orchestrations or service compositions is extensively studied in the literature of process modeling. Sub-processes, process templates, process variants, and process reference models are employed as reusable elements for these purposes. The concept of process fragments has been previously introduced in order to capture parts of a process model and store them for later reuse. However, similar efforts on facilitating the reuse of processes that cross the boundaries of organizations expressed as service choreographies are not available yet. In this paper, we introduce the concept of choreography fragments as reusable elements for service choreography modeling. Choreography fragments can be extracted from choreography models, adapted, stored, and later inserted into new models. Based on a formal model for choreography fragments, we define methods and algorithms for the extraction and insertion of fragments from and into service choreographies. We then discuss an experimental and proof-of-concept evaluation of our proposal.