Abstract: Robotic Process Automation (RPA) is the automation of rule-based routine processes to increase process efficiency and to reduce process costs. In practice, however, RPA is often applied without knowledge of the concrete effects its introduction will have on the automated process and the involved stakeholders. Accordingly, literature on the quantitative effects of RPA is scarce. The objective of this paper is to provide empirical insights into improvements and deteriorations of business processes achieved in twelve RPA projects in the automotive industry. The results indicate that the positive benefits promised in literature are not always achieved in practice. In particular, shorter case duration and better quality are not confirmed by the empirical data gathered in the considered RPA projects. These quantitative insights constitute a valuable contribution to the currently rather qualitative literature on RPA.

Abstract: 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.

Abstract: Business process monitoring techniques have been investigated in depth over the last decade to enable organizations to deliver process insight. Recently, a new stream of work in predictive business process monitoring leveraged deep learning techniques to unlock the potential business value locked in process execution event logs. These works use Recurrent Neural Networks, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), and suffer from misinformation and accuracy as they use the last hidden state (as the context vector) for the purpose of predicting the next event. On the other hand, in operational processes, traces may be very long, which makes the above methods inappropriate for analyzing them. In addition, in predicting the next events in a running case, some of the previous events should be given a higher priority. To address these shortcomings, in this paper, we present a novel approach inspired by the notion of attention mechanism, utilized in Natural Language Processing and, particularly, in Neural Machine Translation. Our proposed approach uses all hidden states to accurately predict future behavior and the outcome of individual activities. Experimental evaluation of real-world event logs revealed that the use of attention mechanisms in the proposed approach leads to a more accurate prediction.

Abstract: Data lakes are on the rise as data platforms for any kind of analytics, from data exploration to machine learning. They achieve the required flexibility by storing heterogeneous data in their raw format, and by avoiding the need for pre-defined use cases. However, storing only raw data is inefficient, as for many applications, the same data processing has to be applied repeatedly. To foster the reuse of processing steps, literature proposes to store data in different degrees of processing in addition to their raw format. To this end, data lakes are typically structured in zones. There exists various zone models, but they are varied, vague, and no assessments are given. It is unclear which of these zone models is applicable in a practical data lake implementation in enterprises. In this work, we assess existing zone models using requirements derived from multiple representative data analytics use cases of a real-world industry case. We identify the shortcomings of existing work and develop a zone reference model for enterprise-grade data lake management in a detailed manner. We assess the reference model’s applicability through a prototypical implementation for a real-world enterprise data lake use case. This assessment shows that the zone reference model meets the requirements relevant in practice and is ready for industry use.

Abstract: The traceable execution of business processes and choreographies using smart contracts is one prominent application of blockchain technology in Business Process Management (BPM). Existing approaches support a large set of patterns, modeling languages, and blockchain architectures, which cover a wide range of practical scenarios. However, they largely neglect the important aspect of time, a crucial part of process and choreography models manifested in deadlines, delays, and other temporal constraints. We argue that this deficit is due to inherent limitations of smart contracts—in particular the absence of a natural notion of measuring time—on popular blockchain platforms used in research and practice. We introduce a set of time measures available on blockchain platforms to alleviate these issues, and systematically compare their properties. We also give hints as to their suitability for facilitating various temporal constraints commonly found in process models.

Abstract: This review paper aims to explore state-of-the-art research and scientific literature about Enterprise Architecture (EA) resilience. Based on a systematic literature review, 850 articles have been subjected to evaluation for relevance. Based on the findings in 58 selected papers, we conclude that the field of EA resilience is still in its infancy. We identified several definitions and classified six types of resilience measures, based on information type (qualitative/quantitative), the source of the disruption (internal/external), and the duration of the resilience (short-term/long-term). Based on the review, we found 19 metrics that are candidates for EA practitioners to consider for the design of measurement and assessment methods for EA resilience. In addition, we identified relevant research from Information Systems sub-domains and other sciences that can be incorporated to create a holistic view on EA resilience. Based on published definitions of resilience in the selected papers, we propose a definition of the concept of EA resilience. This definition is validated using expert opinion and creates a starting point for reasoning about EA resilience and future research.

Abstract: Microservice-based architectures (MSAs) gained momentum in industrial and research communities since finer-grained and more independent components foster reuse and reduce time to market. However, to come from the design of MSAs to running applications, substantial knowledge and technology-specific expertise in the deployment and integration of microservices is needed. In this paper, we propose a model-driven and pattern-based approach for composing microservices, which facilitates the transition from architectural models to running deployments. Using a unified modelling for MSAs, including both their integration based on Enterprise Integration Patterns (EIPs) and deployment aspects, our approach enables automatically generating the artefacts for deploying microservice compositions. This helps abstracting away the underlying infrastructure including container orchestration platforms and middleware layer for service integration. To validate the feasibility of our approach, we illustrate its prototypical implementation, with Kubernetes used as container orchestration system and OpenFaaS used for managing integration logic, and we present a case study.

Abstract: Followed by the introduction of IoT and new sustainable technologies, energy management, Quality of Service and decrease of communication costs become important and complex for enterprise systems at airports. The aviation authorities’ reports reveal that the airport ICT investments are mainly focused on travel safety, mobile commerce, and new technologies. The main idea behind a smart airport is to deploy IoT network managed through a Cloud-Fog-Edge paradigm for a smart platform and optimize the airport’s efficiency. An IoT cloud-based platform solution supports multiple types of data, advanced analytics, artificial intelligence, and machine learning techniques. However, cloud computing has certain limitations such as increased delay in data reporting, increased latency in accessing user network, limited customization, increased reliance on external network and data privacy. Fog-Cloud and Edge-Cloud paradigms can overcome the weaknesses of cloud computing architectures. Therefore, to understand the organizational impact of combining the usage of cloud, fog, and edge computing, we created an enterprise architecture that can be applied in a smart airport demonstration study. The enterprise architecture modelling was done by using ArchiMate and validated by means of an expert assessment and prototype implementation.

Abstract: Blockchain has emerged as one of the most promising and revolutionary technologies in the past years. Companies are exploring use cases in the hope of reaping benefits from this technology. However, to achieve the desired impact, it is not sufficient to merely replace existing technologies. Current business processes must be redesigned to realize benefits. This paper shows how a number of best practices for Business Process Redesign can be applied to leverage blockchain technology. Specifically, we adapt these best practices for redesigning processes intended to execute on blockchain. We further explore their applicability using a case study. The major elements of process redesign for blockchain are: (1) changing the view from intra- to inter-organizational processes by using the shared data ledger of blockchain, (2) using smart contracts performers of tasks and connectors of processes, (3) using blockchain as means for data communication, and (4) using tokens to manage digitally represented assets.

Abstract: Added value can be extracted from event logs generated by business processes in various ways. However, although complex computations can be performed over event logs, the result of such computations is often difficult to explain; in particular, it is hard to determine what parts of an input log actually matters in the production of that result. This paper describes a framework to provide explainable results for queries executed over sequences of events, where individual output values can be precisely traced back to the data elements of the log that contribute to (i.e. "explain") the result. This framework has been implemented into the BeepBeep event processing engine and empirically evaluated on various queries.

Abstract: Media production printing is following the Industry 4.0 era developments, towards the concept of smart printing factory. There, printers, post-processing machinery, media and product handling robots, automated guided vehicles and human operators shall be integrated in a seamless way for achieving higher production rates in a more efficient way, in a safe and ergonomic working environment. This integration of the various actors and the orchestration of their activities can be enabled by a process management system. This paper presents such a Printing Process Management System (PPMS), based on Business Process Management (BPM) technologies, that models and enacts end-to-end activities in a print shop. PPMS is a core information system of a cyber-physical system (CPS) for direct control of devices and humans in a smart production printing environment. It was implemented and demonstrated in the European EIT OEDIPUS 1 project for a major international printing technology provider, proving feasibility and giving insights into how BPM concepts and tools can be applied in smart printing factories.

Abstract: Conformance checking takes a process model and a process log as input and quantifies the degree of conformance between both. This allows a comparison between the intended behavior represented by the model and the actual behavior captured by the log and is useful for many applications such as auditing. Existing approaches calculate conformance as follows: each deviation between model and log is corrected by an alignment, e.g., inserting a missing event to the log, that has a standard per-deviation cost of 1. While deviations in the model can be handled this way, there is no way to differentiate between intended (e.g., ad-hoc repair of instances) and unintended (e.g., security breaches) deviations. Hence this work proposes an advanced cost function, that allows for per-deviation adjustments of the per-deviation costs. By inspecting how the data elements of subsequent tasks are affected, it becomes possible to automatically increase or decrease the per-deviation costs of 1, thus allowing for an automatic classification of deviation causes. The proposed approach works offline and online (i.e., at runtime) and is evaluated based on a real-world dataset from the manufacturing domain.

Abstract: Building on earlier work, this paper aims to demonstrate and discuss an instantiated architecture for situation-aware logistics in an operational environment using smart returnable assets. The demonstration is based on a motivation scenario focusing on exception management. The system outline and its components, interfaces, and enabling technologies are described and linked to the different layers of the architecture. This paper documents and illustrates the use of the system with detailed models and screenshots. Earlier work is extended using business rules to identify and quantify exceptions and potential disruptions. Specifically, it is shown how shipment data are enhanced with data from IoT sensors of smart returnable assets to provide situation-aware decision support based on data analytical methods. This demonstration provides scholars and practitioners, active in the fields of enterprise computing, insights into the concepts, models and engineering technologies used to implement an architecture for situation-aware logistics. The instantiated architecture provides a rich testbed for experiments, measurements, and incorporate the ideas promoted by the international data space initiative. An online recording is available to support the demonstration and ignite discussion about the potential of IoT technologies and future research directions in pursuit of the smart logistics vision.

Abstract: Open Source Software (OSS) has been recognized by the software development community as an effective way to deliver software. Unlike traditional software development, OSS development is driven by collaboration among developers spread geographically and motivated by common goals and interests. Besides this fact, it is recognized by the OSS community the need to understand OSS development process and its activities. Our goal is to investigate the state-of-art about OSS process through conducting a systematic literature review providing an overview of how the OSS community has been investigating OSS process over past years. We identified and summarized OSS process activities and their characteristics and translated them into an OSS macro process using BPMN notation. As a result, we systematically analyzed 33 studies presenting an overview of the OSS process research and a generalized OSS development macro process represented by BPMN notation with a detailed description of each OSS process activity and roles in OSS environment. We conclude that OSS process can be in practice further investigated by researchers. In addition, the presented OSS process can be used as a guide for OSS projects and be adapted according to each OSS project reality. It provides insights to managers and developers who want to improve their development process even in OSS and traditional environments. Finally, recommendations for OSS community regarding OSS process activities are provided.

Abstract: The recurring but mutually distinct ways of executing a business process are referred to as process variants. There are approaches available in the literature aimed at finding such process variants and determining how they differ from each other. However, organizations are more interested in understanding the effect of these differences in terms of the performance of a business process. In this context, we propose a novel approach to enable organizations to learn from each other through business process benchmarks. To do so, the approach bins organizations based on what extent they achieve their performance targets in relation to their Key Performance Indicators (KPIs). Within each bin, process variants are identified using trace clustering. Then, significant differences among process variants are determined and highlighted. These differences help organizations to improve the performance of their processes. We implemented our approach, evaluated its performance, and applied it in a case study.

Abstract: Enterprises collaborate in government-regulated markets by developing and realizing exchange relationships. However, current Accounting Information Systems (AIS) are organized in an enterprise-centric way. We introduce the Multilateral AIS (MAIS) perspective on top of the Bilateral (Shared Ledger) and Unilateral Enterprise-Specific perspectives for Corporate Reporting. We reconsider our previous conceptualization in the OntoUML language of the Economic Exchange lifecycle in Bilateral AIS from an Economic System and Market perspective. We identify requirements for existing accounting framework improvement in MAIS. An online platform case is provided as an illustration.

Abstract: An enterprise architecture (EA) is a high-level representation of an enterprise, used for managing the relation between business and IT. In order to improve the contribution of IT to the business, all elements of an EA should be traceable to the business model and vice versa. However, in practice this is not the case. In addition to reasoning about cost structures and goal contributions of IT to the business, as is customary in EA, traceability would allow practitioners to reason about the contribution of IT to the value offerings of a business. In this research paper we present the results from an experiment where we wanted to refine guidelines for transforming a business model into an EA that we have derived in earlier research. Based on this experiment we refine the guidelines, identify building blocks for a business model (BM) based EA design and illustrate this with an example.

Abstract: Blockchain technology provides an auditable and tamper-proof distributed infrastructure that can support distributed workflow management. Blockchains based on a Byzantine Fault Tolerance (BFT) ordering consensus protocol address many of the shortcomings of proof-of-work chains, such as latency and non-finality of consensus. They are a suitable technology to support workflow execution, in large part because applications do not need to be adapted to deal with eventual consistency and delayed consensus of proof-of-work blockchains. Moreover, in contrast to earlier work that has typically developed custom workflow engines using smart contracts, we port an existing, full-featured workflow engine, the YAWL workflow engine, to a blockchain-based workflow management system (WfMS) without smart contracts.

Abstract: More data is almost always beneficial for analysis and machine learning tasks. In many realistic situations however, an enterprise cannot share its data, either to keep a competitive advantage or to protect the privacy of the data sources, the enterprise’s clients for example. We propose a method for data owners to share synthetic or fake versions of their data without sharing the actual data, nor the parameters of models that have direct access to the data. The method proposed is based on the privGAN architecture where local GANs are trained on their respective data subsets with an extra penalty from a central discriminator aiming to discriminate the origin of a given fake sample. We demonstrate that this approach, when applied to subsets of various sizes, leads to better utility for the owners than the utility from their real small datasets. The only shared pieces of information are the parameter updates of the central discriminator. The privacy is demonstrated with white-box attacks on the most vulnerable elments of the architecture and the results are close to random guessing. This method would apply naturally in a federated learning setting.

Abstract: 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.

Abstract: The verification of global compliance rules (GCR) in process choreographies (e.g., partner-spanning quality assurance in supply chains) is crucial and challenging due to the restricted visibility of the private processes of the collaborating partners. This paper provides a novel algorithm that decomposes global compliance rules into assertions that can be verified by the partners in a distributed way without revealing any private process details. The decomposition is based on transitivity properties of the underlying GCR specification. This work uses GCR based on antecedent and occurrence patterns and illustrates the transitivity properties based on their specification in first order predicate logic. It is formally shown that the original GCR can be reconstructed from the assertions, which ensures the viability of the approach. The algorithms are prototypically implemented and applied to several scenarios. The ability of checking global compliance constitutes a fundamental pillar of any approach implementing process choreographies with multiple partners.

Abstract: As microservices become one of the predominant architectural styles for distributed enterprise computing, there is a need to devise frameworks which allow for the goal driven composition and coordination of such highly granular service components. Even though a number of service composition and orchestration techniques have been proposed over the past decade, these do not take into account stakeholders’ intents as well as data, control, and temporal interdependencies between actions microservices can perform. In this paper, we present extensions to goal models with respect to data, logical and temporal dependencies exhibited between tasks and actions among microservices, and we propose a framework based on a graph transformation approach which, when applied to the extended goal models, can yield service invocation plans that achieve the desired requirements and constraints denoted by the specific goal models being considered.

Abstract: Automated consistent software updating is challenging for distributed internet-of-things applications. We are developing a solution based on allowing several versions of the software to coexist on the same device, so that new and old software can be gradually phased in and out. We demonstrate our solution using a smart-city application for preventing thefts of bike batteries.

Abstract: Previous works on formalizing enterprise application integration (EAI) scenarios showed an emerging need for setting up formal foundations for integration patterns, the EAI building blocks, in order to facilitate model-driven development of enterprise applications and ensure their correctness. So far, the formalization requirements were focusing on more "conventional" integration scenarios, in which control-flow, transactional persistent data and time aspects were considered. However, none of these works took into consideration another arising EAI trend that covers social and multimedia computing. In this work we propose a Petri net-based formalism that addresses requirements arising from the multimedia domain. We also demonstrate how to realize some of the most frequently used multimedia patterns and discuss which implications our formal proposal may bring into the area of the multimedia EAI development.

Abstract: While model support concerning the variability and customization of business processes has been studied extensively, related Process Performance Indicators (PPIs) have not been addressed similarly. An approach that helps PPI calculation and promotes reusability in this context is critical for organizations. A fast adaptation of their information system is needed, concerning new clients’ requirements, new policies or evaluations of audit entities. Business Process Feature Model (BPFM) is a well-known approach to support process variability by modeling all possible variants and generating process skeleton based on different customizations. However, this approach does not consider the PPI’s modeling and calculation variability. In this paper, we will talk about the thesis project concerning the extension of the BPFM approach in order to the integrate PPI variability. We present our first results relying on our experience in a real industrial case; a tree model of PPIs variability linked to the BPFM model facilitating the modeling, the customization and the reasoning about PPIs variants depending on the process customization and PPIs definitions.

Abstract: In this paper, we propose a methodology to verify the regulatory compliance of a composition of multiple interacting business process models. The proposed solution is based on providing a compliance certification for the components of the composition, and evaluating the compliance of the encompassing composition by aggregating the information shared by the certifications, going beyond existing techniques merely checking compliance on message flows. An advantage of adopting such methodology is that the computational details of the inner process models are not needed to verify compliance of the composition, so that companies can decide to not disclose such details while still being able to show that adopting their services would lead to compliant solutions in all possible scenarios.

Abstract: Loose Inter-Organizational Cooperation in Cloud Computing consists on dynamically constructing Cloud Workflow Services (CWS). CWS is a loosely coupled Inter-Organizational business process. The basic bricks of a CWS are its Process Chunks (PCs) which are dynamically discovered, selected and composed in the Cloud. A Process chunk is a dynamic configuration of microservices constructed, published, selected and composed through the Cloud. In this paper we are addressing how an organization playing the role of a PC-provider could construct loosely coupled PCs. To achieve this goal we are proposing a new approach that aims to configure a PC using microservices. This approach starts with PC specification and then dynamically selecting and composing the required microservices to execute the specified model. In order to validate our contribution a drug development case study is used.

Abstract: Autonomous agents are well established to assist customers in contract negotiations on electronic markets. Recently, AI-based conversational agents provide more natural customer communication. Yet, on markets with varying negotiation items natural conversation can not be guaranteed to include all possible customer preferences for optimum negotiation results. In this paper, we propose a negotiation model supporting dynamic interactive preference post-specification and present a Hybrid Alternating Offers Protocol for that purpose. We demostrate it for conversationally initiated negotiations in the context of an app-market.

Abstract: Chemical manufacturers, importers, or distributors produce a Safety Data Sheet (SDS) for every chemical they use based on the Globally Harmonized System of Classification and Labelling of Chemicals(GHS). SDS is essential to adequately deliver information about the hazards of the particular chemical during its usage and handling. Authoring GHS-compliant SDS is very costly. Furthermore, revisions in GHS add, delete or modify the contents of an SDS. A pre-existing SDS therefore also needs to be adapted to changes in the GHS. Automation in the generation and checking of SDS per GHS would results in substantial savings in cost and effort.Additionally, if the SDS pre-exists, we ensure that all requisite label elements as per GHS are present in SDS, including precautionary statements, hazard statements, signal words, and pictograms. Our approach uses a versioned graph database, and image processing to generate, check, and keep an SDS compliant with the GHS. We demonstrate our approach with a pre-existing SDS of a chemical called DOWANOL™PM High Purity Grade, complying with GHS versions 7 and 8.