Business Modeling and Software Design

Modern process optimization approaches do build on various qualitative and quantitative tools, but are mainly limited to simple relations in different process perspectives like cost, time or stock. In this paper, a new approach is presented which focuses on techniques of the area of Artificial Intelligence to capture complex relations within processes. Hence, a fundamental value increase is intended to be gained. Existing modeling techniques and languages serve as basic concepts and try to realize the junction of apparently contradictory approaches. This paper therefore draws a vision of promising future process optimization techniques and presents an innovative contribution.

Businesses and software development processes alike are being challenged by the digital transformation and agility trend. Business processes are increasingly being automated yet are also expected to be agile. Current business process modeling is typically labor-intensive and results in rigid process models. For larger processes it becomes arduous to consider all possible process variations and enactment circumstances. Contemporaneously, in software development microservices have become a popular software architectural style for partitioning business logic into fine-grained services accessible via lightweight protocols which can be rapidly and individually developed by small teams and flexibly (re)deployed. This results in an increasing number of available services and a much more dynamic IT service landscape. Thus, a more dynamic form of modeling, integration, and orchestration of these microservices with business processes is needed. This paper describes agile business process modeling with Microflows, an automatic lightweight declarative approach for the workflow-centric orchestration of semantically-annotated microservices using agent-based clients, graph-based methods, and the lightweight semantic vocabularies JSON-LD and Hydra. A graphical modeling tool supports Microflow modeling and provides dynamic constraint and microservice recommendations via a recommender service using machine learning of domain-categorized Microflows. To be able to utilize existing process model knowledge, a case study shows how Microflow constraints can be automatically extracted from existing Business Process Modeling Notation (BPMN) process files and transformed into flexible Microflow constraints, which can then be used to train the recommendation service. Further, it describes process mining of Microflow execution logs to automatically extract BPMN models and automated recovery for errors occurring during enactment.

This paper reflects on the developments over the past decades in business modelling and software design in the context of a specific niche market. In the first part, the paper describes the changing role of models and modelling in customer projects and in the development of standard software solutions through the past 35 years. In the second part, the paper analyses different types of models and the relation between the model as an artefact and the modelled reality. The paper concludes with a short discussion of the IT system itself as a necessarily reduced and incomplete model of business processes.

Nowadays, domains like Cyber-Physical Systems (CPS) and Internet of Things (IoT) are highly affected by short product cycles and high pricing pressure. Business Process oriented organizations are known to perform better in such flexible environments. However, especially industries which are focused on delivering low cost systems are facing big challenges if the according business processes are not aligned with the capabilities of the product. Consequently, development effort is spent for features which are never addressed by any business goal. With this work, we propose to use a combined variability management in order to create an integrated view on the product variability from an organizational point as well as from a technical view. Using this approach helps in order to identify business drivers as well as to establish a mature product line development.

Networked physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and connectivity, allow for run-time acquisition of user data. This in turn can enable information systems which capture the “current” user state and act accordingly. The use of this data would result in context-aware applications that get fueled by user data (and environmental data) to adapt their behavior. Yet the use of data is often restricted by privacy regulations and norms; for example, the location of a person cannot be shared without given consent. In this paper we propose a design approach that allows for weaving context-awareness and privacy-by-design into the specification of information systems. This is to be done since the very early stages of the software development, while the enterprise needs are captured (and understood) and the software features are specified on that basis. In addition to taking into account context-awareness and privacy-sensitivity these two aspects will be balanced, especially if they are conflicting. The presented approach extends the “Software Derived from Business Components” (SDBC) approach. We partially demonstrate our proposed way of modeling, by means of a case example featuring land border security. Our proposed way of modeling would allow developers to smoothly reflect context and privacy features in the application design, supported by methodological guidelines that span over the enterprise modeling and software specification. Those features are captured as technology-independent societal demands and are in the end reflected in technology-specific (software) solutions. Traceability between the two is possible as well as re-use of modeling constructs.

With the introduction of smart manufacturing, the scope of IT expands towards physical processes on the shop floor. Enterprise architects, software engineers and process engineers will have to work closely together to build the information systems that are connected to the shop floor and aligned with the business needs of smart manufacturers. However, it is unclear whether they have the means to do so. This research aims to provide enterprise architecture modelling support for smart manufacturers by investigating ArchiMate 3.0’s fitness for this purpose. ArchiMate 3.0 meta-model is compared to the ISA-95 standard for enterprise systems and control systems integration. Modelling patterns are introduced, along with some new modelling concepts, to compensate for deficiencies found. The patterns proposed are validated as part of a case study.

Business Process Management (BPM) has become a management discipline that translates strategy into people and technology based execution – fast and at minimal risk. It helps organizations to realize the full potential of their digitalization enterprise transformation initiatives. BPM is implemented through the “process of process management” (PoPM). To assure continuous improvements of the PoPM an appropriate digitalization approach for the PoPM itself is essential. However, little work has been done in this field and companies are failing to recognise the importance of an integrated digitalization of the PoPM. This paper presents a successful approach for the digitalization of the PoPM to enable a powerful BPM-Discipline. It includes experiences from a first pilot implementation of the developed prototype, the BPM-D Application.

The understanding of Business Process modelling is an essential approach for an Organization or Enterprise to achieve set objectives and improve its operations. Recent development has shown the importance of representing processes to carry out continuous improvement. One important aspect of enterprise modelling is actually its involvement in competition. The modelling and simulation of Business Processes has been able to show Business Analysts, and Managers where bottleneck exists in the system, how to optimize the Business Process to reduce cost of running the Organization, and the required resources needed for an Organization. Although large scale organizations have already been involved in such BPM applications, on the other hand, Small Medium Enterprises (SME) have not drawn much attention with this respect. It seems that SME need more practical tools for modelling and analysis with minimum expenses if possible. One approach to make BPM more applicable to SME but, also, to larger scale organizations would be to properly integrate it with analytical management computational techniques, including the game-theoretic analysis, the Markov-chain modelling and the Cognitive Maps methodology. In BPM research the Petri Nets methodology has already been involved in theory, applications and BPM Software tools. However, this is not the case in the previously mentioned as well as to other analytical management techniques. It is, therefore, important in BPM research to take into account such techniques but focusing on specific modelling requirements. One such requirement is the modelling of market share competition. This paper presents an overview of some important analytical management computational techniques, as the above, that could be integrated in the BPM framework, based on the market share competition analysis paradigm. It provides an overview along with examples of market share competition analysis of the applicability of such methods in the BPM field. The major goal of this systematic overview is to propose steps for the integration of such analytical techniques in the BPM framework so that they could be widely applied.

While the sharing of information has turned into a typical practice for governments and organizations, numerous datasets are as yet not openly published since they may violate users’ privacy. The hazard on data protection infringement is a factor that regularly hinders the distribution of information and results in a push back from governments and organizations. Moreover, even published information, which may appear safe, can disregard client security because of the uncovering of users’ personalities. This paper proposes a privacy risk assessment model for open data structures to break down and diminish the dangers related with the opening of data. The key components are privacy attributes of open data reflecting privacy risks versus benefits exchanges-off related with the utilization situations of the information to be open. Further, these attributes are assessed using a decision engine into a privacy risk indicator value and a privacy risk mitigation measure. Privacy risk indicator expresses the anticipated estimation of data protection dangers related with opening such information and privacy risk mitigation measure expresses the estimations that should be connected on the information to evade the expected security risks. The model is exemplified through five genuine scenarios concerning open datasets.