2022, Egger, Nicolas Cyrill, Laurenzi, Emanuele, Ferreira Pires, Luis, Hammoudi, Slimane, Seidewitz, Edwin

Recent development in technology brought Cyber-Physical Systems (CPS) to innovate across many industry fields. However, given the heterogeneous nature of the different integrated components from the virtual and physical spaces, creating a CPS requires high expertise in both engineering and the addressed application domain. Hence, a CPS is complex and time-consuming to design, deploy and test. A model-based approach can tackle this problem by enabling conceptual models to control physical objects and fostering the quick creation of Cyber-Physical Systems. The process logic and decision logic are implemented in re-usable graphical models instead of software code, which makes possible to involve domain-experts early in the design of the CPS. Given the relatively young approach, this paper explores the various model-based CPS that are relevant across industry and how they create value, respectively. For the investigation, a case study research strategy was adopted, which included both literature and a workshop targeting several industry experts. Finally, a pattern matching technique was applied to detect value proposition elements across the created cases.

2020, Hinkelmann, Knut, Laurenzi, Emanuele, Martin, Andreas, Montecchiari, Devid, Spahic, Maja, Thönssen, Barbara, Hammoudi, Slimane, Ferreira Pires, Luis, Selić, Bran

Many enterprises face the increasing challenge of sharing and exchanging data from multiple heterogeneous sources. Enterprise Ontologies can be used to effectively address such challenge. In this paper, we present an Enterprise Ontology called ArchiMEO, which is based on an ontological representation of the ArchiMate standard for modeling Enterprise Architectures. ArchiMEO has been extended to cover various application domains such as supply risk management, experience management, workplace learning and business process as a service. Such extensions have successfully proven that our Enterprise Ontology is beneficial for enterprise applications integration purposes.

2018, Laurenzi, Emanuele, Hinkelmann, Knut, Izzo, Stefano, Reimer, Ulrich, van der Merwe, Alta

The advent of digitalization exposes enterprises to an ongoing transformation with the challenge to quickly capture relevant aspects of changes. This brings the demand to create or adapt domain-specific modeling languages (DSMLs) efficiently and in a timely manner, which, on the contrary, is a complex and time-consuming engineering task. This is not just due to the required high expertise in both knowledge engineering and targeted domain. It is also due to the sequential approach that still characterizes the accommodation of new requirements in modeling language engineering. In this paper we present a DSML adaptation approach where agility is fostered by merging engineering phases in a single modeling environment. This is supported by ontology concepts, which are tightly coupled with DSML constructs. Hence, a modeling environment is being developed that enables a modeling language to be adapted on-the-fly. An initial set of operators is presented for the rapid and efficient adaptation of both syntax and semantics of modeling languages. The approach allows modeling languages to be quickly released for usage.

2018, Hinkelmann, Knut, Laurenzi, Emanuele, Martin, Andreas, Thönssen, Barbara, Dornberger, Rolf

Decision makers use models to understand and analyze a situation, to compare alternatives and to find solutions. Additionally, there are systems that support decision makers through data analysis, calculation or simulation. Typically, modeling languages for humans and machine are different from each other. While humans prefer graphical or textual models, machine-interpretable models have to be represented in a formal language. This chapter describes an approach to modeling that is both cognitively adequate for humans and processable by machines. In addition, the approach supports the creation and adaptation of domain-specific modeling languages. A metamodel which is represented as a formal ontology determines the semantics of the modeling language. To create a graphical modeling language, a graphical notation can be added for each class of the ontology. Every time a new modeling element is created during modeling, an instance for the corresponding class is created in the ontology. Thus, models for humans and machines are based on the same internal representation.

2022, Laurenzi, Emanuele

Knowledge Graphs (KGs) have matured as a topical technique that organizations increasingly adopt for structuring knowledge and its subsequent analysis and reasoning as well as for integrating information extracted from different data sources. KGs also play a central role in Artificial Intelligence systems, as their structured knowledge can be used as input to improve predictions of Machine Learning. Yet, one of the main challenges in KGs is the creation and maintenance of structured and formalized knowledge (or ontologies), which requires high expertise in ontology engineering as well as is tedious and time-consuming. In this workshop, I will present AOAME: an Agile and Ontology-Aided Metamodelling Environment, with which ontologies can be automatically created and maintained while easily adapting a modeling language and creating enterprise models. To underpin the explanation of the research approach, a real-world case taken from a recently finished EU project will be implemented in AOAME.

2020, Witschel, Hans Friedrich, Pande, Charuta, Martin, Andreas, Laurenzi, Emanuele, Hinkelmann, Knut, Dornberger, Rolf

2018, Kritikos, Kyriakos, Laurenzi, Emanuele, Hinkelmann, Knut, Mann, Zoltán Ádám, Stolz, Volker

Cloud computing offers a great opportunity for business process (BP) flexibility, adaptability and reduced costs. This leads to realising the notion of business process as a service (BPaaS), i.e., BPs offered on-demand in the cloud. This paper introduces a novel architecture focusing on BPaaS design that includes the integration of existing state-of-the-art components as well as new ones which take the form of a business and a syntactic matchmaker. The end result is an environment enabling to transform domain-specific BPs into executable workflows which can then be made deployable in the cloud so as to become real BPaaSes.

2020, Laurenzi, Emanuele, Hinkelmann, Knut, Montecchiari, Devid, Goel, Mini, Dornberger, Rolf

This chapter presents an agile visualization approach that supports one of the most widespread innovation processes: Design Thinking. The approach integrates the pre-defined graphical elements of SAP Scenes to sketch digital scenes for storyboards. Unforeseen scenarios can be created by accommodating new graphical elements and related domain-specific aspects on-the-fly. This fosters problem understanding and ideation, which otherwise would be hindered by the lack of elements. The symbolic artificial intelligence (AI)-based approach ensures the machineinterpretability of the sketched scenes. In turn, the plausibility check of the scenes is automated to help designers creating meaningful storyboards. The plausibility check includes the use of a domain ontology, which is supplied with semantic constraints. The approach is implemented in the prototype AOAME4Scenes, which is used for evaluation.

2020, Laurenzi, Emanuele, Hinkelmann, Knut

Domain-Specific Modelling Languages (DSMLs) offer constructs that are tailored to better capture the representational needs of an application domain. They provide customized graphical notations, which facilitate understanding of models by domain experts. As a result, DSMLs allow the construction of domain-specific models that support collaboration, improve work processes and enhance decision-making. Given their special purpose, however, a DSML has to be built each time a new application domain is to be addressed, which is quite demanding and time-consuming. A valid alternative is the creation of DSMLs through domain-specific adaptations of existing modelling languages. This solution has the benefits of starting from a baseline of well-known concepts, which is adapted to fit a specific purpose. Current engineering processes for building or adapting modelling languages, however, lack agility. It follows a sequential engineering lifecycle, where modelling and evaluation activities cannot start before the DSML is deployed for use. Such a sequential approach tends to keep the language engineer separate from the domain expert, who is hindered from gaining experience from the DSML until it is created. The separation of the two roles is a threat to the high quality of the DSML as it requires the joint effort of both experts. On the other hand, the new requirements that arise from the suggestions of the domain expert have to go through the whole engineering lifecycle (i.e. capture and document the requirement, conceptualise, implement and deploy), which is time-consuming. These current drawbacks of present engineering processes have been explored in two case studies, which report the development of a DSML for Patient Transferal Management and a DSML for Business Process as a Service. In this research an agile meta-modelling approach has been conceived to address the identified drawbacks. Specifically, the approach allows the quick interleaving of language engineering, modelling and evaluation activities. Hence, the close cooperation between the language engineers and the domain experts is fostered from an early stage. A set of operators are proposed to enable on-the-fly domain-specific adaptations of modelling languages, thus avoiding the sequential engineering phases. This agile meta-modelling aims to promote both the high-quality and quick development of DSMLs through domain-specific adaptations. Moreover, to avoid misinterpretation of the meaning of the newly created modelling constructs as well as ensuring machine interpretability of models, the agile meta-modelling has been supplemented by an ontology-aided approach. The latter embeds the specification specifications of modelling languages into an ontology. A set of semantic rules are proposed to support the propagation of language adaptations from the graphical to the machine-interpretable representation. In turn, the approach was developed in the modelling environment AOAME, which allows preserving consistency between the graphical and the machine-interpretable knowledge while domain-specific adaptations are performed. An evaluation strategy is proposed, from which three criteria were derived to evaluate the approach. Firstly, the correct design of the approach is evaluated by the extent to which it satisfies the requirements. Secondly, the operationability of the approach is evaluated by its ability to preserve consistency between the graphical and the machine-interpretable representations. Thirdly, the generality of the approach is evaluated by its ability to be applied in different application domains. The evaluation of operationability and generality are supported by implementing real-world use cases in AOAME. Consequently, the approach contributes to the practice in three different application domains, the Patient Transferal Management, Business Process as a Service and Innovation Processes. The scientific contribution of the approach spans research fields of Domain-Specific Modelling Language, Meta-Modelling, Enterprise Modelling and Ontologies.

2018, Laurenzi, Emanuele, Hinkelmann, Knut, van der Merwe, Alta, Buchmann, Robert Andrei, Karagiannis, Dimitris, Kirikova, Marite

Enterprise knowledge is currently subject to ever-changing, complex and domain-specific modeling requirements. Assimilating these requirements in modeling languages brings the benefits associated to both domain-specific modeling languages (DSMLs) and a baseline of well-established concepts. However, there are two problems that hamper the speed and efficiency of this activity: (1) the separation between the two key expertise: language engineering and domain knowledge, and (2) the sequential modeling language engineering life-cycles. In this work, we tackle these two challenges by introducing an Agile and Ontology-Aided approach implemented in our Modeling Environment - the AOAME. The approach seamlessly integrates meta-modeling and modeling in the same modeling environment, thus cooperation between language engineers and domain experts is fostered. Sequential engineering phases are avoided as the adaptation of the language is done on-the-fly. To this end, a modeling language is grounded with an ontology language providing a clear, unambiguous and machine-interpretable semantics. Mechanisms implemented in the AOAME ensure the propagation of changes from the modeling environment to the graph-based database containing the ontology.