Laurenzi, Emanuele
E-Mail-Adresse
Geburtsdatum
Suchergebnisse
Value creation patterns for industry-relevant model-based cyber-physical systems
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.
Practice track: a learning tracker using digital biomarkers for autistic preschoolers
Preschool children, when diagnosed with Autism Spectrum Disorder (ASD), often ex- perience a long and painful journey on their way to self-advocacy. Access to standard of care is poor, with long waiting times and the feeling of stigmatization in many social set- tings. Early interventions in ASD have been found to deliver promising results, but have a high cost for all stakeholders. Some recent studies have suggested that digital biomarkers (e.g., eye gaze), tracked using affordable wearable devices such as smartphones or tablets, could play a role in identifying children with special needs. In this paper, we discuss the possibility of supporting neurodiverse children with technologies based on digital biomark- ers which can help to a) monitor the performance of children diagnosed with ASD and b) predict those who would benefit most from early interventions. We describe an ongoing feasibility study that uses the “DREAM dataset”, stemming from a clinical study with 61 pre-school children diagnosed with ASD, to identify digital biomarkers informative for the child’s progression on tasks such as imitation of gestures. We describe our vision of a tool that will use these prediction models and that ASD pre-schoolers could use to train certain social skills at home. Our discussion includes the settings in which this usage could be embedded.
Visualization of patterns for hybrid learning and reasoning with human involvement
Towards an agile and ontology-aided modeling environment for DSML adaptation
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.
AOAME 4 Society 5.0: Towards the creation and maintenance of knowledge graphs through enterprise modelling
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.
Agile visualization in design thinking
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.
An agile and ontology-aided approach for domain-specific adaptations of modelling languages
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.
A hybrid intelligent approach for the support of higher education students in literature discovery
In this paper, we present a hybrid intelligent approach that combines knowledge engineering, machine learning, and human intervention to automatically recommend literature resources relevant for a high quality of literature discovery. The primary target group that we aim to support is higher education students in their first experiences with research works. The approach builds a knowledge graph by leveraging a logistic regression algorithm which is first parameterized and then influenced by the interventions of a supervisor and a student, respectively. Both interventions allow continuous learning based on both the supervisor’s preferences (e.g. high score of H-index) and the student’s feedback to the resulting literature resources. The creation of the hybrid intelligent approach followed the Design-Science Research methodology and is instantiated in a working prototype named PaperZen. The evaluation was conducted in two complementary ways: (1) by showing how the design requirements manifest in the prototype, and (2) with an illustrative scenario in which a corpus of a research project was taken as a source of truth. A small subset from the corpus was entered into the PaperZen and Google Scholar, independently. The resulting literature resources were compared with the corpus of a research project and show that PaperZen outperforms Google Scholar
ArchiMEO: A standardized enterprise ontology based on the ArchiMate conceptual model
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.
Towards an assistive and pattern learning-driven process modeling approach
The practice of business process modeling not only requires modeling expertise but also significant domain expertise. Bringing the latter into an early stage of modeling contributes to design models that appropriately capture an underlying reality. For this, modeling experts and domain experts need to intensively cooperate, especially when the former are not experienced within the domain they are modeling. This results in a time-consuming and demanding engineering effort. To address this challenge, we propose a process modeling approach that assists domain experts in the creation and adaptation of process models. To get an appropriate assistance, the approach is driven by semantic patterns and learning. Semantic patterns are domain-specific and consist of process model fragments (or end-to-end process models), which are continuously learned from feedback from domain as well as process modeling experts. This enables to incorporate good practices of process modeling into the semantic patterns. To this end, both machine-learning and knowledge engineering techniques are employed, which allow the semantic patterns to adapt over time and thus to keep up with the evolution of process modeling in the different business domains.