Laurenzi, Emanuele
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Value creation patterns for industry-relevant model-based cyber-physical systems
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.
ArchiMEO: A standardized enterprise ontology based on the ArchiMate conceptual model
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.
A hybrid intelligent approach for the support of higher education students in literature discovery
2022, Prater, Ryan, Laurenzi, Emanuele, Martin, Andreas, Hinkelmann, Knut, Fill, Hans-Georg, Gerber, Aurona, Lenat, Doug, Stolle, Reinhard, van Harmelen, Frank
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
Practice track: a learning tracker using digital biomarkers for autistic preschoolers
2022, Sandhu, Gurmit, Kilburg, Anne, Martin, Andreas, Pande, Charuta, Witschel, Hans Friedrich, Laurenzi, Emanuele, Billing, Erik, Hinkelmann, Knut, Gerber, Aurona
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.