Laurenzi, Emanuele
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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.
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 business-to-IT alignment in the cloud
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.
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
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.
An agile and ontology-aided modeling environment
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.
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.
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.
Workplace Learning - Providing Recommendations of Experts and Learning Resources in a Context-sensitive and Personalized Manner
Support of workplace learning is increasingly important as change in every form determines today's working world in industry and public administrations alike. Adapt quickly to a new job, a new task or a new team is a major challenge that must be dealt with ever faster. Workplace learning differs significantly from school learning as it should be strictly aligned to business goals. In our approach we support workplace learning by providing recommendations of experts and learning resources in a context-sensitive and personalized manner. We utilize user s' workplace environment, we consider their learning preferences and zone of proximal development, and compare required and acquired competencies in order to issue the best suited recommendations. Our approach is part of the European funded project Learn PAd. Applied research method is Design Science Research. Evaluation is done in an iterative process. The recommender system introduced here is evaluated theoretically based on user requirements and practically in an early evaluation process conducted by the Learn PAd application partner.