Conceptual models offer numerous benefits but require significant investments, requiring modelers to strive to balance costs and benefits. Understanding the modeling process and the frustrations experienced by modelers can provide valuable insights for this assessment. While research acknowledges certain instances of modelers’ dissatisfaction, its scope often limits detailed examination. This study seeks to identify and analyze the main pain points associated with domain modeling through a five-phase empirical study using a multi-method approach. We identified 71 pain points, synthesized them to 41 , and prioritized 16 as the most significant and prevalent in domain modeling. We then refined, documented, and exemplified the prioritized pain points, analyzed their potential causes, and discussed their practical implications. Our findings provide valuable insights for improving modelers’ experiences and optimizing the modeling process. • 41 pain points in structural conceptual modeling were mapped. • 16 pain points were identified as the most significant and prevalent. • Pain points may stem from a lack of experience among modelers and stakeholders. • Pain points may arise from issues with modeling methods, tools, and languages. • Pain points may arise from factors related to the organization, domain, and project.

The FAIR principles define a number of expected behaviours for the data and services ecosystem with the goal of improving the findability, accessibility, interoperability, and reusability of digital objects. A key aspiration of the principles is that they would lead to a scenario where autonomous computational agents are capable of performing a “self-guided exploration of the global data ecosystem,” and act properly with the encountered variety of types, formats, access mechanisms and protocols. The lack of consistent support for some of these expected behaviours by current information infrastructures such as the internet and the World Wide Web motivated the emergence, in the last years, of initiatives such as the FAIR Digital Object (FDO) movement. The FDO aims at defining an infrastructure where digital objects can be exposed and explored according to the FAIR principles. In this paper, we report the current status of the work towards an ontology-driven conceptual model for FAIR Digital Objects. The conceptual model covers aspects of digital objects that are relevant to the FAIR principles such as the distinction between metadata and the digital object it describes, the classification of digital objects in terms of both their informational value and their computational representation format, and the relation between different types of FAIR Digital Objects.

Multi-domain model catalogs serve as empirical sources of knowledge and insights about specific domains, about the use of a modeling language’s constructs, as well as about the patterns and anti-patterns recurrent in the models of that language crosscutting different domains. They may support domain and language learning, model reuse, knowledge discovery for humans, and reliable automated processing and analysis if built following generally accepted quality requirements for scientific data management. More specifically, not unlike scientific (meta)data, models should be shared according to the FAIR principles (Findability, Accessibility, Interoperability, and Reusability). In this paper, we report on the construction of a FAIR model catalog for Ontology-Driven Conceptual Modeling research, a trending paradigm lying at the intersection of conceptual modeling and ontology engineering in which the Unified Foundational Ontology (UFO) and OntoUML emerged among the most adopted technologies. The catalog, publicly available at https://w3id.org/ontouml-models, currently includes over one hundred and forty models, developed in a variety of contexts and domains.

Data analysis can be quite valuable for the progress of science in general, but more specifically in the healthcare domain, in which it can generate medical advances and improve healthcare services.The Personal Health Train (PHT) is an approach based on distributed learning that allows analytics to be brought to the (personal health) data rather than the other way around, allowing data privacy and control to be preserved, and ethical and legal concerns to be observed.Since computational resources are necessary whenever processing is expected to be done, a sandboxed environment should be available within the healthcare organization.This environment should allow the received algorithms to be executed without interfering with the organization's regular processing.However, the IT infrastructure of a healthcare organization may not be powerful enough to perform a requested analysis task.This paper reports on our efforts to extend the PHT approach to allow data to be processed in the cloud, augmenting the processing power of the IT infrastructure of healthcare organizations.Our solution not only fulfills the functional requirements of the PHT approach, but it also complies with privacy regulations, particularly the General Data Protection Rules (GDPR).The paper describes the design and implementation of our solution, also demonstrating its suitability with a simple and yet representative case study.

Service-Oriented Computing (SOC) builds upon the intuitive notion of service already known and used in our society for a long time. SOC-related approaches are based on computer-executable functional units that often represent automation of services that exist at the social level, i.e., services at the level of human or organizational interactions. With the increasing adoption of SOC, more complex scenarios mixing computational and social services are emerging, raising the need to better understand the relationship between social and computational services and to specify and model them accordingly. In this paper we present our ontology-based approach to dynamic service provisioning. Our approach aims at improving the current state of the art by allowing an explicit distinction between social and computation services and dynamic service provisioning support for these two types of services. We illustrate the applicability of our approach with a use case scenario in the health care domain.

The realization of ubiquitous networks brings new challenges to application development. In this kind of network, services and, more specifically web services, have been used to provide the functionality required by its users and applications. In such environments features like automatic service discovery and composition are of need. This paper reports the ongoing work towards an approach for dynamic service discovery and composition. This approach is based on modeling services as entities independent of concrete implementations. This abstraction allows applications and users to subscribe to a desired functionality and later, the supporting platform discovers the service or a composition of services to provide that functionality.