It has traditionally been assumed that information systems describe physical reality. However, this assumption is becoming obsolete as digital infrastructures are increasingly part of real-world experiences. Digital infrastructures (ubiquitous and scalable information systems) no longer merely map physical reality representations onto digital objects but increasingly assume an active role in creating, shaping, and governing physical reality. We currently witness an “ontological reversal”, where conceptual models and digital infrastructures change physical reality. Still, the fundamental assumption remains that physical reality is the only real world. However, to fully embrace the implications of the ontological reversal, conceptual modeling needs an “institutional turn” that abandons the idea that physical reality always takes priority. Institutional reality, which includes, for example, institutional entities such as organizations, contracts, and payment transactions, is not simply part of physical reality detached from digital infrastructures. Digital infrastructures are part of institutional reality. Accordingly, the research question we address is: What are the fundamental constructs in the design of digital infrastructures that constitute and transform institutional reality? In answering this question, we develop a foundation for conceptual modeling , which we illustrate by modeling the institution of open banking and its associated digital infrastructure. In the article, we identify digital institutional entities, digital agents regulated by software, and digital institutional actions as critical constructs for modeling digital infrastructures in institutional contexts. In so doing, we show how conceptual modeling can improve our understanding of the digital transformation of institutional reality and the prescriptive role of conceptual modeling. We also generate theoretical insights about the need for legitimacy and liability that advance the study and practice of digital infrastructure design and its consequences.

The Internet of Things (IoT) creates an ecosystem that connects people and objects through the internet. IoT-enabled healthcare has revolutionized healthcare delivery by moving toward a more pervasive, patient-centered, and preventive care model. In the ongoing COVID-19 pandemic, it has also shown a great potential for effective remote patient health monitoring and management, which leads to preventing straining the healthcare system. Nevertheless, due to the heterogeneity of data sources and technologies, IoT-enabled healthcare systems often operate in vertical silos, hampering interoperability across different systems. Consequently, such sensory data are rarely shared nor integrated, which can undermine the full potential of IoT-enabled healthcare. Applying semantic technologies to IoT is a promising approach for fulfilling heterogeneity, contextualization, and situation-awareness requirements for real-time healthcare solutions. However, the enrichment of sensor streams has been under-explored in the existing literature. There is also a need for an ontology that enables effective patient health monitoring and management during infectious disease outbreaks. This study, therefore, aims to extend the existing ontology to allow patient health monitoring for the prevention, early detection, and mitigation of patient deterioration. We evaluated the extended ontology using competency questions and illustrated a proof-of-concept of ontology-based semantic representation of vital sign streams. Copyright © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

From a design science perspective, information systems and their components are viewed as artifacts. However, not much has been written yet on the ontological status of artifacts or their structure. After March & Smith’s (1995) initial classification of artifacts in terms of models, constructs, methods and instantiations, there have been only a few attempts to come up with a more systematic approach. This conceptual paper provides an ontological analysis of the notion of artifact grounded in the foundational ontology UFO. Its core is an ontological characterization of artifacts, and technical objects in general from a Design Science Research perspective, developed in conversation with other approaches. This general artifact ontology is applied in a systematic classification of IS artifacts. We include practical implications for Design Science Research.

In “Representing instances: The case for reengineering conceptual modelling grammars”, Lukyanenko et al. (2019) argue that conceptual modelling has been biased towards a focus on knowledge about general phenomena (classes) rather than about specific instances. While we agree that more attention needs to be paid to instances, we critically reflect on their underlying assumptions about instances and classes. Lukyanenko et al. (2019) assume that instances are mainly material things, and also assume that class-based modelling typically requires that class definitions include comprehensive attribute structures that are expected to be stable over time. Based on these assumptions, they conclude that classes are not needed for modelling instances. As an alternative to these assumptions, we suggest that instances can be viewed as language constructs, i.e., as objects that may be anything that is uniquely referred to and identified in human communication. Based on this assumption, we introduce an identity-oriented view of classes, implying that classes are required for modelling objects (instances). We agree with Lukyanenko et al. (2019) that a reengineering of conceptual modelling grammars is required. This reengineering would benefit from approaches such as an identity-oriented view of classes and a class-instance modelling grammar.

Paul Johannesson; Mong Li Lee; Liddle, S.; Opdahl, A.; Pastor López, O. (2017). Special issue on conceptual modeling - 34th International Conference on Conceptual Modeling (ER 2015). Data & Knowledge Engineering. 109:1-2. doi:10.1016/j.datak.2017.03.001

Two major problems in schema integration are to identify correspondences between different conceptual schemas and to verify that the proposed correspondences are consistent with the semantics of the schemas. We propose a heuristic method, based on the use of Galois lattices, for identifying schema correspondences. We show how the results of this method can be checked for correctness by introducing a number of necessary conditions for schema mergeability. These conditions are formulated in the context of a semantically rich modelling formalism, the distinguishing feature of which is the use of case grammar.

A major problem with currently existing database systems is that there often does not exist a conceptual understanding of the data. Such an understanding can be obtained by describing the data using a semantic data model, such as the ER model. Consequently, there is a need for methods that translate a schema in a traditional data model into a conceptual schema. We present a method for translating a schema in the relational model into a schema in a conceptual model. We also show that the schema produced has the same information capacity as the original schema. The conceptual model used is a formalization of an extended ER model, which also includes the subtype concept.>