scholarly journals Defining a Reference Architecture for Transformed Health Ecosystems

2021 ◽  
Author(s):  
Bernd Blobel ◽  
Kenneth Rubin
Keyword(s):  
Knowledge Representation ◽  
Systems Theory ◽  
System Development ◽  
Theory Of Knowledge ◽  
Theory Theory ◽  
Reference Architecture ◽  
Formal Representation ◽  
Development Processes

The paper discusses requirements and solutions for design and management of transformed health ecosystems. After introducing related definitions with reference to the transformation of health to P5 medicine, basics on systems, knowledge representation and management as well as system development processes and their formal representation/modelling from the perspectives of systems theory, theory of knowledge, languages and grammars are considered in some detail. As result, the ISO 23903 reference architecture is shortly introduced and compared with other existing approaches and standards.

Towards Coordination of Decentralized Embedded System Development Processes

2009 ◽  
pp. 221-226
Author(s):  
Kerstin Schmidt ◽  
Grit Walther ◽  
Thomas S. Spengler ◽  
Rolf Ernst
Keyword(s):  
Embedded System ◽  
System Development ◽  
Development Processes

Semantic and Formal Representation of Cognitive Models for the Metacognitive Architecture CARINA

2021 ◽  
pp. 54-72
Author(s):  
Alba J. Jerónimo ◽  
María P. Barrera ◽  
Manuel F. Caro ◽  
Adán A. Gómez
Keyword(s):  
Knowledge Representation ◽  
Semantic Memory ◽  
Cognitive Model ◽  
Semantic Knowledge ◽  
Cognitive Models ◽  
Formal Representation ◽  
Object Properties ◽  
Internal Information ◽  
Processing Mechanisms ◽  
The Brain

A cognitive model is a computational model of internal information processing mechanisms of the brain for the purposes of comprehension and prediction. CARINA metacognitive architecture runs cognitive models. However, CARINA does not currently have mechanisms to store and learn from cognitive models executed in the past. Semantic knowledge representation is a field of study which concentrates on using formal symbols to a collection of propositions, objects, object properties, and relations among objects. In CARINA Beliefs are a form of represent the semantic knowledge. The aim of this chapter is to formally describe a CARINA-based cognitive model through of denotational mathematics and to represent these models using a technique of semantic knowledge representation called beliefs. All the knowledge received by CARINA is stored in the semantic memory in the form of beliefs. Thus, a cognitive model represented through beliefs will be ready to be stored in semantic memory of the metacognitive architecture CARINA. Finally, an illustrative example is presented.

Towards an Operational REA Business Ontology

2009 ◽  
pp. 222-243
Author(s):  
Frederik Gailly
Keyword(s):  
Supply Chain ◽  
Information Systems ◽  
System Development ◽  
Semantic Integration ◽  
Ontology Development ◽  
Supply Chain Collaboration ◽  
Formal Representation ◽  
Heterogeneous Information ◽  
Business Information Systems ◽  
Business Information

It is widely recognized that ontologies can be used to support the semantic integration and interoperability of heterogeneous information systems. Resource Event Agent (REA) is a well-known business ontology that was proposed for ontology-driven enterprise system development. However, the current specification is neither sufficiently explicit nor formal, and thus difficult to operationalize for use in ontology- driven business information systems. In this chapter REA is redesigned and formalized following a methodology based on the reengineering extension of the METHONTOLOGY framework for ontology development. The redesign is focused on developing a UML representation of REA that improves upon existing representations and that can easily be transformed into a formal representation. The formal representation of REA is developed in OWL. The chapter discusses the choices made in redesigning REA and in transforming REA’s UML representation into an OWL representation. It is also illustrated how this new formal representation of the REA-ontology can be used to support ontology-driven supply chain collaboration.

scholarly journals Comparison of Existing Agile Approaches in the Context of Mechatronic System Development: Potentials and Limits in Implementation

2019 ◽  
Vol 1 (1) ◽  
pp. 2199-2208 ◽  
Author(s):  
Jonas Heimicke ◽  
Manuel Niever ◽  
Valentin Zimmermann ◽  
Monika Klippert ◽  
Florian Marthaler ◽  
...  
Keyword(s):  
Software Development ◽  
Physical Properties ◽  
System Development ◽  
Mechatronic System ◽  
Mechatronic Systems ◽  
Dynamic Changes ◽  
Physical Systems ◽  
Development Context ◽  
Development Processes ◽  
Relevant Factors

AbstractThe development of mechatronic systems has always been characterized by continuous handling of uncertainties. This challenge, which is associated with dynamic changes in the development context, is increasingly met by companies in the development of physical systems with the implementation of agile approaches in their development processes. However, since established approaches have their origin in software development, they reach various limits in the context of the development of mechatronic systems, e.g. due to the physical properties of the systems. Other features, such as transparent and flexible project management or targeted and early involvement of customers and users in development processes, can also be implemented in mechatronic system development. In order to derive the potentials and limits of existing agile approaches for the context of mechatronic system development, the present paper compares existing approaches with regard to relevant factors from the context of mechatronic system development. The aim is to create a basis for the targeted development, adaptation and use of agile approaches in the field of mechatronic system development.

scholarly journals Knowledge-Based Expert System Development and Validation with Petri Nets

2008 ◽  
Vol 07 (01) ◽  
pp. 37-46 ◽  
Author(s):  
Madjid Tavana
Keyword(s):  
Expert System ◽  
Knowledge Representation ◽  
Petri Nets ◽  
Decision Trees ◽  
System Development ◽  
Semantic Networks ◽  
Knowledge Based ◽  
Medical Diagnostic ◽  
High Level ◽  
Decision Tables

Expert systems (ESs) are complex information systems that are expensive to build and difficult to validate. Numerous knowledge representation strategies such as rules, semantic networks, frames, objects and logical expressions are developed to provide high-level abstraction of a system. Rules are the most commonly used form of knowledge representation and they are derived from popular techniques such as decision trees and decision tables. Despite their huge popularity, decision trees and decision tables are static and cannot model the dynamic requirements of a system. In this study, we propose Petri Nets (PNs) for dynamic system representation and rule derivation. PNs with their graphical and precise nature and their firm mathematical foundation are especially useful for building ESs that exhibit a variety of situations, including: sequential execution, conflict, concurrency, synchronisation, merging, confusion, or prioritisation. We demonstrate the application of our methodology in the design and development of a medical diagnostic expert system.

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