Frederik Gailly

The pragmatic quality of Resources- Events-Agents diagrams: an experimental evaluation

The Resources‐Events‐Agents (REA) model is a semantic data model for the development of enterprise information systems. Although this model has been proposed as a benchmark for enterprise information modelling, only few studies have attempted to empirically validate the claimed benefits of REA modelling. Moreover, these studies focused on the evaluation of REA‐based system implementations rather than directly assessing the REA‐modelled conceptual schemas that these systems are based on. This paper presents a laboratory experiment that measured the user understanding of diagrammatic conceptual schemas developed using the REA model. The theoretical foundation for the hypotheses are cognitive theories that explain pattern recognition phenomena and the resulting reduction in cognitive effort for understanding conceptual schemas. The results of the experiment indicate a more accurate understanding of the business processes and policies modelled when users recognize the REA model’s core pattern of enterprise information in the diagram. The implication for modelling practice is that the use of the REA model improves the requirements engineering process by facilitating the user validation of conceptual schemas produced by analysts, and thus helps ensuring the quality of the enterprise information system that is developed or implemented.

Positioning and Formalizing the REA Enterprise Ontology.

ABSTRACT: Recent Resource, Event, Agent (REA) research has focused on defining and theoretically justifying the ontologys contents. Here, we elaborate on more practical issues related to REA. First, we classify REA and its applications using ontology classification schemes and application frameworks. This analysis clarifies REAs application potential but also reveals weaknesses that may impede its operationalization. Next, we propose a new REA ontology specification that uses a Unified Modeling Language (UML) profile for graphically representing ontologies. This new specification is more complete and precise than previously available specifications, without compromising understandability. It can easily be transformed into a machine‐readable representation for automatic processing, which is a prerequisite for the successful application of REA in business modeling, software engineering, knowledge representation, and interoperability creation. The paper ends with a proof of concept application in which a f...

Towards ontology-driven information systems: redesign and formalization of the REA ontology

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 paper 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 paper discusses the choices made in redesigning REA and in transforming REAs UML representation into a OWL representation.

Ontology-driven business modelling: improving the conceptual representation of the REA ontology

Business modelling research is increasingly interested in exploring how domain ontologies can be used as reference models for business models. The Resource Event Agent (REA) ontology is a primary candidate for ontology-driven modelling of business processes because the REA point of view on business reality is close to the conceptual modelling perspective on business models. In this paper Ontology Engineering principles are employed to reengineer REA in order to make it more suitable for ontology-driven business modelling. The new conceptual representation of REA that we propose uses a single representation formalism, includes a more complete domain axiomatization (containing definitions of concepts, concept relations and ontological axioms), and is proposed as a generic model that can be instantiated to create valid business models. The effects of these proposed improvements on REA-driven business modelling are demonstrated using a business modelling example.

Enhancing Declarative Process Models with DMN Decision Logic

Modeling dynamic, human-centric, non-standardized and knowledge-intensive business processes with imperative process modeling approaches is very challenging. Declarative process modeling approaches are more appropriate for these processes, as they offer the run-time flexibility typically required in these cases. However, by means of a realistic healthcare process that falls in the aforementioned category, we demonstrate in this paper that current declarative approaches do not incorporate all the details needed. More specifically, they lack a way to model decision logic, which is important when attempting to fully capture these processes. We propose a new declarative language, Declare-R-DMN, which combines the declarative process modeling language Declare-R with the newly adopted OMG standard Decision Model and Notation. Aside from supporting the functionality of both languages, Declare-R-DMN also creates bridges between them. We will show that using this language results in process models that encapsulate much more knowledge, while still offering the same flexibility.

Measuring the perceived semantic quality of information models

Semantic quality expresses the degree of correspondence between the information conveyed by a model and the domain that is modelled. As an early quality indicator of the system that implements the model, semantic quality must be evaluated before proceeding to implementation. Current evaluation approaches are based on ontological or meta-model analysis and/or use objective metrics. They ignore the model users perception of semantic quality, which also determines whether the benefits of using a faithful model will be achieved. The paper presents the development of a perceived semantic quality measure. It presents a measure pre-test, i.e. a study aimed at refining and validating a new measure before its use in research and practice. The results of the pre-test show that our measure is reliable and that it is sufficiently differentiated from other perception-based measures of information model use like ease of use, usefulness, and user information satisfaction.

Integrating Business Domain Ontologies with Early Requirements Modelling

Requirements engineering is an important and complex phase during systems development because it combines the description of the systems domain with the elicitation, specification and validation of application-specific requirements. In this paper we propose an ontology-based requirements engineering approach that integrates the use of domain ontologies to capture domain knowledge into early requirements modelling techniques, which primarily aim at eliciting and representing the organisational and intentional context of the system. The proposed approach can be used with different types of domain ontologies and different requirements engineering techniques. The approach is illustrated using the REA enterprise ontology, the E3-value ontology and the i* requirements modelling framework.

The Structured Process Modeling Theory (SPMT) a cognitive view on why and how modelers benefit from structuring the process of process modeling

After observing various inexperienced modelers constructing a business process model based on the same textual case description, it was noted that great differences existed in the quality of the produced models. The impression arose that certain quality issues originated from cognitive failures during the modeling process. Therefore, we developed an explanatory theory that describes the cognitive mechanisms that affect effectiveness and efficiency of process model construction: the Structured Process Modeling Theory (SPMT). This theory states that modeling accuracy and speed are higher when the modeler adopts an (i) individually fitting (ii) structured (iii) serialized process modeling approach. The SPMT is evaluated against six theory quality criteria.

Using the REA Ontology to Create Interoperability between E-Collaboration Modeling Standards

E-collaboration modeling standards like ISO/IEC 15944 and the UN/CEFACT Modeling Methodology (UMM) provide techniques, terms and reference models for modeling collaborative business processes. They offer a standardized approach for business partners to codify the business conventions, agreements and rules that govern business collaborations and to share business process information. Although effective in creating interoperability between organizations at the business process level, prospective business partners are required to commit to the same modeling standard. In this paper we show how the REA enterprise ontology can be used to semantically relate the ISO/IEC 15944 and UMM e-collaboration standards. Using the REA ontology as a shared business collaboration ontology, business partners can create interoperability between their respective business process models without having to use the same modeling standard.

On the Symbiosis between Enterprise Modelling and Ontology Engineering

In different fields, ontologies are increasingly deployed to specify and fix the terminology of a particular domain. In enterprise modelling, their main use lies in serving as a knowledge base for enterprise model creation. Such models, based on one or several compatible so-called enterprise-specific ontologies, allow for model alignment and solve interoperability issues. On the other hand, enterprise models may enrich the enterprise-specific ontology with concepts emerging from practical needs. In order to achieve this reciprocal advantage, we developed an ontology-based enterprise modeling meta-method that facilitates modelers to construct their models using the enterprise-specific ontology. While doing so, modelers give their feedback for ontology improvement. This feedback is subject to community approval, after which it is possibly incorporated into the ontology, thereby evolving the ontology to better fit the enterprises needs.