The measurement of perceived ontological deficiencies of conceptual modeling grammars

Over recent years, many scholars have studied the conceptual modeling of information systems based on a theory of ontological expressiveness. This theory offers four constructs that inform properties of modeling grammars in the form of ontological deficiencies, and their implications for development and use of conceptual modeling in IS practice. In this paper we report on the development of a valid and reliable instrument for measuring the perceptions that individuals have of the ontological deficiencies of conceptual modeling grammars. We describe a multi-stage approach for instrument development that incorporates feedback from expert and user panels. We also report on a field test of the instrument with 590 modeling practitioners. We further study how different levels of modeling experience influence user perceptions of ontological deficiencies of modeling grammars. We provide implications for practice and future research.

Conceptual modeling - an epistemological foundation

The success of contemporary organizations depends on their ability to make appropriate decisions. Making appropriate decisions is inevitably bound to the availability and provision of relevant information. Information systems should be able to provide information in an efficient way. Thus, within information systems development a detailed analysis of information supply and information demands has to prevail. Based on Syperski’s information set and subset-model we will give an epistemological foundation of information modeling in general and show, why conceptual modeling in particular is capable of specifying effective and efficient information systems. Furthermore, we derive conceptual modeling requirements based on our findings. A short example illustrates the usefulness of a conceptual data modeling technique for the specification of information systems.

An epistemological foundation of conceptual modeling

In a business environment, making the right decisions is vital for the success of a company. Making right decisions is inevitably bound to the availability and provision of relevant information. Information systems are supposed to be able to provide this information in an efficient way. Thus, within information systems development a detailed analysis of information supply and information demands has to prevail. Based on Szyperski’s information set and subset-model we will give an epistemological foundation of information modeling in general and show, why conceptual modeling in particular is capable of developing effective and efficient information systems. Furthermore, we derive conceptual modeling requirements based on our findings.

Is Use of Optional Attributes and Associations in Conceptual Modeling Always Problematic? Theory and Empirical Tests

Prior research has argued that use of optional properties in conceptual models results in loss of information about the semantics of the domains represented by the models. Empirical research undertaken to date supports this argument. Nevertheless, no systematic analysis has been done of whether use of optional properties is always problematic. Furthermore, prior empirical research might have deliberately or unwittingly employed models where use of optionality always causes problems. Accordingly, we examine analytically whether use of optional properties is always problematic. We employ our analytical results to inform the design of an experiment where we systematically examined the impact of optionality on users’ ability to understand domains represented by different types of conceptual models. We found evidence that use of optionality undermines users’ ability to understand the domain represented by a model but that this effect weakens when use of mandatory properties to replace optional properties leads to more-complex models.

Improving the Semantics of Conceptual-Modeling Grammars:A New Perspective on an Old Problem

A core activity in information systems development involves understanding the
conceptual model of the domain that the information system supports. Any conceptual model is ultimately created using a conceptual-modeling (CM) grammar. Accordingly, just as high quality conceptual models facilitate high quality systems development, high quality CM grammars facilitate high quality conceptual modeling. This paper seeks to provide a new perspective on improving the quality of CM grammar semantics. For the past twenty years, the leading approach to this topic has drawn on ontological theory. However, the ontological approach captures just half of the story. It needs to be coupled with a logical approach. We show how ontological quality and logical quality interrelate and we outline three contributions of a logical approach: the ability to see familiar conceptualmodeling problems in simpler ways, the illumination of new problems, and the ability to prove the benefit of modifying CM grammars.

On the Ontological Quality and Logical Quality of Conceptual-Modeling Grammars: The Need for a Dual Perspective

A core activity in information systems development involves building a conceptual model of the domain that an information system is intended to support. Such models are created using a conceptual-modeling (CM) grammar. Just as high-quality conceptual models facilitate high-quality systems development, high-quality CM grammars facilitate high-quality conceptual modeling. This paper provides a new perspective on ways to improve the quality of the semantics of CM grammars. For many years, the leading approach to this topic has relied on ontological theory. We show, however, that the ontological approach captures only half the story. It needs to be coupled with a logical approach. We explain how the ontological quality and logical quality of CM grammars interrelate. Furthermore, we outline three contributions that a logical approach can make to evaluating the quality of CM grammars: a means of seeing some familiar conceptual-modeling problems in simpler ways; the illumination of new problems; and the ability to prove the benefit of modifying existing CM grammars in particular ways. We demonstrate these benefits in the context of the Entity-Relationship grammar. More generally, our paper opens up a new area of research with many opportunities for future research and practice.

Some Aspects of Domain Specific Conceptual Modeling for Simulation of Logistic Terminal Operations

When simulation modeling is used for performance improvement studies of complex systems such as transport terminals, domain specific conceptual modeling constructs could be used by modelers to create structured models. A two stage procedure which includes identification of the problem characteristics/cluster - ‘knowledge acquisition’ and identification of standard models for the problem cluster – ‘model abstraction’ was found to be effective in creating structured models when applied to certain logistic terminal systems. In this paper we discuss some methods and examples related the knowledge acquisition and model abstraction stages for the development of three different types of model categories of terminal systems

Ontological engineering for conceptual modeling

In acknowledging the importance of ontologies in conceptual modeling, database integration and business process modeling, this paper introduces a set of principles for building ontologies. Starting from Guarino's meta-properties of ontological terms, the paper describes the denotational semantics of the meta-properties and derives from them some engineering rules and checks for constructing domain specific conceptual models, based on the overarching requirement to assign meanings to concepts using tags and labels. Parallel research by the authors into the use of contextual references and roles to restrict such meanings will be published elsewhere.

Introducing usability in a conceptual modeling-based software development process.

Usability plays an important role to satisfy users? needs. There are many recommendations in the HCI literature on how to improve software usability. Our research focuses on such recommendations that affect the system architecture rather than just the interface. However, improving software usability in aspects that affect architecture increases the analyst?s workload and development complexity. This paper proposes a solution based on model-driven development. We propose representing functional usability mechanisms abstractly by means of conceptual primitives. The analyst will use these primitives to incorporate functional usability features at the early stages of the development process. Following the model-driven development paradigm, these features are then automatically transformed into subsequent steps of development, a practice that is hidden from the analyst.

How do practitioners use conceptual modeling in practice?

Much research has been devoted over the years to investigating and advancing the techniques and tools used by analysts when they model. As opposed to what academics, software providers and their resellers promote as should be happening, the aim of this research was to determine whether practitioners still embraced conceptual modeling seriously. In addition, what are the most popular techniques and tools used for conceptual modeling? What are the major purposes for which conceptual modeling is used? The study found that the top six most frequently used modeling techniques and methods were ER diagramming, data flow diagramming, systems flowcharting, workflow modeling, UML, and structured charts. Modeling technique use was found to decrease significantly from smaller to medium-sized organizations, but then to increase significantly in larger organizations (proxying for large, complex projects). Technique use was also found to significantly follow an inverted U-shaped curve, contrary to some prior explanations. Additionally, an important contribution of this study was the identification of the factors that uniquely influence the decision of analysts to continue to use modeling, viz., communication (using diagrams) to/from stakeholders, internal knowledge (lack of) of techniques, user expectations management, understanding models' integration into the business, and tool/software deficiencies. The highest ranked purposes for which modeling was undertaken were database design and management, business process documentation, business process improvement, and software development. (c) 2005 Elsevier B.V. All rights reserved.