Conceptual modelling for requirements analysis

4.2Conceptual modelling for requirements analysis

“On the ontological expressiveness of information systems analysis and design.”

“Abstract. Information systems analysis and design (ISAD) methodologies provide facilities for describing existing or conceived real-world systems. These facilities are ontologically expressive if they are capable of describing all real-world phenomena completely and clearly. In this paper we formally examine the notion of the ontological expressiveness of a grammar and discuss some of its implications for the design and use of ISAD methodologies. We identify some generic ways in which ontological expressiveness may be undermined in a grammar and some potential consequences of these violations. We also examine ontological expressiveness within the context of some other desirable features that might be considered in the design of ISAD methodologies.”

(Wand and Weber, 1995)

“On the deep structure of information systems.”

“Abstract. The deep structure of an information system comprises those properties that manifest the meaning of the real-world system the information system is intended to model. In this paper we describe three models we have developed of information systems' deep-structure properties. The first, the representational model, proposes a set of constructs that enable the ontological expressiveness of grammars used to model information systems (such as the entity-relationship model) to be evaluated. The second, the state-tracking model, proposes four requirements that information systems must satisfy if they are to faithfully track the real-world system they are intended to model. The third, the good-decomposition model, proposes three necessary conditions that information systems must meet if they are to be well decomposed. The three models provide a theoretically based, structured way of evaluating grammars that are used to analyse, design and implement information systems and scripts that have been generated using these grammars to describe specific information systems.”

(Wand and Weber, 2002)

"Research Commentary: Information Systems and Conceptual Modeling--A Research Agenda"

Abstract:

"Within the information systems field, the task of conceptual modeling involves building a representation of selected phenomena in some domain. High-quality conceptual- modeling work is important because it facilitates early detection and correction of system development errors. It also plays an increasingly important role in activities like business process reengineering and documentation of best-practice data and process models in enterprise resource planning systems. Yet little research has been undertaken on many aspects of conceptual modeling. In this paper, we propose a framework to motivate research that addresses the following fundamental question: How can we model the world to better facilitate our developing, implementing, using, and maintaining more valuable information systems? The framework comprises four elements: conceptual-modeling grammars, conceptual-modeling methods, conceptual-modeling scripts, and conceptual-modeling contexts. We provide examples of the types of research that have already been undertaken on each element and illustrate research opportunities that exist."

(Wand, Storey and Weber, 1999)

“Conceptual models or semantic data models were developed to capture the meaning of an application domain as perceived by someone. Moreover, concepts employed in semantic data models have recently been adopted in object-oriented approaches to systems analysis and design. To employ conceptual modeling constructs effectively, their meanings have to be defined rigorously. Often, however, rigorous definitions of these constructs are missing. This situation occurs especially in the case of the relationship construct. Empirical evidence shows that use of relationships is often problematical as a way of communicating the meaning of an application domain. For example, users of conceptual modeling methodologies are frequently confused about whether to show an association between things via a relationship, an entity, or an attribute. Because conceptual models are intended to capture knowledge about a real-world domain, we take the view that the meaning of modeling constructs should be sought in models of reality. Accordingly, we use ontology, which is the branch of philosophy dealing with models of reality, to analyze the meaning of common conceptual modeling constructs. Our analysis provides a precise definition of several conceptual modeling constructs. Based on our analysis, we derive rules for the use of relationships in entity-relationship conceptual modeling. Moreover, we show how the rules resolve ambiguities that exist in current practice and how they can enrich the capacity of an entity-relationship conceptual model to capture knowledge about an application domain.”

4.3Conceptual modelling for simulation and execution

(Dinu and Nadkarni, 2007)

4.4Conceptual modelling for learning and understanding

(Gregory, 1992)

4.5Conceptual modelling for knowledge representation

4.6Some problems in conceptual modelling

4.7Reading and writing conceptual models: different skills, different outcomes

§5An important aside: formal conceptual structures

§6The design of Conceprocity and its justification

6.1How and why Conceprocity differs from G-MOT

So why not simply reuse the existing G-MOT formalism? Table gives a (gentle) critique of Mot+ and G-MOT and outlines how Conceprocity differs:

Table How Conceprocity differs from G-MOT

G-MOT	Conceprocity
Based on the object oriented (OO) approach extensively used in software engineering, but just as the OO approach is often vague about its philosophical and pragmatic antecedents, so (sometimes) is G-MOT	Conceprocity is a little closer to UML – particularly in the ways in which concepts are related. In part because Conceprocity owes a great debt to G-MOT, it too is sometimes vague about its antecedents. However, this present paper attempts to make explicit the antecedents of Conceprocity.
G-MOT is object-influenced, most obviously by class diagrams. But it separates procedures out from concepts, thus eschewing encapsulation (which has value in software engineering but not always in clarifying meaning and understanding)	Conceprocity follows G-MOT. Inheritance is explicitly supported between concepts by means of a specialisation- generalisation relationship. The effect of encapsulation can be achieved by deft use of hierarchy what appears at one level to be an atomic concept is expanded at a lower level in the modelling hierarchy.
The visual representation used is sometimes obscure, specifically in the areas of how the different types of relationship are displayed; they are signified by a character label rather than by a visual device	Conceprocity prefers a UML-influenced style in which the type of arrow shows the kind of relationship. This is initially a little more difficult to teach and learn, but subsequently makes Conceprocity models easier to read and to understand.
The visual representation used is sometimes unclear, particularly the visual distinction between classes and object-instances (although this is better in G-MOT than in the earlier Mot+)	Conceprocity is clearer again in this respect.
The expression is not very visual, depending too much on textual elements and not on images and icons: it does not engage the right brain	Particularly in the simple usage profile, users are actively encouraged to make full use of icons, images and sketches.
It does not permit the clear expression of algorithms, in particular conditionality (if… then… else… endif) and repetition (do while…; repeat until…)	Whereas in G-MOT conditional statements are represented as principles, Conceprocity prefers to make this visually much clearer by using logical connectors and the separate event syntax (here following the event process chain paradigm suggested by(Scheer, Thomas and Adam, 2005)).
The language does not encourage consideration of object state and/or events	Conceprocity uses the event notion to make this much clearer.
Cardinality and ordinality (multiplicity) is not made explicit in associations	Conceprocity follow s the conventions of UML class diagrams in this respect, making multiplicity much more evident – if the modeller wants this.
G-MOT is a standalone (“desktop”) application available only for Windows. It is therefore not SaaS, software as a service – which is needed to make web-based collaboration on concept maps possible and easy	Conceprocity is implemented using the Lucidchart web-based diagramming system, which is SaaS.

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