Yair Wand

Anchoring data quality dimensions in ontological foundations

of an organization. A leading computer industry information service firm indicated that it “expects most business process reengineering initiatives to fail through lack of attention to data quality.” An industry executive report noted that more than 60% of surveyed firms (500 medium-size corporations with annual sales of more than

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

20 million) had problems with data quality. The Wall Street Journal also reported that, “Thanks to computers, huge databases brimming with information are at our fingertips, just waiting to be tapped. They can be mined to find sales Anchoring Data Quality Dimensions Ontological Foundations

On the ontological expressiveness of information systems analysis and design grammars

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.

An ontological analysis of the relationship construct in conceptual modeling

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.

Complexity and clarity in conceptual modeling: comparison of mandatory and optional properties

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.

A framework for empirical evaluation of conceptual modeling techniques

Two versions of the entity-relationship model (ERM) are compared in this empirical study. One model grammar uses optional properties and the other employs mandatory properties and subtypes. The optional grammar produces apparently less complex models than the mandatory with subtypes. An ontological analysis indicates that mandatory properties may be superior to optional properties in providing clearer representations. The Cognitive Theory of Multimedia Learning is used to hypothesize superior local information provided by mandatory properties can lead to improved viewer understanding of a model. An experiment comparing the two ERM grammars is described and results confirm the use of mandatory relationships leads to improved understanding even though the model is apparently more complex. These results suggest clarity within the model may be more important than the apparent complexity of the model when a model is used for developing domain understanding.

Theoretical foundations for conceptual modelling in information systems development

The paper presents a framework for the empirical evaluation of conceptual modeling techniques used in requirements engineering. The framework is based on the notion that modeling techniques should be compared via their underlying grammars. The framework identifies two types of dimensions in empirical comparisons—affecting and affected dimensions. The affecting dimensions provide guidance for task definition, independent variables and controls, while the affected dimensions define the possible mediating variables and dependent variables. In particular, the framework addresses the dependence between the modeling task—model creation and model interpretation—and the performance measures of the modeling grammar. The utility of the framework is demonstrated by using it to categorize existing work on evaluating modeling techniques. The paper also discusses theoretical foundations that can guide hypothesis generation and measurement of variables. Finally, the paper addresses possible levels for categorical variables and ways to measure interval variables, especially the grammar performance measures.

Organizational memory information systems: a transactive memory approach

Conceptual modelling in information systems development is the creation of an enterprise model for the purpose of designing the information system. It is an important aspect of systems analysis. The value of a conceptual modelling language (CML) lies in its ability to capture the relevant knowledge about a domain. To determine which constructs should be included in a CML it would be beneficial to use some theoretical guidelines. However, this is usually not done. The purpose of this paper is to promote the idea that theories related to human knowledge can be used as foundations for conceptual modelling in systems development. We suggest the use of ontology, concept theory, and speech act theory. These approaches were chosen because: (1) they deal with important and different aspects relevant to conceptual modelling and (2) they have already been used in the context of systems analysis. For each approach we discuss: the rationale for its use, its principles, its application to conceptual modelling, and its limitations. We also demonstrate the concepts of the three approaches by analysing an example. The analysis also serves to show how each approach deals with different aspects of modelling.

Emancipating instances from the tyranny of classes in information modeling

Effective management of organizational memory (OM) is critical to collaboration and knowledge sharing in organizations. We present a framework for managing organizational memory based on transactive memory, a mechanism of collective memory in small groups. While being effective in small groups, there are difficulties hindering the extension of transactive memory to larger groups. We claim that information technology can be used to help overcome these difficulties. We present a formal architecture for directories of meta-memories required in extended transactive memory systems and propose the use of meta-knowledge to substitute for the lack of tacit group knowledge that exists in small groups.

Evaluating modeling techniques based on models of learning

Database design commonly assumes, explicitly or implicitly, that instances must belong to classes. This can be termed the assumption of inherent classification. We argue that the extent and complexity of problems in schema integration, schema evolution, and interoperability are, to a large degree, consequences of inherent classification. Furthermore, we make the case that the assumption of inherent classification violates philosophical and cognitive guidelines on classification and is, therefore, inappropriate in view of the role of data modeling in representing knowledge about application domains. As an alternative, we propose a layered approach to modeling in which information about instances is separated from any particular classification. Two data modeling layers are proposed: (1) an instance model consisting of an instance base (i.e., information about instances and properties) and operations to populate, use, and maintain it; and (2) a class model consisting of a class base (i.e., information about classes defined in terms of properties) and operations to populate, use, and maintain it. The two-layered model provides class independence. This is analogous to the arguments of data independence offered by the relational model in comparison to hierarchical and network models. We show that a two-layered approach yields several advantages. In particular, schema integration is shown to be partially an artifact of inherent classification that can be greatly simplified in designing a database based on a layered model; schema evolution is supported without the complexity of operations currently required by class-based models; and the difficulties associated with interoperability among heterogeneous databases are reduced because there is no need to agree on the semantics of classes among independent databases. We conclude by considering the adequacy of a two-layered approach, outlining possible implementation strategies, and drawing attention to some practical considerations.