Stakeholder Experiences with Conceptual Modeling: An Empirical Investigation

During the design of an information system, a significant task that is sometimes undertaken is conceptual modeling. It involves designers building a representation called a conceptual schema that captures application domain features to be included in the information system. For five reasons, conceptual modeling has become increasingly important: (1) conceptual schemas help clarify different assumptions that stakeholders hold about the domain being modeled; (2) integrating conceptual schemas is critical to organizations effectively re-engineering their business processes; (3) the quality of conceptual schemas affects the quality of database schemas that can be generated automatically; (4) the quality of conceptual schemas affects the usability of databases; and (5) stakeholders working with distributed, heterogeneous databases cannot effectively transcend boundaries without high-quality conceptual schemas. While researchers have expended substantial effort on developing conceptual modeling methodologies, little empirical work has been done on stakeholder experiences with conceptual modeling. The meager results obtained suggest that organizations have found few benefits from conceptual modeling and that often it has fallen into disuse. Laboratory work indicates, however, that improved design outcomes occur when conceptual modeling is undertaken. For two reasons, we expect that stakeholders will experience problems with using conceptual modeling in practice. First, we believe that many designers approach conceptual modeling with a functionalist view of the world. We believe that a social relativist view more accurately describes how stakeholders conceive the world. Second, many conceptual modeling tools provide only incomplete representations of the application domain to be modeled. We are currently undertaking case-study research to document the conceptual modeling practices engaged in by a large public-sector organization. We are also seeking to identify the problems that stakeholders experi- ence when they participate in conceptual modeling exercises. Our goal is to provide a taxonomy of problems that the stakeholders face and ultimately to develop theory to account for why these problems occur

Ontological clarity and comprehension in health data models

Conceptual modeling forms an important part of systems analysis. If this is done incorrectly or incompletely, there can be serious implications for the resultant system, specifically in terms of rework and useability. One approach to improving the conceptual modelling process is to evaluate how well the model represents reality. Emergence of the Bunge-Wand-Weber (BWW) ontological model introduced a platform to classify and compare the grammar of conceptual modelling languages. This work applies the BWW theory to a real world example in the health arena. The general practice computing group data model was developed using the Barker Entity Relationship Modelling technique. We describe an experiment, grounded in ontological theory, which evaluates how well the GPCG data model is understood by domain experts. The results show that with the exception of the use of entities to represent events, the raw model is better understood by domain expert

A FOUNDATION FOR OPEN INFORMATION ENVIRONMENTS

Traditionally, information systems were developed within organizations for use by known audiences for known purposes. Advances in information technology have changed this landscape dramatically. The reach of information systems frequntly extends beyond organizational boundaries for use by unknown audiences and for purposes not originally anticipated. Individuals and informal communities can generate and use information in ways previously restricted to formal organizations. We term applications with these characteristics open information environments (OIEs). OIEs are marked by diversity of information available, flexibility in accommodating new sources, users and uses, and information management with minimal controls on structure, content, and access. This creates opportunities to generate new information and use it in unexpected ways. However, OIEs also come with challenges in managing the semantic diversity, flexibility of use, and information quality issus arising from the range of users and lack of controls. In this paper, we propose a set of principles for managing OIEs effectively. We outline a research program to examine the potential of OIEs, the challenges they present, and how to design OIEs to realize the benefits while mitigating the challenges. We highlight our ongoing research in this area, and conclude with a call for more research on this important phenomenon

Understanding Relationships with Attributes in Entity-Relationship Diagrams

Conceptual modeling is an important task undertaken during the systems development process to build a representation of those features of an application domain that are important to stakeholders. In spite of its importance, however, substantial evidence exists to show that it is not done well. Designers often provide incomplete, inaccurate, or inconsistent representations of domain features in the conceptual models they prepare. Users often have difficulty understanding the meaning inherent in a conceptual model. In this paper, we investigate the proposition that part of the difficulties that stakeholders experience with conceptual modeling arises when a conceptual modeling grammar or a representation produced using the grammar lacks ontological clarity. Lack of ontological clarity arises when a one-one mapping does not exist between conceptual modeling constructs and real-world constructs. For example, the grammatical construct of an entity is used to represent both things and events in the real world. Specifically, we focus on the grammatical construct of a relationship with attributes, which is often used in entity-relationship modeling. We argue that use of this construct produces ontologically unclear representations of a domain. We also report results from an experiment we undertook where we investigated the impact of using relationships with attributes in conceptual modeling representations on the problem-solving performance of users of these representations. Consistent with our predictions, we found that using relationships with attributes undermined problem-solving performance in unfamiliar domains. Contrary to our predictions, however, their use did not undermine problem-solving performance in familiar domains

Words and objects in information systems development: Six paradigms of information as representation

The notion of ‘information’ is one of the most basic in the Information Systems field. However, a clear consensus of what the term signifies remains evasive to both theorists and practitioners. Even in the applied discipline of Information Systems Development, the notion of information as representation is ambiguous. To motivate the discussion, we demonstrate a variety of contradictory stances held by several researchers in this domain. To make sense out of this perplexing variety, we develop a philosophical framework to highlight the divergence in philosophical assumptions. Our goal in this exercise is to delineate the ontological and epistemological bias of six exemplars of systems development techniques: software engineering, ontological engineering, ontological design, conceptual modeling, database normalization, and formal methods. A deeper understanding of the implicit philosophical premises can enlighten the choice of an appropriate method to address specific, concrete developmental challenges, as well as provide an understanding of the philosophical genesis of widely applied developmental tools

Representing Things and Properties in Conceptual Modelling: An Empirical Evaluation

The representation of things and properties is a fundamental issue in conceptual modelling. Important theoretical issues surrounding the representation of things and properties remain unresolved. For example, proponents of object-role modelling argue that there should be no distinction between things and properties, while proponents of entity-relationship modelling argue that the distinction is important but provide ambiguous guidelines about how the distinction should be made. In this paper, we use ontological theory to support our arguments about how things and properties should be represented. We describe an experiment that we undertook to test whether an ontologically sound representation of things and properties enabled users to better understand a domain than two other alternative, widely used representations. Our results provide evidence to support the use of ontologically sound representations of things and properties in conceptual modelling

Supporting Database Designers in Entity-Relationship Modeling: An Ontology- Based Approach

Database design has long been recognized as a difficult problem, requiring a great deal of skill on the part of the designer. Research has been carried out that provides methodologies and rules for creating good designs. There have even been attempts to automate the design process. However, before these can be truly successful, methodologies and tools are needed that can incorporate and use domain knowledge. In this research, a methodology for supporting database design is proposed that makes use of domain-specific knowledge about an application, which is stored in the form of ontologies. The ontologies provide information that is useful in both the creation of new designs and the verification of existing ones. They also capture the constraints of an application domain. A methodology for assisting database design that takes advantage of the ontologies has been implemented in a prototype system. Initial testing of the prototype illustrates that the incorporation and use of ontologies are effective in creating database design

Comparing the Understandability of Alternative Data Warehouse Schemas: An Empirical Study

An easily understood data warehouse model enables users to better identify and retrieve its data. It also makes it easier for users to suggest changes to its structure and content. Through an exploratory, empirical study, we compared the understandability of the star and traditional relational schemas. The results of our experiment contradict previous findings and show schema type did not lead to significant performance differences for a content identification task. Further, the relational schema actually led to slightly better results for a schema augmentation task. We discuss the implications of these findings for data warehouse design and future research