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Improving the representation of roles in conceptual modeling: theory, method, and evidence

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Abstract

Conceptual models represent the Organizational domain for which an information system is developed. These models are important tools in defining the requirements for the system. When describing an Organization or part of it, a key concept is the notion of roles played by actors in the domain. Actors in an Organization act in various roles, hence, showing that roles in a conceptual model can promote understanding of how the Organization works. However, despite the importance of roles in understanding Organizations and their prevalence in various aspects of information systems development, no consensus exists on what roles are, or how to represent them in conceptual models. In this paper, we formally define role as a conceptual modeling construct based on literature analysis, ontological concepts, and principles of classification. Using this definition, we derive guidelines for representing roles in conceptual models and suggest rules for modeling roles with the widely used extended entity-relationship grammar. Finally, we test the effectiveness of the modeling rules by conducting an experimental study to compare the domain understanding of readers using two types of conceptual modeling scripts. One script was obtained by violating the rules and the other by not violating the rules. We obtained data on domain understanding (using problem-solving questions) and on the process of understanding (using eye tracking). The results indicate that the role-based rules are not only useful for understanding the models but also provide direct clues as to why this is so.

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Notes

  1. Note that this is not a criticism of ORM. ORM provides a comprehensive data modeling approach. The approach offered in this paper, in contrast, is focused specifically on modeling roles. Moreover, it is important to note that even if these role annotations in ER diagrams and ORM diagrams did reflect the true nature of a real-world role (which they do not), they are frequently omitted in practice (e.g., [8, 25]).

  2. Although we focus on EER, our proposed method could also be adapted quite easily to other approaches such as UML class diagrams when used for conceptual modeling.

  3. For example, one could add a label such as “is a part of” between two entity types (e.g., wood and building), but this would not reflect a role in our definition. Only some relationships, not all, reflect roles.

  4. While it might add credibility to our results to add additional cases, the results of a single case do show the differences we were examining. We return to this issue when discussing future research opportunities.

  5. As one of our reviewers noted, annotations could still be useful. For instance, annotations could be used to make guided scripts even clearer. Alternatively, annotations could be used to overcome the limitations of unguided scripts. The experiment reported here is just a first step. Future studies would be needed to test these different scenarios.

  6. We only make this prediction for users of the guided scripts. We cannot make a prediction for users of the unguided scripts because there is no clear connection between any specific area of the script and performance in the task.

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Correspondence to Palash Bera.

Appendices

Appendix 1: demonstrating the general applicability of the modeling rules

The purpose of this appendix is to provide an indication that the proposed rules can support a range of applications. In particular, we show how they can be used to model four cases in which roles can be salient and which could be, if a method is not available, difficult to model. We use examples from Elmasri and Navathe [43], represented on the left side of the figure. The right side of the figure shows the guided EER models that follow our rules. The annotations in the figure indicate how each rule in Table 3 is brought to bear. For example, the link (1, 2) in Fig. 10 shows that rules 1 and 2 are brought to bear in the identification of university and Person.

  • In Fig. 10, we see a Person who can be in one or more roles (alone or together)—a student and an Employee. To show each as a role, we need to show the linked base classes. Based on the context, these are the university and an Organization.

    Fig. 10
    figure 10

    Scenario 1: an object can play multiple roles (e.g., Person can be student and Employee)

  • In Fig. 11, we see a role that can have two base entity classes—a Personal customer and a corporate customer. To show each as a fulfilling the same role, we need to show the linked base class in this case—a bank. In this case, the Account_Holder can be a Person—inheriting all the Person attributes, or a company—inheriting its attributes.

    Fig. 11
    figure 11

    Scenario 2: possession of the same role by unrelated objects (e.g., an account can be possessed by a Person and a company)

  • In Fig. 12, we see a role that can be further specialized. A Person works for an Organization—being an Employee. Then, the Employee (a role) is used as a base class to derive the meaning of a faculty—a specialization of an Employee. In this case, a faculty is linked to a new base class—a university, which is a subclass of an Organization.

    Fig. 12
    figure 12

    Scenario 3: a role can have a sub-role (e.g., a faculty is a sub-role of Employee)

  • In Fig. 13, we see a Person who takes one or both of two roles—separately or together—a student and an Employee. People who are in both roles obtain a new role with the university—reduced tuition. This is shown by creating an additional level of specialization. The students who are Employees have a reduced tuition with the university.

    Fig. 13
    figure 13

    Scenario 4: an object can play different roles simultaneously (joint role) (e.g., a Person can be student and an Employee at the same time)

Overall, our examples show that that the proposed rules are quite flexible and can model each of these four scenarios.

Appendix 2: guided and unguided EER scripts in the experiment

See Figs. 14 and 15.

Fig. 14
figure 14

Unguided EER script

Fig. 15
figure 15

Guided EER script

Appendix 3: additional detailed results from eye tracking

See Tables 13 and 14.

Table 13 Eye movement analysis of interactions for problem-solving task 1
Table 14 Eye movement analysis of interactions for problem-solving task 2

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Bera, P., Burton-Jones, A. & Wand, Y. Improving the representation of roles in conceptual modeling: theory, method, and evidence. Requirements Eng 23, 465–491 (2018). https://doi.org/10.1007/s00766-017-0275-9

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