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Database Modeling with Object Role Modeling - Part 3

Modeling constraints commonly enforced in relational database systems

  • Database Modeling with ORM - Part 2
  • Object Role Modeling - Part 1: A Picture Is Worth a Thousand Words

    This article is the third in a series introducing Object Role Modeling (ORM). ORM is an excellent modeling methodology you can use to construct the conceptual database model. It is an approach to describing data in terms of objects and the roles they play.

    The focus of the first article (.NETDJ, Vol. 1, issue 10) was working with domain experts to identify the various object types and fact types that need to be included in the conceptual model. It introduced you to the various common types of predicate arities as well as the use of Visio to construct an ORM source model. The second article (.NETDJ, Vol. 1, issue 12) continued the study of ORM source modeling. The process of developing the database model is iterative and involves the continual refinement and verification of the various fact types and object types that make up the model. It introduced some of the concepts involved in the refinement process and focused on the concepts of primitive entity types, subtypes, derived fact types, and unique constraints.

    This article examines the process of modeling other types of constraints commonly enforced in relational database systems. It focuses on constraints such as external uniqueness, disjunctive role, and set constraints. In addition, it covers applying population checks to verify the conceptual database model.

    Uniqueness Constraints, Reference Schemes, and Mandatory Role Constraints
    Enforcing constraints is fundamental to designing the structure of a relational database. There are many different types of constraints to consider when defining the model. For example, Figure 1 shows a fact type with an arrow placed above the student role box in the predicate phrase. The arrow models a frequency constraint indicating that a student has at the most one SSN. Figure 1 also models a mandatory role constraint. The dot on the line connecting the student role to the predicate phrase indicates that each student must have a SSN. Figure 2 further constrains the roles in the fact type. It places similar constraints on the SSN role making it mandatory and unique. A student and SSN combination must be unique in the database. Since a student's SSN is unique, it can distinguish between different instances of the student entity. A unique constraint used to distinguish between identity types is the primary unique constraint (PUC) and modeled by tagging the constraint with a P. The PUC of an entity type is referred to as its reference mode, and it is often more convenient to indicate the reference mode by placing it in parenthesis within the entity role shape. Figure 3 demonstrates the two ways of modeling a reference mode.

    Up to this point, the constraints modeled have been internal to a particular fact type. When defining constraints, you must also consider external constraints that apply across a group of fact types. For example, students can have the same first name and/or the same last name but the combination of first name, middle name, and last name must be unique. To symbolize external uniqueness constraints in ORM, a dashed line connects the roles involved in the constraint to a circled letter U symbol (see Figure 4). There may be times when an external uniqueness constraint uniquely identifies instances of an entity type. This type of constraint is a compound reference scheme and a P replaces the U in the model (see Figure 5).

    In modeling external constraints sometimes it is necessary to model exclusive, or constraints. For example, it is necessary to be able to contact students by phone. Students must have either a home phone or a cell phone or both. In ORM terminology, this is a disjunctive mandatory role constraint (see Figure 6). Dashed lines connect the roles involved in the constraint to a circle symbol containing a solid dot.

    Constraint modeling in ORM is very robust and ORM can represent very intricate constraints as part of the conceptual model. Set constraints restrict the population of two roles and are primarily external in nature. They come in three flavors: exclusion, equality, and subset constraints. Exclusion constraints prevent instances of one set from appearing in another. The exclusion constraint symbol consists of a circled x with dashed lines connecting the roles involved in the constraint (see Figure 7). This constraint restricts the classifications of instructors to either full-time or part-time.

    An equality constraint ensures that all instances of one set appear in a related set. Figure 8 shows an equality constraint stipulating that full-time instructors get a salary. This is in contrast to part-time instructors who get a stipend. Notice that the symbol for this type of constraint is an equality symbol. A subset constraint ensures the instance of one set is also included in the instance of another. Figure 9 shows a subset constraint stipulating department chairs must also be full-time instructors. The symbol for the subset is a sideways underlined U. Notice that an arrow head has been added that points to the superset.

    Modeling External Constraints Using Visio
    The following activity demonstrates adding external constraints to a Visio ORM model. Note: You need Visio for Enterprise Architects in order to create an ORM Source Model.

    1.  Start up Visio. On the File menu, choose New > Database > ORM Source Model.

    2.  If the Business Rules Editor is not visible at the bottom of the window, on the Database menu, choose View > Business Rules.

    3.  Double-click the first Object Type cell in the Business Rules Editor. Create an Instructor entity object type with a reference mode of EmpNo. Repeat this procedure to add the rest of the object types shown in Figure 10.

    4.  Select the Fact Types tab at the bottom of the Business Rules Editor. Press the F2 key to launch the Fact Editor window. Using the left Object dropdown choose Instructor. In the relationship textbox enter "is a". In the right object drop down choose Full Time Emp. Repeat this procedure to add the rest of the fact types shown in Figure 11.

    5.  Click and drag the fact types "Instructor is a Part Time Emp" and "Instructor is a Full Time Emp" from the Business Rules Editor onto the drawing surface.

    6.  Select both fact types on the design surface by clicking on the predicate phrases while holding down the shift key. Right click on one of the predicate phrases and choose Add Constraints, which launches the Add Constraint dialog box.

    7.  Select exclusion from the constraint type dropdown and select the instructor role in each of the predicate phrases listed by clicking in the role box (see Figure 12). Click OK to close the Add Constraint dialog. The resulting diagram is similar to that in Figure 7.

    8.  Experiment with adding the various types of constraints. See if you can construct a diagram similar to that in Figure 13.

    Verifying the Conceptual Model using Verbalizations and Population Checks
    One of the most powerful features of ORM is the ability to verify the model with the domain experts using verbalizations and population checks. Verbalizations are natural language statements of the fact type and applied constraints that domain experts can easily understand and verify. Providing an ORM diagram such as Figure 14 to the domain experts for verification would not be very effective. Instead, a verbalization of the fact type is generated that can be easily understood and verified against the data artifacts that have been collected. Figure 15 contains the verbalization of the fact type of Figure 14. Using Visio, you can export the verbalizations of the various fact types contained in the model to a report. This report is very useful to the domain experts when they verify the model.

    Population checks are an extremely useful way to verify the accuracy of the constraints modeled. Sample populations represent data from a variety of sources such as reports and forms that currently exist to maintain and manage the data. In order to achieve a valid and useful population check, the sample data must be actual real world data and not fictitious data created to conform to the constraints of the model. Figure 16 shows a sample population entered for the fact type in Figure 14. Using the sample population, Visio can verify the constraints, report any errors and make recommendations for modifying the constraints. In order for a proper analysis, it is important that the sample population is large enough to be statistically significant.

    Analyzing Constraints Using Visio
    1.  Start up Visio. On the File menu, choose New > Database > ORM Source Model.

    2.  If the Business Rules Editor is not visible at the bottom of the window, on the Database menu, choose View > Business Rules.

    3.  Double-click the first Object Type cell in the Business Rules Editor. Create a class object type, room object type and a time value type.

    4.  Select the Fact Types, tab at the bottom of the Business Rules Editor. Press the F2 key to launch the Fact Editor window. Construct the ternary fact type using the settings shown in Figure17. Add the constraint settings using the constraint tab shown in Figure 18.

    5.  Select the Examples tab and enter a sample population as shown in Figure 16. Click the Analyze button and examine the results. Experiment with changing the sample population to see how it affects the resulting analysis.

    6.  Close the Fact Editor and drag the fact type to the design surface.

    7.  Under the Database menu, select Report. The New Report Wizard launches. Select the Fact Type Report, click Next, and then finish. Experiment with the different report setting options and preview the report.

    This article concludes this series on Object Role Modeling. ORM is an excellent aid to modeling the data and data relations that will comprise the backend database structure of your application. The real strength of ORM is its ability to extract the business data requirements from the domain experts into a conceptual model that is easily understood and verifiable by those same domain experts. By spending time creating a thorough and accurate conceptual model, the application's chance of success as it progresses to the logical and physical stages significantly increases.

    The goal of this series was to give you a sufficient introduction into the process of ORM modeling with Visio so that you are comfortable enough to start experimenting with the process. As you become more comfortable with ORM and Visio, you will gain an appreciation of its capabilities and benefits. Remember that the ORM model does not replace the traditional entity relationship model. ORM captures the conceptual requirements while the ER model describes the logical requirements. Once the ORM model is developed, you can use Visio to create and refine the logical and physical models of the relational database system. Although this series of articles did not cover the process of developing the logical and physical database models, it may be fodder for a future series. Let me know if this would be of interest to you.

  • More Stories By Dan Clark

    Dan is a Microsoft Certified Trainer, Microsoft Certified Solution Developer, and a Microsoft Certified Database Administrator. For the past seven years he has been developing applications and training others how to develop applications using Microsoft technologies. Dan has been developing and training Microsoft's .NET technologies since the early betas. He has recently authored the book "An Introduction to Object-Oriented Programming with Visual Basic .NET," published by Apress.

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