Chapter 2: The Entity-Relationship Model (E-R)

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Entity-relationship model (E-R model)
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A logical representation of the data for an organization or for a business area, using entities for categories of data and relationships for associations between entities
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Entity-relationship diagram(E-R diagram, ERD)
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graphical representation of an entity-relationship model
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Entity types
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Strong(box), Weak(double square outline), Associative (rounded corners)
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Attributes
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Identifier(one underline), Partial identifier(double underline), ENTITY NAME, optional, [derived brackets], Multivalued{curly brackets}, Composite(parenthesis)
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Relationship degrees
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– unary, binary, ternary
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Relationship cardinality
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mandatory one, mandatory many, optional one, optional many.
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data modeling
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is all about Documenting rules and policies of an organization that govern data, not universal
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Business rules and policies
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creating, updating and removing data
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Our job as a database analyst is to
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i. Identify and understand those rules that govern data ii. Represent those rules so that they can be unambiguously understood by information systems developers and users iii. Implement those rules in database technology
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Business rule
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a statement that defines or constrains some aspect of the business. It is intended to assert business structure or to control or influence the behavior of the business.
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Integrity constraint
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maintaining valid data values and relationships
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The Business Rules Paradigm
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1. Guide individual and aggregate behavior. 2. To end users so define and maintain their own rules 3. Enforcement of business rules can be automated; highly maintainable
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Scope of Business Rules
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Cannot be represented in common data modeling notation, but in a natural language. Some can be represented in the relational data model. Facilitate the surfacing of the rules and the transformation of ill-stated rules into ones that satisfy the desired characteristics. Business people become stewards of the business rules
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Good business rules
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1. Defined 2. Approved 3. Owned by business
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Gathering business rules
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Found in the description of business functions, events, policies, units, stakeholders, and other objects. Asking questions about the who, what, when, where, why, and how of the organization. may be vague or imprecise. Data Names and Definitions. Give entities, relationships, and attributes clear and distinct names and definitions
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Characteristics of a Good Business Rule
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1. Declarative- statement of policy 2. Precise- one interpretation 3. Atomic- marks one statement, no part of the rule can stand on its own as a rule 4. Consistent- cannot contain conflicting statements 5. Expressible- structured natural language 6. Distinct- not redundant 7. Business-oriented- owned by the business
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Data names should
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1. Relate to business, not technical (hardware or software), characteristics; 2. Be meaningful- “not words like has, is, person, or it”, 2. Be unique- home address vs. campus address 3. Be readable- not be difficult to understand 4. Be compose of words taken from an approved list- vocabulary, abbreviations are used in database technology limits 5. Be repeatable- student vs. employee Birthdates 6. Follow a standard syntax; does name already exist
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Data Definitions (structural assertion)
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i. A definition is an explanation of a term or fact ii. type of business rule iii. Term- a word or phrase that has a specific meaning for the business- course, section, rental car, flight, reservation, passenger. Key words used to form data names, careful and concisely, not like day, month, person or television. iv. Fact- as association between two or more terms- simple declarative statement that relates terms v. Examples of facts that are definitions are the following. Defined terms underlined
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Good Data Definitions
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1. Gathered from the same sources as all requirements for information systems 2. Accompanied by diagrams E-R, as supplement 3. Singular and explain what the data is, not what it’s not.
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Good Data Definitions- State such characteristics of a data object as
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a. Subtleties b. Special or exceptional conditions c. Examples i. Where, when, and how the data are created or calculated in the organization d. Static or change overtime e. Singular, plural, in its atomic form f. Who determines the value for the data g. Who owns the data h. Optional or whether empty( what we will call null) values are allowed i. Broken down into more atomic parts or are often combined with other data into some more composite or aggregate form i. If not included in a data definition , these characteristics need to be documented elsewhere, where other metadata are stored j. A data object should not be added to a data model, such as a ERD, until after it has been carefully defined 1. Modeling data is an iterative process 2. ” he who controls the meaning of data controls the data
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Series of stages in relationships whit a school
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i. Prospect- formal contact, interest ii. Applicant- applies for admission iii. Admitted applicant admitted to the school or degree program iv. Matriculated student- registers for at least one course v. Continuing student- ongoing basis no gaps vi. Former student- stipulated time period vii. Graduate degree program
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Three Alternatives to giving good data definitions
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1. Use multiple definitions to cover the various situations. 2. Use a very general definition that will cover most situations 3. Consider using multiple, related, data object for Student

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