Conceptual Database Design
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1 Conceptual Database Design Fall 2009 Yunmook Nah Department of Electronics and Computer Engineering Dankook University
2 Conceptual Database Design Methodology Chapter 15, Connolly & Begg
3 Steps to Build Conceptual Data Model 1.1 identify entity types 1.2 identify relationship types 1.3 identify and associate attributes with entity or relationship types 1.4 determine attribute domains 1.5 determine candidate, primary, and alternate key attributes 1.6 consider use of enhanced modeling concepts (optional) 1.7 check model for redundancy 1.8 validate conceptual model against user transactions 1.9 review conceptual data model with user
4 Steps to Build Conceptual Data Model 1.1 identify entity types Examine the users requirements specification Identify nouns and noun phrases e.g., staff number, staff name, property number, property address, rent, number of rooms Look for major objects such as people, places, or concepts of interest, excluding those nouns that are merely qualities of other objects staff number, staff name -> entity Staff property number, property address, rent, number of rooms -> entity PropertyForRent
5 (an alternative way) Look for objects that have an existence in their own right Staff is an entity because staff exist whether or not we know their names, positions, and dates of birth Users frequently use synonyms and homonyms Synonyms: have the same meaning. branch and office Homonyms: the same word can have different meanings the word program : a course of study, a series of events, a plan of work, an item on the television Not always obvious whether a particular object is an entity, a relationship, or an attribute e.g., marriage
6 Design is subjective Relies on judgement and experience There may be no unique set of entity types deducible from a given requirements specification The entities from the Staff user views of DreamHome Staff, PropertyForRent, PrivateOwner, BusinessOwner, Client, Preference, Lease Document entity types Record the names, descriptions and synonyms (aliases) of entities in a data dictionary If possible, document the expected number of occurrences of each entity Entity name : Description : Aliases : Occurrence Staff: General term describing all staff employed by DreamHome: Employee: Each member of staff works at one particular branch (Figure 15.1)
7 1.2 identify relationship types Typically, relationships are indicated by verbs or verbal expressions Staff manages PropertyForRent PrivateOwner Owns PropertyForRent PropertyForRent AssociatedWith Lease In most instances, the relationships are binary Be careful to look out for complex relationships and recursive relationships Ensure that all the relationships that are either explicit or implicit in the users requirements spec are detected Check each pair of entity types for a potential relationships between them
8 Use ER diagrams (Figure 15.2) Determine the multiplicity constraints of relationship types Check for fan and chasm traps Document relationship types Entity name : multiplicity : relationship : multiplicity : entity name Staff (0..1) Manages (0..100) PropertyForRent (Figure 15.3)
9
10 Supplementary material Problems with ER Models (pp ) Connection traps occur due to a misinterpretation of the meanings of certain relationship Fan traps Chasm traps Fan traps: where a model represents a relationship between entity types, but the pathway between certain entity occurrences is ambiguous Exist where two or more 1:N relationships fan out from the same entity Division Operates one or more Branch (1:N); Division Has one or more Staff (1:N) => A problem arises when we want to know which members of staff work at a particular branch Division Operates Branch; Branch Has Staff
11 Supplementary material Chasm traps: where a model suggests the existence of a relationship between entity types, but the pathway does not exist between certain entity occurrences Occur where there are one or more relationships with a minimum multiplicity of zero (optional participation) forming part of pathway between related entities Branch (1..1) Has (1..*) Staff; Staff (0..1) Oversees (0..*) PropertyForRent => A problem arises when we want to know which properties are overseen by a member of staff Add Branch (1..1) Offers (1..*) PropertyForRent
12 1.3 identify and associate attributes with entity or relationship types Look for nouns or noun phrases in the users requirement specification The attributes can be identified where the noun or noun phrases is a property, quality, identifier, or characteristic of one these entities or relationships Simple/composite attributes The address attribute Single/multi-valued attributes Derived attributes Based on the values of other attributes The age of a member of staff
13 Potential problems Aware of cases where attributes appear to be associated with more than one entity or relationship type Several entities that can be represented as a single entity» Assistant & Supervisor -> Staff A relationship between entity types» PropertyForRent: propertyname,, managername» Staff Manages PropertyForRent
14 DreamHome attributes for entities Staff: staffno, name(composite: fname, lname), position, sex, DOB PropertyForRent: propertyno, address(composite: street, city, postcode), type, rooms, rent PrivateOwner: ownerno, name(composite: fname, lname), address, telno DreamHome attributes for relationships Views: viewdate, comment Document attributes Entity name: attributes: description : data type & length : Nulls : Multi-valued Staff: staffno: Uniquely identifies a member of staff: 5 variable characters: Nulls No: Multi-valued No : (Figure 15.4)
15 1.4 determine attribute domains Allowable set of values for the attribute Sizes and formats of the attribute staff numbers (staffno): A 5-character variable-length string, with the first two characters as letters and the next one to three characters as digits in the range 1-999, e.g., SG5, SG132 sex attribute: M or F. A single character.
16 1.5 determine candidate, primary, and alternate key attributes Guidelines to choose a primary key from among the candidate keys The CK with the minimal set of attributes The CK that is least like to have its values changed The CK with fewest characters (for those with textual attribute(s)) The CK with smallest maximum value (for those with numerical attribute(s)) The CK that is easiest to use from the users point of view DreamHome primary keys (Figure 15.5) Document primary and alternate keys
17
18 1.6 consider use of enhanced modeling concepts (optional) Specialization/generalization PrivateOwner & BusinessOwner -> a superclass Owner Staff -> a subclass Supervisor Figure 15.6 Aggregation Composition
19
20 1.7 check model for redundancy Re-examine 1:1 relationship Remove redundant relationships Figure 15.7 Consider time dimension Man, Woman, Child: FatherOf, MotherOf, MarriedTo (Figure 15.8)
21 1.8 validate conceptual model against user transactions (queries) Two possible approaches Describing transactions Check that all the information (entities, relationships, and their attributes) required by each transaction is provided by the model e.g., list the details of properties managed by a named member of staff at the branch Using transaction pathways Diagrammatically represent the pathway taken by each transaction directly on the ER diagram Figure 15.9
22
23 1.9 review conceptual data model with user Repeat this process until the user is prepared to sign off the model as being a true representation of the part of the enterprise that we are modeling
24 Enhance E-R and UML Modeling Chapter 4, Elmasri & Navathe
25 FIGURE 4.1 EER diagram notation to represent subclasses and specialization. Several specializations on the same entity type A single subclass only; No circle notation d
26 The completeness constraint Total Every entity in the superclass must be a member of at least one subclass in the specialization Double line Partial Single line Which allows an entity not to belong to any of the subclasses The disjointness constraint d: disjoint o: overlap
27 FIGURE 4.7 A specialization lattice with multiple inheritance for a UNIVERSITY database. d
28 FIGURE 4.9 An EER conceptual schema for a UNIVERSITY database.
29 FIGURE 4.10 A UML class diagram corresponding to the EER diagram in Figure 4.7, illustrating UML notation for specialization/ generalization.
30 FIGURE 4.11 Ternary relationship types. (a) The SUPPLY relationship. (b) Three binary relationships not equivalent to SUPPLY. (c) SUPPLY represented as a weak entity type.
31 Example Consider the ER schema, which stores information about interviews by job applicants to various companies Some interviews result in job offers, whereas others do not
32 FIGURE 4.14a,b Aggregation. (a) The relationship type INTERVIEW. (b) Including JOB_OFFER in a ternary relationship type (incorrect).
33 FIGURE 4.14c Aggregation. (c) Having the RESULTS_IN relationship participate in other relationships (generally not allowed in ER).
34 FIGURE 4.14d Aggregation. (d) Using aggregation and a composite (molecular) object (generally not allowed in ER).
35 FIGURE 4.14e Aggregation. (e) Correct representation in ER.
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