1. www.monash.edu.au Introduction to Data Modelling: Entity Relationship Modelling IMS1002 /CSE1205 Systems Analysis and Design

2. www.infotech.monash.edu.au 2 Data Modelling Focus on the information aspects of the organisation In a database environment many applications share the same data The database is a common asset and corporate resource Corporate and application level data modelling

3. www.infotech.monash.edu.au 3 Conceptual Data Modelling A conceptual data model is a representation of organisational data Captures the structure, meaning and interrelationships amongst the data Independent of any data storage and access method, DBMS, platform issues Occurs in parallel with other systems analysis activities

4. www.infotech.monash.edu.au 4 Identification of information requirements Allows integration of data across the organisation and across applications Helps eliminate problems of data inconsistency and duplication across the organisation Conceptual Data Modelling

5. www.infotech.monash.edu.au 5 Techniques; –Entity Relationship (ER) modelling –Normalisation –Data Structure Diagrams (DSD) Good modelling techniques are supported by rigorous standards and conventions to remove ambiguity and aid understanding Conceptual Data Modelling

6. www.infotech.monash.edu.au 6 Entity Relationship Modelling Used for conceptual data modelling Diagrammatic technique used to represent: – things of importance in an organisation - entities –the properties of those things - attributes –how they are related to each other - relationships

7. www.infotech.monash.edu.au 7 Entity relationship (ER) models can be readily transformed into a variety of technical architectures All information about the system’s data identified during conceptual data modelling must be entered into the data dictionary or repository This assists in checking the consistency of data and process models Entity Relationship Modelling

8. www.infotech.monash.edu.au 8 Data “objects” or entities are things about which we wish to store information ER models show the major data objects and the associations between them ER models are useful in the initiation, analysis and design phases Entity Relationship Modelling

9. www.infotech.monash.edu.au 9 Entity Something of interest about which we store information eg.EMPLOYEE SALES ORDER SUPPLIER Often identified from nouns used within the business application Should be LOGICAL (not physical)

10. www.infotech.monash.edu.au 10 Identifying Entities Entities are subjective (i.e. they reflect the viewpoint of the system) and can be: RealegVEHICLE AbstractegQUOTA Event rememberedegLOAN Role playedegCUSTOMER OrganisationegDEPARTMENT GeographicalegLOCATION

11. www.infotech.monash.edu.au 11 Representing Entities We represent an entity by a named rectangle Use a singular noun, or adjective + noun Refer to one instance in naming convention PART-TIME EMPLOYEE CUSTOMER

12. www.infotech.monash.edu.au 12 Entity Types and Instances An entity type is a classification of entity instances eg BN Holdings ABC Engineering Acme Corp. Ltd. SUPPLIER

13. www.infotech.monash.edu.au 13 Entity Types are Logical E.g. in a sales and inventory system there might be 3 physical forms of data: –a stock file –product brochures sent to customers enquiring about products –a product range book used by salespeople when calling on customers to take orders which could be represented by one logical entity PRODUCT

14. www.infotech.monash.edu.au 14 Entity Types are Logical E.g. in a Student Records System there might be an entity type STUDENT which represents some of the data used in several physical forms of data: >Student re-enrolment forms >Subject class lists >Student results file The ER model identifies the minimum set of data objects necessary to construct the data used within the system in its various physical forms.

15. www.infotech.monash.edu.au 15 Relationship Is an association between two entities We may wish to store information about the association Often recognised by a verb or "entity” + verb + “entity" eg CUSTOMER places ORDER Relationships capture the "business rules" of the system

16. www.infotech.monash.edu.au 16 Representing Relationships We represent a relationship as a line between two entities The relationship is named by a meaningful verb phrase which should indicate the meaning of the association Relationships are bi-directional so naming each end of the relationship conveys more meaning SUPPLIERITEM supplies Supplied by

17. www.infotech.monash.edu.au 17 Relationship Types and Instances A relationship type is a classification of relationship instances MIS Finance Marketing John Smith Bill Brown Sue Blackemploys DEPTEMPLOYEE employs

18. www.infotech.monash.edu.au 18 Cardinalities in Relationships The cardinality of a relationship is the number of instances of one entity type that may be associated with each instance of the other entity type eg a CUSTOMER may place many ORDERs an ORDER is placed by one CUSTOMER an ITEM can appear on many ORDERs

19. www.infotech.monash.edu.au 19 Examples of Cardinalities EMPLOYEECUSTOMERSUPPLIER PROJECT SALES ORDER ITEM leads placessupplies One to OneOne to ManyMany to Many led by placed by supplied by

20. www.infotech.monash.edu.au 20 Nature of Relationships We can indicate whether relationships are optional or mandatory: A customer MAY place many sales orders Each sales order MUST be placed by one customer CUSTOMER SALES ORDER places placed by

21. www.infotech.monash.edu.au 21 Notations EMPLOYEE COURSE attends EMPLOYEE COURSE attends Is attended by Notation used in Hoffer et al (1999)Notation used in Whitten et al (2001)

22. www.infotech.monash.edu.au 22 Notations EMPLOYEE or Exactly one One and only one Zero or one One or more Zero, one or more More than one

23. www.infotech.monash.edu.au 23 Relationship Degree The degree of a relationship is the number of entity types that participate in the relationship. The most common relationships in ER modelling in practice are: unary (degree one) binary (degree two) ternary (degree three)

24. www.infotech.monash.edu.au 24 Unary relationships A unary relationship is a relationship between instances of one entity type (also called a recursive relationship) EMPLOYEEITEM Has component manages Reports to Is a component of

25. www.infotech.monash.edu.au 25 Binary relationships A binary relationship is a relationship between instances of two entity types and is the most common type of relationship encountered in practice. MOVIE VIDEO TAPE has copy Is a copy of

26. www.infotech.monash.edu.au 26 Ternary relationships A ternary relationship is a simultaneous relationship between instances of three entity types. A ternary relationship is NOT the same as three binary relationships between the same three entity types.

27. www.infotech.monash.edu.au 27 Ternary relationships PROGRAMMER PROJECT LANGUAGE PROGRAMMER PROJECT LANGUAGE Triplets e.g. Mary uses COBOL on HR Project 3 independent sets of pairs e.g. Mary uses COBOL; Mary works on HR Project; COBOL is used in the HR Project

28. www.infotech.monash.edu.au 28 Example ER model CUSTOMER ITEM SALES ORDER EMPLOYEE employs made by places is on employed by makes placed by is for DEPARTMENT

29. www.infotech.monash.edu.au 29 Associative Entities (Gerunds) An associative entity (or gerund) is a relationship that a data modeller decides to model as an entity type As both entities and relationships can have attributes, this is possible PRODUCT CUSTOMER Is ordered by orders PRODUCT CUSTOMER SALES ORDER makes Is made by has Is on

30. www.infotech.monash.edu.au 30 Multiple Relationships It is common to have two or more relationships between the same entities. They represent different business rules. EMPLOYEE PROJECT Is working on Is eligible for Has working Has eligible

31. www.infotech.monash.edu.au 31 Modelling Time-dependent Data Some data values vary over time and it may be important to store a history of data values to understand trends and for forecasting. E.g. for accounting purposes we are likely to need a history of costs of material and labour costs and the time period over which each cost was in effect. Modelling time-dependent data can result in changes to entities, attributes and relationships.

32. www.infotech.monash.edu.au 32 One technique is to store a series of time stamped data values. These values can either be represented as repeating data or as an additional entity called PRICE HISTORY. Modelling Time-dependent Data PRODUCT PRICE HISTORY has belongs to PRODUCT Price Effective date

33. www.infotech.monash.edu.au 33 Modelling Time-dependent Data Relationship cardinality can change. DEPTEMPLOYEE Works for DEPT EMPLOYEE Has worked for Has working Had working

34. www.infotech.monash.edu.au 34 Entity subtypes and supertypes Some entities can be generalised (or specialised) to form other entities An entity subtype is made up from some of the instances of the entity E.g. the entity types motor car truck train can be grouped together to form the entity supertype transport vehicle

35. www.infotech.monash.edu.au 35 Entity Subtypes Entity subtypes are included in the ER model only when they are of use - they may participate in relationships and have additional attributes SALESPERSON EMPLOYEE DEPARTMENT CUSTOMER employs services employed served by

36. www.infotech.monash.edu.au 36 Multiple entity subtypes EMPLOYEE PART-TIME FULL-TIME PROPERTY RESIDENTIAL METROPOLITAN COUNTRY COMMERCIAL Entity subtypes may be nestedEntity types may have multiple subtypes

37. www.infotech.monash.edu.au 37 Entity Subtypes Multiple entity subtypes should be Non-overlapping (disjoint) Collectively exhaustive This enables easier translation to a relational design EMPLOYEE PART-TIME SALARIED RESIDENTIAL METROPOLITAN COMMERCIAL PROPERTY ? ?

38. www.infotech.monash.edu.au 38 Building a Basic ER Model Identify and list the major entities in the system Represent the entities by named rectangles Identify, draw, name, and quantify relationships Indicate mandatory/optional nature of relationships Revise for entity subtypes where appropriate

39. www.infotech.monash.edu.au 39 Example ER model Airline ticketing model FLIGHT TICKET PASSENGER AIRLINE ROUTE AIRLINE AIRPORT COUPON for made up of scheduled as arrival departure operated by shown on See Barker (1989), chap 2.4

40. www.infotech.monash.edu.au 40 Eliciting Information for an ER Model Fact-finding and information gathering techniques are used to determine the entities and relationships Identify both existing and new information Existing documents are particularly useful e.g. forms, paper-based and computer files, reports, listings, data manuals, data dictionary Existing and new business rules for information are often difficult to elicit from documents... it is essential to speak directly to the client

41. www.infotech.monash.edu.au 41 ER Modelling Difficulties Is a given object an entity or relationship ? Are two similar objects one entity or two ? Is a given object an entity or an attribute of (data item about) an entity? e.g. EMPLOYEE and EMPLOYEE SPOUSE Do we need to store data about the object? What is the 'best' data model ?

42. www.infotech.monash.edu.au 42 Quality dimensions Correctness Completeness Understandability Simplicity Flexibility

43. www.infotech.monash.edu.au 43 ER models and DFDs Do not to confuse entities with sources/sinks or relationships with data flows TREASURER is the person entering data; there is only one person and hence it is not an entity type ACCOUNT has many account balance instances EXPENSE has many expense transactions EXPENSE REPORT contents are already in ACCOUNT and EXPENSE - it is not an entity type EXPENSE REPORT ACCOUNTTREASUREREXPENSE

44. www.infotech.monash.edu.au 44 Integration of ER Models with DFDs All data elements represented in data flow diagrams for a system (data flows and data stores) MUST correspond to entities and their attributes in the ER model ORDERCUSTOMER placed by ORDER LINE PRODUCT for made up of 2 Check sales order 3 Produce weekly sales totals Sales orders

45. www.infotech.monash.edu.au 45 Barker, R. (1989) CASE*METHOD Entity Relationship Modelling, Addison-Wesley, Wokingham UK. Chapters 4,5 Hoffer, J.A., George, J.F. and Valacich, J.S., (1999)., Modern Systems Analysis and Design, (2nd ed), Benjamin/Cummings, Massachusetts. Chapter 10 Whitten, J.L. & Bentley, L.D. and Dittman, K.C., (2001), Systems Analysis and Design Methods, (5th edn.), McGraw Hill Irwin, Boston MA USA. Chapter 7 References