1. www.sims.monash.edu.au Information description – data dictionary Introduction to data modelling IMS9300 IS/IM FUNDAMENTALS

2. www.sims.monash.edu.au/subjects/ims9300 2 Data Modelling Focus on the information aspects of the organisation In a business environment many tasks share the same data The database/ data warehouse is a common asset and corporate resource

3. www.sims.monash.edu.au/subjects/ims9300 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.sims.monash.edu.au/subjects/ims9300 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.sims.monash.edu.au/subjects/ims9300 5 Techniques; –Entity Relationship (ER) modelling –Data dictionary Good modelling techniques are supported by rigorous standards and conventions to remove ambiguity and aid understanding Conceptual Data Modelling

6. www.sims.monash.edu.au/subjects/ims9300 6 the data dictionary is a database or repository of information about objects identified during systems development every object (and each of its components) must have a definition in the data dictionary the data dictionary is a major source of documentation about the information system the data dictionary must contain precise definitions of all components of all data flow diagrams Data Dictionary

7. www.sims.monash.edu.au/subjects/ims9300 7 Data Dictionary -data element entry ACADEMIC CONSULTANCY DATA ELEMENT: Product code Alias: Inventory number, product number Description: Number to identify and differentiate each product held in warehouse Values: Must be a positive integer Range: 00001 to 99999 AUTHOR: David Ross DATE: 14 Oct 2002

8. www.sims.monash.edu.au/subjects/ims9300 8 Data Dictionary -data flow entry ACADEMIC CONSULTANCY DATA FLOW: Sales order Alias: Customer order Description: Request for product from customer Composition: sales order = sales order no. + sales order date+ customer number+ [account customer cash customer]+ customer name+ customer address+ (customer telephone no) + {item no + item desc + item price + item qty} + sales order total amount AUTHOR: David Ross DATE: 03 July 2002

9. www.sims.monash.edu.au/subjects/ims9300 9 Data Dictionary -data store entry ACADEMIC CONSULTANCY DATA STORE: Sales Description: Store of all pertinent data about product sales made to customers Composition: sales = date+ ({product} product quantity)+ sales total {customer} AUTHOR: David Ross DATE: 10 Oct 2002

10. www.sims.monash.edu.au/subjects/ims9300 10 Data Dictionary Entries a data dictionary entry must be included for each –data flow –data store –higher level process –primitive process –external agent

11. www.sims.monash.edu.au/subjects/ims9300 11 Detailed Data Modelling To expand the conceptual data model, we need to identify and describe the details of the entities and relationships Attributes (data elements) of entities and relationships are identified The attributes should be independent of implementation technology Each attribute should represent a single “fact” An organisation-wide perspective should be adopted to ensure minimum redundancy and inconsistency and to facilitate data sharing

12. www.sims.monash.edu.au/subjects/ims9300 12 Attributes An attribute is a named property or characteristic of an entity that is of interest to the organisation Use an initial capital letter followed by lower case letters in naming attributes Each attribute name must be unique

13. www.sims.monash.edu.au/subjects/ims9300 13 Identifying Attributes There are three main sources of attributes: data to support essential user functions – members_address; book_publisher data to support current operations – loan_commencement_date; amount_paid data to measure performance against objectives – purchase_date; loan_quantity

14. www.sims.monash.edu.au/subjects/ims9300 14 examine all forms, documents, reports and files (computerised and manual) used in the current system check with users for accurate definitions of attributes ensure all attributes identified are still required in the new system Identifying attributes

15. www.sims.monash.edu.au/subjects/ims9300 15 Documenting Attributes All attributes should be defined and described in the data dictionary Information should include: name description example values domain of values range of values synonyms method of derivation validation constraints entities which this attribute describes

16. www.sims.monash.edu.au/subjects/ims9300 16 Each attribute should convey a single fact avoid embedding extra information in ranges of values e.g.invoice-number 0000-1499north-east region 1500-2999south-east region 3000-4499central region Attribute definition

17. www.sims.monash.edu.au/subjects/ims9300 17 The relational database model The relational database model represents data in the form of tables or relations Important concepts are: –relation –primary key –foreign key –functional dependency

18. www.sims.monash.edu.au/subjects/ims9300 18 Example relation A relation generally corresponds to some real world object or concept of interest to the system (derived from an entity) e.g.: Emp#NameSalaryDept 1247 1982 9314 Adams Smith Jones 24000 27000 33000 Finance MIS Finance Employee Employee (Emp#, Name, Salary, Dept)

19. www.sims.monash.edu.au/subjects/ims9300 19 Primary key An attribute or group of attributes which uniquely identifies a row of a relation Entity integrity (relational data base theory) requires that each relation has a non-null primary key Where several possible keys are identified, they are known as candidate keys - choose one to be the primary key Employee (Emp#, Name, Salary, Dept) Order-item (Order#, Item#, Qty-ordered) E.g.

20. www.sims.monash.edu.au/subjects/ims9300 20 Well Structured Relations A well structured relation contains a minimum amount of redundancy and allows users to insert, modify, and delete rows in a table without errors or inconsistencies (known as “anomalies”) Three types of anomaly are possible: –insertion –deletion –modification Third normal form relations are considered to be well structured relations

21. www.sims.monash.edu.au/subjects/ims9300 21 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

22. www.sims.monash.edu.au/subjects/ims9300 22 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)

23. www.sims.monash.edu.au/subjects/ims9300 23 Entity Types and Instances An entity type is a classification of entity instances eg BN Holdings ABC Engineering Acme Corp. Ltd. SUPPLIER

24. www.sims.monash.edu.au/subjects/ims9300 24 Relationship Is an association between two classes of information – eg. customer and order We may wish to store additional information about the association itself – eg. volume of orders placed Often described in the form: entity + verb + entity eg. CUSTOMER places ORDER Separation of information into easily manageable collections (data tables) often necessitates mechanisms to link related collections of data

25. www.sims.monash.edu.au/subjects/ims9300 25 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

26. www.sims.monash.edu.au/subjects/ims9300 26 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

27. www.sims.monash.edu.au/subjects/ims9300 27 Examples of Cardinalities EMPLOYEECUSTOMERSUPPLIER PROJECT SALES ORDER ITEM leads placessupplies One to OneOne to ManyMany to Many

28. www.sims.monash.edu.au/subjects/ims9300 28 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

29. www.sims.monash.edu.au/subjects/ims9300 29 Nature of Relationships - Notations EMPLOYEE MUST have a related record May have a related record

30. www.sims.monash.edu.au/subjects/ims9300 30 Example ER model MEMBER FINE LOAN BOOK takes out makes is for

31. www.sims.monash.edu.au/subjects/ims9300 31 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

32. www.sims.monash.edu.au/subjects/ims9300 32 References 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 5