1. IMS 5024 Lecture 4 Semester 2, 20021 IMS 5024 Data Modelling

2. IMS 5024 Lecture 4 Semester 2, 20022 Content Individual assignment Pitfall revisited Group assignment Class assignment Nature of data modelling Tools/Techniques used in data modelling Place in ISD Evaluation of data modelling Reading list

3. IMS 5024 Lecture 4 Semester 2, 20023 Individual assignment Date due: 29 August 2002 – Difference between social and technical –Show understanding of the subject matter –Questions e-mail Bahar directly

4. IMS 5024 Lecture 4 Semester 2, 20024 Teaching Assistant Bahar Jamshidi E-mail: bahar.jamshidi@infotech.monash.edu.au Queries about marks Other queries

5. IMS 5024 Lecture 4 Semester 2, 20025 Pitfalls Not starting early Reading, more reading and then some reading. Plagiarism !!!!!

6. IMS 5024 Lecture 4 Semester 2, 20026 Data modelling describe:

7. IMS 5024 Lecture 4 Semester 2, 20027 Data modelling describe: Structure Meaning Relationship Of Data

8. IMS 5024 Lecture 4 Semester 2, 20028 Data modelling help us to grasp: Static Data in the organisation Fundamental building block of the system Two perspectives (Process and Data)

9. IMS 5024 Lecture 4 Semester 2, 20029 Techniques used in Data modelling Entity relationship diagrams Normalisation Data Dictionary What difference? Use both?

10. IMS 5024 Lecture 4 Semester 2, 200210 Entity Entity – things of interest to the business Identification of an entity is subjective Entities can be: Realeg product Abstracteg Quota Event rememberedeg sale Role playedeg employee Employee

11. IMS 5024 Lecture 4 Semester 2, 200211 Relationship Relationship Between entities Cardinality (eg. One to many, one to one ect.) Degree of relationship (Unary, Binary, Ternary) DepartmentEmployee

12. IMS 5024 Lecture 4 Semester 2, 200212 Examples of Cardinalities PATIENT HISTORY PATIENT PROJECT EMPLOYEE PERSON Mandatory cardinalities Optional and mandatory cardinalities Optional cardinalities Has Is assigne d to Is married to

13. IMS 5024 Lecture 4 Semester 2, 200213 Relationship Cardinality Summary Mandatory 1 cardinality Many cardinality (1,2 …m) Optional (0 or 1) cardinality Optional (0 or many) cardinality

14. IMS 5024 Lecture 4 Semester 2, 200214 Unary Relationship Also called a recursive relationship PERSONEMPLOYEE One to manyOne to one Is married to Manages

15. IMS 5024 Lecture 4 Semester 2, 200215 Binary Relationship A binary relationship is a relationship between instances of two entity types. PROJECT EMPLOYEE SALES ORDER CUSTOMER ITEM SUPPLIER One to oneOne to manyMany to many Leads Places Supplie s

16. IMS 5024 Lecture 4 Semester 2, 200216 Ternary Relationship A ternary relationship is a relationship between instances of three entity types. PART supplies VENDORCUSTOMER

17. IMS 5024 Lecture 4 Semester 2, 200217 Attributes What we want to know about the entity or a relationship Types: – Derived, – multi-valued, –Composite, –Simple

18. IMS 5024 Lecture 4 Semester 2, 200218 Example of attributes EMPLOYEE Emp-no NameAddress Skill

19. IMS 5024 Lecture 4 Semester 2, 200219 Why normalisation? Remove redundancy and incompleteness Bottom up process Rely on Maths – well researched

20. IMS 5024 Lecture 4 Semester 2, 200220 Determining columns One fact per column Hidden data Derivable data Determining the key

21. IMS 5024 Lecture 4 Semester 2, 200221 Steps in Basic Normalisation Basic Normalisation is most often accomplished in three stages (these are the three basic normal forms) First Normal Form Second Normal Form Third Normal Form Unnormalised table Remove repeating groups Remove partial dependencies Remove transitive dependencies

22. IMS 5024 Lecture 4 Semester 2, 200222 First normal form Step 1: Remove the repeating group Why is repeating groups a problem? Determine the key for the new group. Order-Item (Order#, Customer#, (Item#, Desc, Qty)) Order-Item (Order#, Item#, Desc, Qty) Order (Order#, Customer#)

23. IMS 5024 Lecture 4 Semester 2, 200223 Second and Third normal forms Eliminate redundancy Determinates – one or more columns which determines other column values

24. IMS 5024 Lecture 4 Semester 2, 200224 Second and third normal form procedure Identify any determinates (other than the key) Establish a separate table for each determinate and the columns it determines Name the new tables Remove the determined columns from the original table

25. IMS 5024 Lecture 4 Semester 2, 200225 Third normal form A table is in third normal form if the only determinates of nonkey columns are candidate keys

26. IMS 5024 Lecture 4 Semester 2, 200226 Advanced normalisation A set of tables can be in 3NF and still not be fully normalised Further stages of normalisation are BCNF, 4NF, 5 NF and Domain key NF

27. IMS 5024 Lecture 4 Semester 2, 200227 Higher Normal forms Occur infrequently Most tables in 3 NF is already in BCNF, 4NF and 5 NF Data in 3NF but not in 5NF has –Redundancy –Insert/update/delete anomalies –Difficulty in storing facts independently

28. IMS 5024 Lecture 4 Semester 2, 200228 BC NF Example Branch-customer relationship (customer no, branch no, visitng frequency, date relationship established, salesperson no) BRANCH CUSTOMER RELATIONSHIP CUSTOMER BRANCH SALES- PERSON

29. IMS 5024 Lecture 4 Semester 2, 200229 Problem Salesperson branch no Overlapping and candidate keys Customer no and branch no & Customer no and salesperson Branch-customer relationship (customer no, salesperson no, branch no, visiting frequency, date relationship established) Customer salesperson relationship (customer no, salesperson no, visiting frequency, date relationship established) Salesperson (Salesperson No, branch no)

30. IMS 5024 Lecture 4 Semester 2, 200230 B-C NF Every determinant must be a candidate key (must have overlapping keys) BRANCH CUSTOMER RELATIONSHIP CUSTOMER BRANCH SALES- PERSON

31. IMS 5024 Lecture 4 Semester 2, 200231 Fourth and Fifth NF Apply to all-key tables, degree > 2 INSTRUMENT DEALER LOCATION

32. IMS 5024 Lecture 4 Semester 2, 200232 Problems are the result of multi-valued dependencies

33. IMS 5024 Lecture 4 Semester 2, 200233 Fifth NF Keep splitting tables until: –Any further splitting would lead to tables unable to be joined to produce the original table –The only splits left are trivial

34. IMS 5024 Lecture 4 Semester 2, 200234 Dealing authority problem

35. IMS 5024 Lecture 4 Semester 2, 200235 Many to many relationships resolved

36. IMS 5024 Lecture 4 Semester 2, 200236 Combined table

37. IMS 5024 Lecture 4 Semester 2, 200237 Combined table which cannot be split

38. IMS 5024 Lecture 4 Semester 2, 200238 Other normalisation issues Normalisation and redundancy –Overlapping classifications –Derivable data Selecting primary keys

39. IMS 5024 Lecture 4 Semester 2, 200239 Thinking in Data modelling Hard Vs Soft ?? Perspective –Objective vs Subjective –Nature of the organisation

40. IMS 5024 Lecture 4 Semester 2, 200240 Evaluation of Data modelling Problem orientedProduct oriented Concep- tual Structured analysis Entity relationship modelling Logical construction of systems Modern structured analysis Object oriented analysis Structured design Object oriented design Formal PSL/PSA JSD VDM Levels of abstraction Stepwise refinement Proof of correctness Data abstraction JSP Object oriented programming

41. IMS 5024 Lecture 4 Semester 2, 200241 Advantages of Data modelling Data model is not computer oriented (agree??) Model understandable by technologist and users Does not show bias UoD can vary (whole organisation or department) Readily transformable into other models Different data analysis techniques Data modelling is rule-based

42. IMS 5024 Lecture 4 Semester 2, 200242 Disadvantages Does not encourage/support user participation Your view on the organisation –people or data The idea that the model is THE model Subjective view One-side ito data Others??

43. IMS 5024 Lecture 4 Semester 2, 200243 Advantages of Normalisation Rid the data of redundancy and other problems Very well researched Math basis for normalisation

44. IMS 5024 Lecture 4 Semester 2, 200244 Disadvantages of normalisation Does not encourage/support user participation Your view on the organisation –people or data One-side ito data Can be done mechanistically without thought Others??

45. IMS 5024 Lecture 4 Semester 2, 200245 Process modelling view of ISD Development group Objectives Environment Object system Object system Change process Hirschheim et al see reading list

46. IMS 5024 Lecture 4 Semester 2, 200246 Reading for next week Johnstone, M.N., McDermid, D.C. (1999). Extending and validating the business rules diagram method. Proceedings of the 10 th Australian Conference on Information Systems. Chapter 2 of Curran, Keller, Ladd (1998). SAP R/3 the business blueprint: Understanding the business process reference model. Prentice Hall.