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Information Technology IMS 5024 Information Systems Modelling Data Modelling.

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Presentation on theme: "Information Technology IMS 5024 Information Systems Modelling Data Modelling."— Presentation transcript:

1 Information Technology IMS 5024 Information Systems Modelling Data Modelling

2 School of Information Management & Systems 7.2 Content Second assignment Pitfalls revisited Nature of data modelling Tools/Techniques used in data modelling Place in ISD Evaluation of data modelling Reading list NB. Not all slides in this lecture will be discussed

3 School of Information Management & Systems 7.3 Assignment two – OO modelling Due Monday 4th of October – week 11 Undertake in pairs from the same tute group. Individual submissions accepted Decide this week!

4 School of Information Management & Systems 7.4 Assignment Pitfalls Not starting early Not confirming your understanding of the case and the requirements with me and Clyde Not starting early Not integrating the separate elements of the models Not starting early Assuming that Clyde and I do not coordinate our marking

5 School of Information Management & Systems 7.5 Data modelling describes: Structure Meaning Relationship of data

6 School of Information Management & Systems 7.6 Data modelling help us to grasp: static data in the organisation fundamental building blocks of the system different view of data from that of process modelling

7 School of Information Management & Systems 7.7 Techniques used in Data Modelling Normalisation Data Dictionary Entity relationship diagrams What difference? Use all?

8 School of Information Management & Systems 7.8 Entity Entity – thing of interest to the business Identifying an entity is subjective Entities can be: Realeg product Abstracteg quota Event rememberedeg sale Role playedeg employee Employee

9 School of Information Management & Systems 7.9 Relationship Relationship Between entities Cardinality (eg. One to many, one to one ect.) Degree of relationship (Unary, Binary, Ternary) DepartmentEmployee

10 School of Information Management & Systems 7.10 Notation conventions There are several - but ER concepts are consistent Entity Cardinality and existential status Degree

11 School of Information Management & Systems 7.11 PATIENT HISTORY PATIENT PROJECT EMPLOYEE Mandatory cardinalities Optional and mandatory cardinalities Optional cardinalities Has Is assigned to Is married to Examples of Cardinalities PERSON

12 School of Information Management & Systems 7.12 Mandatory 1 cardinality Many cardinality (1,2 …m) Optional (0 or 1) cardinality Optional (0 or many) cardinality Relationship Cardinality Summary

13 School of Information Management & Systems 7.13 Also called a recursive relationship One to manyOne to one Unary Relationship Employee Is married to Person Manages

14 School of Information Management & Systems 7.14 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 Binary Relationship LeadsPlaces Supplies Supplied byPlaced by Lead by

15 School of Information Management & Systems 7.15 A ternary relationship is a relationship between instances of three entity types. PART supplies VENDORWAREHOUSE Ternary Relationship

16 School of Information Management & Systems 7.16 Attributes Individual components of information that characterise an Entity – its constituent data Types: derived, multi-valued, composite, simple

17 School of Information Management & Systems 7.17 Example of attributes EMPLOYEE Emp-no NameAddress Skill

18 School of Information Management & Systems 7.18 Normalisation the process of identifying the “natural” groupings of attributes remove redundancy and incompleteness a bottom up process relies on Set Theory – well researched

19 School of Information Management & Systems 7.19 Determining columns One fact per column Hidden data Derivable data Determining the key CAF CAF CAM CAF243175

20 School of Information Management & Systems 7.20 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 Steps in Basic Normalisation

21 School of Information Management & Systems 7.21 First normal form Step 1: Remove the repeating group Why are 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#)

22 School of Information Management & Systems 7.22 Second and Third normal forms Eliminate redundancy Determinates – one or more columns (attributes) which determine other column values

23 School of Information Management & Systems 7.23 Second and Third normal form procedure Identify any non-key determinates Establish a separate table for each determinate and the columns it determines Name the new tables Remove the determined columns from the original table

24 School of Information Management & Systems 7.24 Third normal form A table is in third normal form if the only determinant(s) of non-key columns is (are) the primary key determinant(s)

25 School of Information Management & Systems 7.25 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 Refer to Date, C.J. (1990) An Introduction to Database Systems, Volume 1. 5 th edn. Addison- Wesley Publishing Company, MA. pp

26 School of Information Management & Systems 7.26 Higher Normal forms Occur infrequently Most tables in 3 NF are 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

27 School of Information Management & Systems 7.27 Thinking in Data modelling Hard Vs Soft ?? Perspective Objective vs Subjective Nature of the organisation

28 School of Information Management & Systems 7.28 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

29 School of Information Management & Systems 7.29 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

30 School of Information Management & Systems 7.30 Disadvantages Does not encourage or support user participation Your view on the organisation – people or data The idea that the model is THE model Subjective view One-side to data Others??

31 School of Information Management & Systems 7.31 Advantages of Normalisation Rid the data of redundancy and other problems Very well researched Math basis for normalisation

32 School of Information Management & Systems 7.32 Disadvantages of normalisation Does not encourage or support user participation Your view on the organisation –people or data One-side to data Can be done mechanistically without thought Others??

33 School of Information Management & Systems 7.33 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.


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