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1 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Normalisation Introduction.

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Presentation on theme: "1 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Normalisation Introduction."— Presentation transcript:

1 1 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Normalisation Introduction

2 2 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Outline motivation: database design – validation redundancy / update anomalies basis: functional dependencies (FDs) definitions examples concepts and terminology semantic assumtpions (more) advanced theoretical issues (in brief) normal form: illustration definition example

3 3 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Database Design relational model how do we know whether a relational model is good or not? how do we know whether a relation is well designed or not? normal forms a (semi-)formal way of validating a relational model, from the point of view of reducing the redundancy of data

4 4 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Redundancy Student-Modules

5 5 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Redundancy Student-Modules

6 6 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Redundancy a relation contains redundant data if it stores the same information more than once a relational model may have redundancy and at the same time have no redundant relations how? give an example redundant data may cause update anomalies and may lead to inconsistencies normalisation deals with redundant data at the level of individual relations

7 7 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Update anomalies - insertion insert the fact that takes Networks - Introduction; the name of the student and the name of the personal tutor have to be entered as well; this is prone to errors inconsistent data the structure of the relation does not prevent such errors from happening can you identify other kinds of update anomalies on this relation?

8 8 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Update anomalies - deletion delete the fact that takes HCI, in the original table; relevant information will be also deleted - about T.A Flo and about HCI the structure of the relation does not prevent such errors from happening

9 9 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Update anomalies - modification it is possible to modify an attribute and to bring the relation in an inconsistent state; e.g. it is possible (e.g. by mistake) to modify the value of Database Systems to 1/2cu in just some rows; such situations must be avoided the structure of the relation does not prevent such errors from happening

10 10 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Update anomalies update anomalies may lead to inconsistent data are caused by redundancy normal forms are a measure of the amount of redundancy in a relation are defined on the basis of a simpler concept: functional dependencies normalisation a way of transforming relations to eliminate redundancies no data should be lost/changed through normalisation

11 11 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Functional dependency (FD) R - relation, X and Y - subsets of attributes of R X Y iff in every possible legal value of R each X-value has a single Y-value associated

12 12 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Examples S_id S_name S_id P_Tutor S_id (S_id, S_name) P_tutor (S_id, S_name, P_tutor) P_tutor Module Val (S_id, Module) Res (S_id, S_name, P_tutor, Module, Val) Res (S_id, S_name, P_tutor, Module, Val, Res)

13 13 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Concepts FD is a semantic concept you must understand the meaning of the attributes determinant / dependent trivial / non-trivial left-irreducible yes: (S_id, S_name) P_tutor no: (S_id, Module) Res closure irreducible set

14 14 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Semantic assumptions FDs are deduced from the semantic assumptions (that define the application) (patient, symptom, doctor, practice, diagnosis) a patient is seen only by one doctor patient doctor a patient, for a given symptom, is seen by only one doctor patient, symptom doctor a doctor gives only one diagnosis for a symptom of one patient patient, symptom, doctor diagnosis

15 15 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Operations with FDs inference rules augmentation: if A B then AC BC transitivity : if A B and B C then A C decomposition:if A BC then A B and A C union:if A B and A C then A BC composition: if A B and C D then AC BD

16 16 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Functional diagram S_id City P_tutor S_name S_id Module Res Module

17 17 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College FDs and Keys define a candidate key (CK) in terms of FDs how is a FD expressed in a relation?

18 18 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Closure all FDs that can be derived from a given set S notation S+ Armstrongs inference rules for a partial set refer to slide Operations with FDs

19 19 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Irreducible set S 1 covers S 2 iff S 2 + S 1 + S is irreducible iff RightHandSide of every FD is non-composite all FDs in S are left-irreducible no FD ca be discarded from S without changing S + a database that enforces S enforces, in fact, S + the irreducible set of S is S iff S - irreducible S + = S + more efficient to work with the irreducible set

20 20 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College 1NF – First Normal Form not based on FDs a relation is in 1NF if and only if all the domains of its attributes contain only scalar values the relational model can only contain relations in 1NF

21 21 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College 2NF – Second Normal Form a relation (with just one CK) is in 2NF if and only if it is in 1NF and there is no FD from a subset of attributes of the PK to a non-key attribute

22 22 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College 2NF – Examples not 2NF (S_id, S_name, S_add, M_id, M_name, M_type, M_val, Result) why? 2NF (S_id, S_name, S_add) (M_id, M_name, M_type, M_val) (S_id, M_id, Result)

23 23 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College 3NF – Third Normal Form a relation (with just one CK) is in 3NF if and only if it is in 2NF and there is no FD between non-key attributes

24 24 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College 3NF - Examples not 3NF (M_id, M_name, M_type, M_val) why? 3NF (M_id, M_name, M_type) (M_type, M_val)

25 25 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Normalisation the process of transforming a relation with redundancies into an equivalent set of relations that have less redundancies equivalent – non-loss decomposition

26 26 Term 2, 2004, Lecture 2, Normalisation - IntroductionMarian Ursu, Department of Computing, Goldsmiths College Conclusion redundancy update anomalies normal forms – solution functional dependencies normal forms – simple definitions and examples


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