Download presentation

Presentation is loading. Please wait.

1
**CM20145 Further DB Design – Normalization**

Dr Alwyn Barry Dr Joanna Bryson CM20145 Further DB Design – Normalization

2
**Last Time Database design is an ongoing, iterative process.**

Requirements come from data, user demands, design issues. Change occurs: Corporations & technologies grow. Programmers & users learn. Views / security. Lossless-join decomposition Now: Science for improving design.

3
**Design Process & Normalization**

We assume a schema R is given. R could have been generated when converting E-R diagram to a set of tables. R could have been a single relation containing all attributes that are of interest (called universal relation). Normalization breaks R into smaller relations. R could be the result of any ad hoc design of relations, which we then test & convert to normal form.

4
**Overview First Normal Form. Functional Dependencies.**

Second Normal Form. Third Normal Form. Boyce-Codd Normal Form. Fourth Normal Form. Fifth Normal Form. Domain Key / Normal Form. Design Process & Problems.

5
**BAD First Normal Form – 1NF**

You aren’t supposed to have more than one value per attribute of a tuple. All tuples have the same number of attributes. Necessary for a relational database. Name Office Office Hours Barry 2.23 1pm, 4pm Bryson L2.27 11am, 5pm BAD

6
**Getting Caught Out With 1NF**

A domain is atomic if its elements are considered to be indivisible units. Examples of non-atomic domains: Set-valued attributes, composite attributes. Identifiers like CS101 that can be broken up into parts. A relational schema R is in first normal form if the domains of all attributes of R are atomic. Non-atomic values: complicate storage, encourage redundancy, Depend on interpretation built into application programs.

7
Are You Atomic? Atomicity is not an intrinsic property of the elements of the domain. Atomicity is a property of how the elements of the domain are used. E.g. strings containing a possible delimiter (here: a space) cities = “Melbourne Sydney” (non-atomic: space separated list) surname = “Fortescue Smythe” (atomic: compound surname) E.g. strings encoding two separate fields bucs_login = cssjjb If the first two characters are extracted to find the department, the domain bucs_login is not atomic. Leads to encoding of information in application program rather than in the database.

8
**Second Normal Form (2NF)**

Violated when a nonkey column is a fact about part of the primary key. A column is not fully functionally dependent on the full primary key. CUSTOMER-CREDIT in this case: From Watson ORDER ITEMID CUSTOMERID QUANTITY CUSTOMER-CREDIT Desk JJB 25 OK Chair AMB 3 POOR BAD FIX ITEM *itemid … ORDER quantity … CUSTOMER *customerid customer-credit …

9
**Def: Functional Dependency**

Let R be a relation schema R and R The functional dependency (FD) holds on R (“ is FD on ”) iff for any legal relations r(R): whenever any two tuples t1 and t2 of r agree on the attributes they also agree on the attributes . i.e. (t1) = (t2) (t1) = (t2) Example: Consider r(A,B) with the following instance of r: A B does NOT hold, but B A does hold A: Initials B: Chore JJB Grading AMB Setting Tutorials Writing Unit Reviews

10
**Functional Dependencies: Uses**

Way to encode “business rules”. Specify constraints on the set of legal relations. We say that F holds on R if all legal relations on R satisfy the set of FDs F. Test relations to see if they are legal under a given set of FDs. If a relation r is legal under a set F of FDs, we say that r satisfies F.

11
**Functional Dependencies**

An FD is an assertion about a schema, not an instance. If we only consider an instance or a few instances, we can’t tell if an FD holds. Inspecting only a few bird species (e.g. crows, cardinals and canaries) we might conclude colour species. However, this would be a bad FD as we would find out if we found some ravens. Thus, identifying FDs is part of the data modelling process.

12
**Trivial Functional Dependencies**

An FD is trivial if it is satisfied by all instances of a relation E.g. customer-name, loan-number customer-name customer-name customer-name In general, is trivial if Permitting such FDs makes certain definitions and algorithms easier to state.

13
**Functional Dependency vs Key**

FDs can express the same constraints we could express using keys: Superkeys: K is a superkey for relation schema R if and only if K R Candidate keys: K is a candidate key for R if and only if K R, and there is no K’ K such that K’ R Of course, which candidate key becomes the primary key is arbitrary.

14
FDs <> Keys FDs can represent more information than keys can on their own. Consider the following Loan-info-schema: Loan-info-schema = (customer-name, loan-number, branch-name, amount). We expect these FDs to hold: loan-number amount loan-number branch-name We could try to express this by making loan-number the key, however the following FD does not hold: loan-number customer-name Incidentally, this isn’t a very good table! (¬2NF)

15
**FD Closure Given a set F of FDs, other FDs are logically implied.**

E.g. If A B and B C, we can infer that A C The set of all FDs implied by F is the closure of F, written F+ . Find F+ by applying Armstrong’s Axioms: if , then (reflexivity) if , then (augmentation) if , and , then (transitivity) Additional rules (derivable from Armstrong’s Axioms): If and holds, then holds (union) If holds, then holds and holds (decomposition) If holds and holds, then holds (pseudotransitivity)

16
**Bad Decomposition Example (From Last Time)**

A Non Lossless-Join Decomposition R = (A, B) R1 = (A), R2 = (B) A B A B A B 1 2 1 2 1 2 B(r) A(r) r A (r) ⋈ B (r) Thus, r is different to A (r) ⋈ B (r) So A,B is not a lossless-join decomposition of R.

17
**FDs & Lossless Decomposition**

All attributes of an original schema (R) must appear in the decomposition (R1, R2): R = R1 R2 Lossless-join decomposition. For all possible relations r on schema R r = R1 (r) ⋈ R2 (r) A decomposition of R into R1 and R2 is lossless-join if and only if at least one of the following dependencies is in F+: R1 R2 R1 R1 R2 R2

18
**Second Normal Form (2NF)**

Violated when a nonkey column is a fact about part of the primary key. A column is not fully functionally dependent on the full primary key. CUSTOMER-CREDIT in this case: From Watson ORDER ITEMID CUSTOMERID QUANTITY CUSTOMER-CREDIT Desk JJB 25 OK Chair AMB 3 POOR BAD FIX ITEM *itemid … ORDER quantity … CUSTOMER *customerid customer-credit …

19
**FIX BAD Third Normal Form (3NF)**

Violated when a nonkey column is a fact about another nonkey column. A column is not fully functionally dependent on the primary key. R is 3NF iff R is 2NF and has no transitive dependencies. EXCHANGE RATE violates this. FIX STOCK STOCK CODE NATION EXCHANGE RATE GOOG USA 0.67 NOK FIN 0.46 BAD

20
**BAD FIX Boyce-Codd (BCNF) Arises when a table:**

has multiple candidate keys, the candidate keys are composite, the candidate keys overlap. R is BCNF iff every determinant is a cand. key. E.g. Assume one consultant per problem per client, and one problem per consultant. If client-problem is the primary key, how do you add a new consultant? Like 3NF but now worry about all fields. BAD ADVISOR CLIENT PROBLEM CONSULTANT Alpha Marketing Gomez Production Raginiski FIX

21
**Design Goals & their discontents**

Goals for a relational database design: eliminate redundancies by decomposing relations, must be able to recover original data using lossless joins, prefer not to loose dependencies. BCNF: no redundancies, no guarantee of dependency preservation. 3NF: dependency preservation, but possible redundancies.

22
**Fourth normal form (4NF)**

A row should not contain two or more independent multivalued facts. 4NF iff BCNF & no non-trivial multi-valued dependencies. Multivalued dependency means the value of one attributed determines a set of values for another. BAD STUDENT STUDENTID SPORT SUBJECT … 50 Football English Music Tennis Botany Karate FIX

23
**Fifth normal form (5NF) 5NF iff a relation has no join dependency.**

The schemas R1, R2,.., Rn have a join dependency over R if they define a lossless-join decomposition over R. This is way too complicated, don’t worry about it.

24
Domain Key Normal Form Every constraint on the relation must be a logical consequence of the domain constraints and the key constraints that apply to the relation. Key: unique identifier. Constraint: rule governing attribute values. Domain: set of values of the same data type. No known algorithm gives DK/NF.

25
**E-R Model and Normalization**

When an E-R diagram is carefully designed, identifying all entities correctly, the tables generated should not need further normalization. However, in a real (imperfect) design there can be FDs from non-key attributes of an entity to other attributes of the entity. The keys identified in E-R diagrams might not be minimal - FDs can help us to identify minimal keys. FDs from non-key attributes of a relationship set are possible, but rare.

26
**Denormalization & Performance**

May want to use non-normalized schema for performance. E.g. displaying customer-name along with account-number and balance requires join of account with depositor. Alternative 1: Use denormalized relation containing attributes of account as well as depositor. faster lookup. extra space and extra execution time for updates. extra coding work for programmer and possibility of error in extra code. Alternative 2: use a materialized view defined as account ⋈ depositor as above, except less extra coding, errors.

27
**Limits of Normalization**

Examples of bad database design, not caught by normalization. Good: earnings(company-id, year, amount) Bad: earnings-2000, earnings-2001, earnings-2002, etc., on (company-id, earnings) all are BCNF, but querying across years difficult needs a new table each year company-year(company-id, earnings-2000,earnings-2001, earnings-2002) in BCNF, but querying across years difficult requires new attribute each year

28
**Summary 1 – Rules to Watch**

1NF: attributes not atomic. 2NF: non-key attribute FD on part of key. 3NF: one non-key attribute FD on another. Boyce-Codd NF: overlapping but otherwise independent candidate keys. 4NF: multiple, independent multi- valued attributes. 5NF: join dependency. Domain Key / NF: all constraints either domain or key

29
**Summary 2 – Concepts Functional Dependencies:**

Axioms & Closure. Lossless-join decomposition. Design Process. Normalization Problems. Next: Interfaces and Architectures

30
**Reading & Exercises Reading Exercises:**

Connolly & Begg Chapter (13, 14) Silberschatz Chapters 7. Any other book, the design/normalization chapter. Exercises: Silberschatz 7.1, 7.2, 7.16, 7.23, 7.24,

31
Next Week Architectures and Implementations Integrity and Security

32
Slides after and including this one you are not responsible for, but I am saving in case I decide to use them in the future.

33
**Goal: Formalize “Good Design”**

Process: Decide whether a particular relation R is in “good” form. In the case that a relation R is not in “good” form, decompose it into a set of relations {R1, R2, ..., Rn} such that: each relation is in good form, the decomposition is a lossless-join decomposition. Theory: Constraints on the set of legal relations. Require that the value for a certain set of attributes determines uniquely the value for another set of attributes – functional dependencies.

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google