A Normal Form for Relational Databases That Is Based on Domains and Keys
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Relational Database Design Chapter 7
Chapter 7: Relational Database Design Chapter 7: Relational Database Design First Normal Form Pitfalls in Relational Database Design Functional Dependencies Decomposition Boyce-Codd Normal Form Third Normal Form Multivalued Dependencies and Fourth Normal Form Overall Database Design Process Database System Concepts 7.2 ©Silberschatz, Korth and Sudarshan 1 First Normal Form Domain is atomic if its elements are considered to be indivisible units + Examples of non-atomic domains: Set of names, composite attributes Identification numbers 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 and encourage redundant (repeated) storage of data + E.g. Set of accounts stored with each customer, and set of owners stored with each account + We assume all relations are in first normal form (revisit this in Chapter 9 on Object Relational Databases) Database System Concepts 7.3 ©Silberschatz, Korth and Sudarshan First Normal Form (Contd.) Atomicity is actually a property of how the elements of the domain are used. + E.g. Strings would normally be considered indivisible + Suppose that students are given roll numbers which are strings of the form CS0012 or EE1127 + If the first two characters are extracted to find the department, the domain of roll numbers is not atomic. + Doing so is a bad idea: leads to encoding of information in application program rather than in the database. Database System Concepts 7.4 ©Silberschatz, Korth and Sudarshan 2 Pitfalls in Relational Database Design Relational database design requires that we find a “good” collection of relation schemas. -
Normalization Exercises
DATABASE DESIGN: NORMALIZATION NOTE & EXERCISES (Up to 3NF) Tables that contain redundant data can suffer from update anomalies, which can introduce inconsistencies into a database. The rules associated with the most commonly used normal forms, namely first (1NF), second (2NF), and third (3NF). The identification of various types of update anomalies such as insertion, deletion, and modification anomalies can be found when tables that break the rules of 1NF, 2NF, and 3NF and they are likely to contain redundant data and suffer from update anomalies. Normalization is a technique for producing a set of tables with desirable properties that support the requirements of a user or company. Major aim of relational database design is to group columns into tables to minimize data redundancy and reduce file storage space required by base tables. Take a look at the following example: StdSSN StdCity StdClass OfferNo OffTerm OffYear EnrGrade CourseNo CrsDesc S1 SEATTLE JUN O1 FALL 2006 3.5 C1 DB S1 SEATTLE JUN O2 FALL 2006 3.3 C2 VB S2 BOTHELL JUN O3 SPRING 2007 3.1 C3 OO S2 BOTHELL JUN O2 FALL 2006 3.4 C2 VB The insertion anomaly: Occurs when extra data beyond the desired data must be added to the database. For example, to insert a course (CourseNo), it is necessary to know a student (StdSSN) and offering (OfferNo) because the combination of StdSSN and OfferNo is the primary key. Remember that a row cannot exist with NULL values for part of its primary key. The update anomaly: Occurs when it is necessary to change multiple rows to modify ONLY a single fact. -
Adequacy of Decompositions of Relational Databases*9+
JOURNAL OF COMPUTER AND SYSTEM SCIENCES 21, 368-379 (1980) Adequacy of Decompositions of Relational Databases*9+ DAVID MAIER State University of New York, Stony Brook, New York 11794 0. MENDELZON IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598 FEREIDOON SADRI Princeton University, Princeton, New Jersey 08540 AND JEFFREY D. ULLMAN Stanford University, Stanford, California 94305 Received August 6, 1979; revised July 16, 1980 We consider conditions that have appeared in the literature with the purpose of defining a “good” decomposition of a relation scheme. We show that these notions are equivalent in the case that all constraints in the database are functional dependencies. This result solves an open problem of Rissanen. However, for arbitrary constraints the notions are shown to differ. I. BASIC DEFINITIONS We assume the reader is familiar with the relational model of data as expounded by Codd [ 111, in which data are represented by tables called relations. Rows of the tables are tuples, and the columns are named by attributes. The notation used in this paper is that found in Ullman [23]. A frequent viewpoint is that the “real world” is modeled by a universal set of attributes R and constraints on the set of relations that can be the “current” relation for scheme R (see Beeri et al. [4], Aho et al. [ 11, and Beeri et al. [7]). The database scheme representing the real world is a collection of (nondisjoint) subsets of the * Work partially supported by NSF Grant MCS-76-15255. D. Maier and A. 0. Mendelzon were also supported by IBM fellowships while at Princeton University, and F. -
Aslmple GUIDE to FIVE NORMAL FORMS in RELATIONAL DATABASE THEORY
COMPUTING PRACTICES ASlMPLE GUIDE TO FIVE NORMAL FORMS IN RELATIONAL DATABASE THEORY W|LL|AM KErr International Business Machines Corporation 1. INTRODUCTION The normal forms defined in relational database theory represent guidelines for record design. The guidelines cor- responding to first through fifth normal forms are pre- sented, in terms that do not require an understanding of SUMMARY: The concepts behind relational theory. The design guidelines are meaningful the five principal normal forms even if a relational database system is not used. We pres- in relational database theory are ent the guidelines without referring to the concepts of the presented in simple terms. relational model in order to emphasize their generality and to make them easier to understand. Our presentation conveys an intuitive sense of the intended constraints on record design, although in its informality it may be impre- cise in some technical details. A comprehensive treatment of the subject is provided by Date [4]. The normalization rules are designed to prevent up- date anomalies and data inconsistencies. With respect to performance trade-offs, these guidelines are biased to- ward the assumption that all nonkey fields will be up- dated frequently. They tend to penalize retrieval, since Author's Present Address: data which may have been retrievable from one record in William Kent, International Business Machines an unnormalized design may have to be retrieved from Corporation, General several records in the normalized form. There is no obli- Products Division, Santa gation to fully normalize all records when actual perform- Teresa Laboratory, ance requirements are taken into account. San Jose, CA Permission to copy without fee all or part of this 2. -
Database Normalization
Outline Data Redundancy Normalization and Denormalization Normal Forms Database Management Systems Database Normalization Malay Bhattacharyya Assistant Professor Machine Intelligence Unit and Centre for Artificial Intelligence and Machine Learning Indian Statistical Institute, Kolkata February, 2020 Malay Bhattacharyya Database Management Systems Outline Data Redundancy Normalization and Denormalization Normal Forms 1 Data Redundancy 2 Normalization and Denormalization 3 Normal Forms First Normal Form Second Normal Form Third Normal Form Boyce-Codd Normal Form Elementary Key Normal Form Fourth Normal Form Fifth Normal Form Domain Key Normal Form Sixth Normal Form Malay Bhattacharyya Database Management Systems These issues can be addressed by decomposing the database { normalization forces this!!! Outline Data Redundancy Normalization and Denormalization Normal Forms Redundancy in databases Redundancy in a database denotes the repetition of stored data Redundancy might cause various anomalies and problems pertaining to storage requirements: Insertion anomalies: It may be impossible to store certain information without storing some other, unrelated information. Deletion anomalies: It may be impossible to delete certain information without losing some other, unrelated information. Update anomalies: If one copy of such repeated data is updated, all copies need to be updated to prevent inconsistency. Increasing storage requirements: The storage requirements may increase over time. Malay Bhattacharyya Database Management Systems Outline Data Redundancy Normalization and Denormalization Normal Forms Redundancy in databases Redundancy in a database denotes the repetition of stored data Redundancy might cause various anomalies and problems pertaining to storage requirements: Insertion anomalies: It may be impossible to store certain information without storing some other, unrelated information. Deletion anomalies: It may be impossible to delete certain information without losing some other, unrelated information. -
Normalization for Relational Databases
Chapter 10 Functional Dependencies and Normalization for Relational Databases Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Chapter Outline 1 Informal Design Guidelines for Relational Databases 1.1Semantics of the Relation Attributes 1.2 Redundant Information in Tuples and Update Anomalies 1.3 Null Values in Tuples 1.4 Spurious Tuples 2. Functional Dependencies (skip) Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide 10- 2 Chapter Outline 3. Normal Forms Based on Primary Keys 3.1 Normalization of Relations 3.2 Practical Use of Normal Forms 3.3 Definitions of Keys and Attributes Participating in Keys 3.4 First Normal Form 3.5 Second Normal Form 3.6 Third Normal Form 4. General Normal Form Definitions (For Multiple Keys) 5. BCNF (Boyce-Codd Normal Form) Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide 10- 3 Informal Design Guidelines for Relational Databases (2) We first discuss informal guidelines for good relational design Then we discuss formal concepts of functional dependencies and normal forms - 1NF (First Normal Form) - 2NF (Second Normal Form) - 3NF (Third Normal Form) - BCNF (Boyce-Codd Normal Form) Additional types of dependencies, further normal forms, relational design algorithms by synthesis are discussed in Chapter 11 Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide 10- 4 1 Informal Design Guidelines for Relational Databases (1) What is relational database design? The grouping of attributes to form "good" relation schemas Two levels of relation schemas The logical "user view" level The storage "base relation" level Design is concerned mainly with base relations What are the criteria for "good" base relations? Copyright © 2007 Ramez Elmasri and Shamkant B. -
Final Exam Review
Final Exam Review Winter 2006-2007 Lecture 26 Final Exam Overview • 3 hours, single sitting • Topics: – Database schema design – Entity-Relationship Model – Functional and multivalued dependencies – Normal forms – Also some SQL DDL and DML, of course • General format: – Final will give a functional specification for a database – Design the database; normalize it; write some queries – Maybe one or two functional dependency problems Entity-Relationship Model • Diagramming system for specifying database schemas – Can map an E-R diagram to the relational model • Entity-sets: “things” that can be uniquely represented – Can have a set of attributes; must have a primary key • Relationship-sets: associations between entity-sets – Can have descriptive attributes – Relationships uniquely identified by the participating entities! – Primary key of relationship depends on mapping cardinality of the relationship-set • Weak entity-sets – Don’t have a primary key; have a discriminator instead – Must be associated with a strong entity-set via an identifying relationship Entity-Relationship Model (2) • E-R model has extended features too • Generalization/specialization of entity-sets – Subclass entity-sets inherit attributes and relationships of superclass entity-sets • Aggregation – Relationships involving other relationships • Should know: – How to diagram each of these things – Kinds of constraints and how to diagram them – How to map each E-R concept to relational model, including rules for primary keys, candidate keys, etc. E-R Model Attributes • Attributes -
Normalization of Database Tables
Normalization Of Database Tables Mistakable and intravascular Slade never confect his hydrocarbons! Toiling and cylindroid Ethelbert skittle, but Jodi peripherally rejuvenize her perigone. Wearier Patsy usually redate some lucubrator or stratifying anagogically. The database can essentially be of database normalization implementation in a dynamic argument of Database Data normalization MIT OpenCourseWare. How still you structure a normlalized database you store receipt data? Draw data warehouse information will be familiar because it? Today, inventory is hardware key and database normalization. Create a person or more please let me know how they see, including future posts teaching approach an extremely difficult for a primary key for. Each invoice number is assigned a date of invoicing and a customer number. Transform the data into a format more suitable for analysis. Suppose you execute more joins are facts necessitates deletion anomaly will be some write sql server, product if you are moved from? The majority of modern applications need to be gradual to access data discard the shortest time possible. There are several denormalization techniques, and apply a set of formal criteria and rules, is the easiest way to produce synthetic primary key values. In a database performance have only be a candidate per master. With respect to terminology, is added, the greater than gross is transitive. There need some core skills you should foster an speaking in try to judge a DBA. Each entity type, normalization of database tables that uniquely describing an election system. Say that of contents. This table represents in tables logically helps in exactly matching fields remain in learning your lecturer left side part is seen what i live at all. -
A Simple Guide to Five Normal Forms in Relational Database Theory
A Simple Guide to Five Normal Forms in Relational Database Theory 1 INTRODUCTION 2 FIRST NORMAL FORM 3 SECOND AND THIRD NORMAL FORMS 3.1 Second Normal Form 3.2 Third Normal Form 3.3 Functional Dependencies 4 FOURTH AND FIFTH NORMAL FORMS 4.1 Fourth Normal Form 4.1.1 Independence 4.1.2 Multivalued Dependencies 4.2 Fifth Normal Form 5 UNAVOIDABLE REDUNDANCIES 6 INTER-RECORD REDUNDANCY 7 CONCLUSION 8 REFERENCES 1 INTRODUCTION The normal forms defined in relational database theory represent guidelines for record design. The guidelines corresponding to first through fifth normal forms are presented here, in terms that do not require an understanding of relational theory. The design guidelines are meaningful even if one is not using a relational database system. We present the guidelines without referring to the concepts of the relational model in order to emphasize their generality, and also to make them easier to understand. Our presentation conveys an intuitive sense of the intended constraints on record design, although in its informality it may be imprecise in some technical details. A comprehensive treatment of the subject is provided by Date [4]. The normalization rules are designed to prevent update anomalies and data inconsistencies. With respect to performance tradeoffs, these guidelines are biased toward the assumption that all non-key fields will be updated frequently. They tend to penalize retrieval, since data which may have been retrievable from one record in an unnormalized design may have to be retrieved from several records in the normalized form. There is no obligation to fully normalize all records when actual performance requirements are taken into account. -
Normalization Dr
Normalization Dr. Zhen Jiang Normalization is a process for determining what attributes go into what tables, in order to reduce the redundant copies (i.e., unnecessary duplicates). In order to understand this process, we need to know certain definitions: • Functional Dependency: Attribute B is functionally dependent on Attribute A, (or group of attributes A) if for each value of A there is only one possible value of B. We say A---> B (A decides B) or (B is functionally dependent on A). Notice this dependent relation is different from the one in real world, such as parent-children dependent relation, why? Consider the following tables – and list the functional dependencies Student(StNum,SocSecNum,StName,age) Emp(EmpNum,SocSecNum,Name,age,start-date,CurrSalary) Registration(StNum,CNum,grade) Dependency defines whether the data of the dependent can be retrieved via this relation. Rule 1: Dependency is defined on the behalf of information search and storage in database, not on importance or any other dependent relation in real life. Therefore, in this class, we have children à parent, not parent à children. • primary key : 1. A single attribute (or minimal group of attributes) that functionally determine all other attributes. 2. If more than one attribute meets this condition, they are considered as candidate keys, and one is chosen as the primary key Rule 2: A key contains a unique value for each row of data. • candidate keys for the Student table are _______________________ for the Emp table ________________________ for the registration table _______________________ 1 For the tables with more than one candidate key, what should be chosen for the primary key? Determining functional dependencies Consider the following table Emp(Enum,Ename,age,acctNum,AcctName), what are the functional dependencies? Can we ensure? In order to determine the functional dependencies we need to know about the “real world model” the database is based on. -
Functional Dependency and Normalization for Relational Databases
Functional Dependency and Normalization for Relational Databases Introduction: Relational database design ultimately produces a set of relations. The implicit goals of the design activity are: information preservation and minimum redundancy. Informal Design Guidelines for Relation Schemas Four informal guidelines that may be used as measures to determine the quality of relation schema design: Making sure that the semantics of the attributes is clear in the schema Reducing the redundant information in tuples Reducing the NULL values in tuples Disallowing the possibility of generating spurious tuples Imparting Clear Semantics to Attributes in Relations The semantics of a relation refers to its meaning resulting from the interpretation of attribute values in a tuple. The relational schema design should have a clear meaning. Guideline 1 1. Design a relation schema so that it is easy to explain. 2. Do not combine attributes from multiple entity types and relationship types into a single relation. Redundant Information in Tuples and Update Anomalies One goal of schema design is to minimize the storage space used by the base relations (and hence the corresponding files). Grouping attributes into relation schemas has a significant effect on storage space Storing natural joins of base relations leads to an additional problem referred to as update anomalies. These are: insertion anomalies, deletion anomalies, and modification anomalies. Insertion Anomalies happen: when insertion of a new tuple is not done properly and will therefore can make the database become inconsistent. When the insertion of a new tuple introduces a NULL value (for example a department in which no employee works as of yet). -
Advanced Database Systems Midterm Exam First 2009/2010 Student Name: ID: Part 1: Multiple-Choice Questions (17 Questions, 1 Point Each) 1
Jordan University of Science & Technology Computer Science Department CS 728: Advanced Database Systems Midterm Exam First 2009/2010 Student Name: ID: Part 1: Multiple-Choice Questions (17 questions, 1 point each) 1. Select the TRUE statement concerning normalization. a. Performance is increased. b. Data consistency is improved. c. Redundancy is increased. d. The number of tables is reduced. e. Functional dependencies increase. 2. A lack of normalization can lead to which one of the following problems: a. Deadlock b. Lost Updates c. Insertion problems d. Deferred updates e. Deletion of data 3. Each of the following is an argument which might be used to support the use of relations which are not fully normalized. Select the weakest argument. a. A fully normalized database may have too many tables b. Full normalization may compromise existing applications/systems c. Full normalization may make some queries too complicated d. A fully normalised database may result in tables which are too large e. A fully normalized database may perform too slowly 4. If a non-key attribute of a table can be null, that table automatically violates which normal form (choose the lowest one): NONE 1NF 2NF 3NF BCNF 4NF 5NF 5. If an attribute of a table can have multiple values, that table automatically violates which normal form (choose the lowest one): NONE 1NF 2NF 3NF BCNF 4NF 5NF 6. Given the following relation and dependences, state which normal form the relation is in. R(p,q,r,s,t) p,q -> r,s,t r,s -> p,q,t t -> s a.