SEMESTER-II Functional Dependencies
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Functional Dependencies
Basics of FDs Manipulating FDs Closures and Keys Minimal Bases Functional Dependencies T. M. Murali October 19, 21 2009 T. M. Murali October 19, 21 2009 CS 4604: Functional Dependencies Students(Id, Name) Advisors(Id, Name) Advises(StudentId, AdvisorId) Favourite(StudentId, AdvisorId) I Suppose we perversely decide to convert Students, Advises, and Favourite into one relation. Students(Id, Name, AdvisorId, AdvisorName, FavouriteAdvisorId) Basics of FDs Manipulating FDs Closures and Keys Minimal Bases Example Name Id Name Id Students Advises Advisors Favourite I Convert to relations: T. M. Murali October 19, 21 2009 CS 4604: Functional Dependencies I Suppose we perversely decide to convert Students, Advises, and Favourite into one relation. Students(Id, Name, AdvisorId, AdvisorName, FavouriteAdvisorId) Basics of FDs Manipulating FDs Closures and Keys Minimal Bases Example Name Id Name Id Students Advises Advisors Favourite I Convert to relations: Students(Id, Name) Advisors(Id, Name) Advises(StudentId, AdvisorId) Favourite(StudentId, AdvisorId) T. M. Murali October 19, 21 2009 CS 4604: Functional Dependencies Basics of FDs Manipulating FDs Closures and Keys Minimal Bases Example Name Id Name Id Students Advises Advisors Favourite I Convert to relations: Students(Id, Name) Advisors(Id, Name) Advises(StudentId, AdvisorId) Favourite(StudentId, AdvisorId) I Suppose we perversely decide to convert Students, Advises, and Favourite into one relation. Students(Id, Name, AdvisorId, AdvisorName, FavouriteAdvisorId) T. M. Murali October 19, 21 2009 CS 4604: Functional Dependencies Name and FavouriteAdvisorId. Id ! Name Id ! FavouriteAdvisorId AdvisorId ! AdvisorName I Can we say Id ! AdvisorId? NO! Id is not a key for the Students relation. I What is the key for the Students? fId, AdvisorIdg. -
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. -
Fundamentals of Database Systems [Normalization – II]
Outline First Normal Form Second Normal Form Third Normal Form Boyce-Codd Normal Form Fundamentals of Database Systems [Normalization { II] Malay Bhattacharyya Assistant Professor Machine Intelligence Unit Indian Statistical Institute, Kolkata October, 2019 Outline First Normal Form Second Normal Form Third Normal Form Boyce-Codd Normal Form 1 First Normal Form 2 Second Normal Form 3 Third Normal Form 4 Boyce-Codd Normal Form Outline First Normal Form Second Normal Form Third Normal Form Boyce-Codd Normal Form First normal form The domain (or value set) of an attribute defines the set of values it might contain. A domain is atomic if elements of the domain are considered to be indivisible units. Company Make Company Make Maruti WagonR, Ertiga Maruti WagonR, Ertiga Honda City Honda City Tesla RAV4 Tesla, Toyota RAV4 Toyota RAV4 BMW X1 BMW X1 Only Company has atomic domain None of the attributes have atomic domains Outline First Normal Form Second Normal Form Third Normal Form Boyce-Codd Normal Form First normal form Definition (First normal form (1NF)) A relational schema R is in 1NF iff the domains of all attributes in R are atomic. The advantages of 1NF are as follows: It eliminates redundancy It eliminates repeating groups. Note: In practice, 1NF includes a few more practical constraints like each attribute must be unique, no tuples are duplicated, and no columns are duplicated. Outline First Normal Form Second Normal Form Third Normal Form Boyce-Codd Normal Form First normal form The following relation is not in 1NF because the attribute Model is not atomic. Company Country Make Model Distributor Maruti India WagonR LXI, VXI Carwala Maruti India WagonR LXI Bhalla Maruti India Ertiga VXI Bhalla Honda Japan City SV Bhalla Tesla USA RAV4 EV CarTrade Toyota Japan RAV4 EV CarTrade BMW Germany X1 Expedition CarTrade We can convert this relation into 1NF in two ways!!! Outline First Normal Form Second Normal Form Third Normal Form Boyce-Codd Normal Form First normal form Approach 1: Break the tuples containing non-atomic values into multiple tuples. -
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. -
A Hybrid Approach to Functional Dependency Discovery
A Hybrid Approach to Functional Dependency Discovery Thorsten Papenbrock Felix Naumann Hasso Plattner Institute (HPI) Hasso Plattner Institute (HPI) 14482 Potsdam, Germany 14482 Potsdam, Germany [email protected] [email protected] ABSTRACT concrete metadata. One reason is that they depend not only Functional dependencies are structural metadata that can on a schema but also on a concrete relational instance. For be used for schema normalization, data integration, data example, child ! teacher might hold for kindergarten chil- cleansing, and many other data management tasks. Despite dren, but it does not hold for high-school children. Conse- their importance, the functional dependencies of a specific quently, functional dependencies also change over time when dataset are usually unknown and almost impossible to dis- data is extended, altered, or merged with other datasets. cover manually. For this reason, database research has pro- Therefore, discovery algorithms are needed that reveal all posed various algorithms for functional dependency discov- functional dependencies of a given dataset. ery. None, however, are able to process datasets of typical Due to the importance of functional dependencies, many real-world size, e.g., datasets with more than 50 attributes discovery algorithms have already been proposed. Unfor- and a million records. tunately, none of them is able to process datasets of real- world size, i.e., datasets with more than 50 columns and We present a hybrid discovery algorithm called HyFD, which combines fast approximation techniques with efficient a million rows, as a recent study showed [20]. Because validation techniques in order to find all minimal functional the need for normalization, cleansing, and query optimiza- dependencies in a given dataset. -
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. -
The Theory of Functional and Template Dependencs[Es*
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Theoretical Computer Science 17 (1982) 317-331 317 North-Holland Publishing Company THE THEORY OF FUNCTIONAL AND TEMPLATE DEPENDENCS[ES* Fereidoon SADRI** Department of Computer Science, Princeton University, Princeton, NJ 08540, U.S.A. Jeffrey D. ULLMAN*** Department of ComprrtzrScience, Stanfoc:;lUniversity, Stanford, CA 94305, U.S.A. Communicated by M. Nivat Received June 1980 Revised December 1980 Abstract. Template dependencies were introduced by Sadri and Ullman [ 171 to generuize existing forms of data dependencies. It was hoped that by studyin;g a 4arge and natural class of dependen- cies, we could solve the inference problem for these dependencies, while that problem ‘was elusive for restricted subsets of the template dependencjes, such as embedded multivalued dependencies. At about the same time, other generalizations of known clependency forms were developed, such as the implicational dependencies of Fagin [l 1j and the algebraic dependencies of Vannakakis and Papadimitriou [20]. Unlike the template dependencies, the latter forms include the functional dependencies as special cases. In this paper we show that no nontrivial functional dependency follows from template dependencies, and we characterize those template dependencies that follow from functional dependencies. We then give a complete set of axioms for reasoning about combinations of functional and template dependencies. As a result, template dependencies augmented1 by functional dependencies can serve as a sub!;tltute for the more genera1 implicational or algebraic dependenc:ies, providing the same ability to rl:present those dependencies that appear ‘in nature’,, while providing a somewhat simpler notation and set of axioms than the more genera4 classes. -
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. -
Chapter 7+8+9
7+8+9: Functional Dependencies and Normalization how “good” how do we know the is our data database design won’t model cause any problems? design? what do we grouping of know about attributes so far has the quality been intuitive - how of the do we validate the logical design? schema? 8 issue of quality of design an illustration - figure 7.1b STOCK (Store, Product, Price, Quantity, Location, Discount, Sq_ft, Manager) are there data redundancies? for price? location? quantity? discount? repeated appearances of a data value ≠ data redundancy unneeded repetition that does what is data not add new meaning = data redundancy? redundancy data redundancy → modification anomalies are there data redundancies? yes - for price, location, and discount insertion anomaly - adding a washing machine with a price (cannot add without store number) update anomaly - store 11 moves to Cincinnati (requires updating multiple rows) deletion anomaly - store 17 is closed (pricing data about vacuum cleaner is lost) decomposition of the STOCK instance no data redundancies/ modification anomalies in STORE or PRODUCT data redundancies/ modification anomalies persist in INVENTORY normalized free from data redundancies/modification anomalies relational schema reverse-engineered from tables design-specific ER diagram reverse-engineered from relational schema how do we systematically identify data redundancies? how do we know how to decompose the base relation schema under investigation? how we know that the data decomposition is correct and complete? the seeds of data redundancy are undesirable functional dependencies. functional dependencies relationship between attributes that if we are given the value of one of the attributes we can look up the value FD of the other the building block of normalization principles functional dependencies an attribute A (atomic or composite) in a relation schema R functionally determines another attribute B (atomic or composite) in R if: ! for a given value a1 of A there is a single, specific value b1 of B in every relation state ri of R. -
Functional Dependencies and Normalization Juliana Freire
Design Theory for Relational Databases: Functional Dependencies and Normalization Juliana Freire Some slides adapted from L. Delcambre, R. Ramakrishnan, G. Lindstrom, J. Ullman and Silberschatz, Korth and Sudarshan Relational Database Design • Use the Entity Relationship (ER) model to reason about your data---structure and relationships, then translate model into a relational schema (more on this later) • Specify relational schema directly – Like what you do when you design the data structures for a program Pitfalls in Relational Database Design • Relational database design requires that we find a “good” collection of relation schemas • A bad design may lead to – Repetition of Information – Inability to represent certain information • Design Goals: – Avoid redundant data – Ensure that relationships among attributes are represented – Facilitate the checking of updates for violation of database integrity constraints Design Choices: Small vs. Large Schemas Which design do you like better? Why? EMPLOYEE(ENAME, SSN, ADDRESS, PNUMBER) PROJECT(PNAME, PNUMBER, PMGRSSN) EMP_PROJ(ENAME, SSN, ADDRESS, PNUMBER, PNAME, PMGRSSN) An employee can be assigned to at most one project, many employees participate in a project What’s wrong? EMP(ENAME, SSN, ADDRESS, PNUM, PNAME, PMGRSSN)" The description of the project (the name" and the manager of the project) is repeated" for every employee that works in that department." Redundancy! " The project is described redundantly." This leads to update anomalies." EMP(ENAME, SSN, ADDRESS, PNUM, PNAME, PMGRSSN)" Update -
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. -
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).