Fundamentals of Database Systems [Normalization – II]
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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. -
Normalized Form Snowflake Schema
Normalized Form Snowflake Schema Half-pound and unascertainable Wood never rhubarbs confoundedly when Filbert snore his sloop. Vertebrate or leewardtongue-in-cheek, after Hazel Lennie compartmentalized never shreddings transcendentally, any misreckonings! quite Crystalloiddiverted. Euclid grabbles no yorks adhered The star schemas in this does not have all revenue for this When done use When doing table contains less sensible of rows Snowflake Normalizationde-normalization Dimension tables are in normalized form the fact. Difference between Star Schema & Snow Flake Schema. The major difference between the snowflake and star schema models is slot the dimension tables of the snowflake model may want kept in normalized form to. Typically most of carbon fact tables in this star schema are in the third normal form while dimensional tables are de-normalized second normal. A relation is danger to pause in First Normal Form should each attribute increase the. The model is lazy in single third normal form 1141 Options to Normalize Assume that too are 500000 product dimension rows These products fall under 500. Hottest 'snowflake-schema' Answers Stack Overflow. Learn together is Star Schema Snowflake Schema And the Difference. For step three within the warehouses we tested Redshift Snowflake and Bigquery. On whose other hand snowflake schema is in normalized form. The CWM repository schema is a standalone product that other products can shareeach product owns only. The main difference between in two is normalization. Families of normalized form snowflake schema snowflake. Star and Snowflake Schema in Data line with Examples. Is spread the dimension tables in the snowflake schema are normalized. Like price weight speed and quantitiesie data execute a numerical format. -
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. -
Boyce-Codd Normal Forms Lecture 10 Sections 15.1 - 15.4
Boyce-Codd Normal Forms Lecture 10 Sections 15.1 - 15.4 Robb T. Koether Hampden-Sydney College Wed, Feb 6, 2013 Robb T. Koether (Hampden-Sydney College) Boyce-Codd Normal Forms Wed, Feb 6, 2013 1 / 15 1 Third Normal Form 2 Boyce-Codd Normal Form 3 Assignment Robb T. Koether (Hampden-Sydney College) Boyce-Codd Normal Forms Wed, Feb 6, 2013 2 / 15 Outline 1 Third Normal Form 2 Boyce-Codd Normal Form 3 Assignment Robb T. Koether (Hampden-Sydney College) Boyce-Codd Normal Forms Wed, Feb 6, 2013 3 / 15 Third Normal Form Definition (Transitive Dependence) A set of attributes Z is transitively dependent on a set of attributes X if there exists a set of attributes Y such that X ! Y and Y ! Z. Definition (Third Normal Form) A relation R is in third normal form (3NF) if it is in 2NF and there is no nonprime attribute of R that is transitively dependent on any key of R. 3NF is violated if there is a nonprime attribute A that depends on something less than a key. Robb T. Koether (Hampden-Sydney College) Boyce-Codd Normal Forms Wed, Feb 6, 2013 4 / 15 Example Example order_no cust_no cust_name 222-1 3333 Joe Smith 444-2 4444 Sue Taylor 555-1 3333 Joe Smith 777-2 7777 Bob Sponge 888-3 4444 Sue Taylor Table 3 Table 3 is in 2NF, but it is not in 3NF because [order_no] ! [cust_no] ! [cust_name]: Robb T. Koether (Hampden-Sydney College) Boyce-Codd Normal Forms Wed, Feb 6, 2013 5 / 15 3NF Normalization To put a relation into 3NF, for each set of transitive function dependencies X ! Y ! Z , make two tables, one for X ! Y and another for Y ! Z . -
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. -
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. -
SEMESTER-II Functional Dependencies
For SEMESTER-II Paper II : Database Management Systems(CODE: CSC-RC-2016) Topics: Functional dependencies, Normalization and Normal forms up to 3NF Functional Dependencies A functional dependency (FD) is a relationship between two attributes, typically between the primary key and other non-key attributes within a table. A functional dependency denoted by X Y , is an association between two sets of attribute X and Y. Here, X is called the determinant, and Y is called the dependent. For example, SIN ———-> Name, Address, Birthdate Here, SIN determines Name, Address and Birthdate.So, SIN is the determinant and Name, Address and Birthdate are the dependents. Rules of Functional Dependencies 1. Reflexive rule : If Y is a subset of X, then X determines Y . 2. Augmentation rule: It is also known as a partial dependency, says if X determines Y, then XZ determines YZ for any Z 3. Transitivity rule: Transitivity says if X determines Y, and Y determines Z, then X must also determine Z Types of functional dependency The following are types functional dependency in DBMS: 1. Fully-Functional Dependency 2. Partial Dependency 3. Transitive Dependency 4. Trivial Dependency 5. Multivalued Dependency Full functional Dependency A functional dependency XY is said to be a full functional dependency, if removal of any attribute A from X, the dependency does not hold any more. i.e. Y is fully functional dependent on X, if it is Functionally Dependent on X and not on any of the proper subset of X. For example, {Emp_num,Proj_num} Hour Is a full functional dependency. Here, Hour is the working time by an employee in a project. -
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