Database Normalization 1 Database Normalization
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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. -
The Design of Multidimensional Data Model Using Principles of the Anchor Data Modeling: an Assessment of Experimental Approach Based on Query Execution Performance
WSEAS TRANSACTIONS on COMPUTERS Radek Němec, František Zapletal The Design of Multidimensional Data Model Using Principles of the Anchor Data Modeling: An Assessment of Experimental Approach Based on Query Execution Performance RADEK NĚMEC, FRANTIŠEK ZAPLETAL Department of Systems Engineering Faculty of Economics, VŠB - Technical University of Ostrava Sokolská třída 33, 701 21 Ostrava CZECH REPUBLIC [email protected], [email protected] Abstract: - The decision making processes need to reflect changes in the business world in a multidimensional way. This includes also similar way of viewing the data for carrying out key decisions that ensure competitiveness of the business. In this paper we focus on the Business Intelligence system as a main toolset that helps in carrying out complex decisions and which requires multidimensional view of data for this purpose. We propose a novel experimental approach to the design a multidimensional data model that uses principles of the anchor modeling technique. The proposed approach is expected to bring several benefits like better query execution performance, better support for temporal querying and several others. We provide assessment of this approach mainly from the query execution performance perspective in this paper. The emphasis is placed on the assessment of this technique as a potential innovative approach for the field of the data warehousing with some implicit principles that could make the process of the design, implementation and maintenance of the data warehouse more effective. The query performance testing was performed in the row-oriented database environment using a sample of 10 star queries executed in the environment of 10 sample multidimensional data models. -
Powerdesigner 16.6 Data Modeling
SAP® PowerDesigner® Document Version: 16.6 – 2016-02-22 Data Modeling Content 1 Building Data Models ...........................................................8 1.1 Getting Started with Data Modeling...................................................8 Conceptual Data Models........................................................8 Logical Data Models...........................................................9 Physical Data Models..........................................................9 Creating a Data Model.........................................................10 Customizing your Modeling Environment........................................... 15 1.2 Conceptual and Logical Diagrams...................................................26 Supported CDM/LDM Notations.................................................27 Conceptual Diagrams.........................................................31 Logical Diagrams............................................................43 Data Items (CDM)............................................................47 Entities (CDM/LDM)..........................................................49 Attributes (CDM/LDM)........................................................55 Identifiers (CDM/LDM)........................................................58 Relationships (CDM/LDM)..................................................... 59 Associations and Association Links (CDM)..........................................70 Inheritances (CDM/LDM)......................................................77 1.3 Physical Diagrams..............................................................82 -
Denormalization Strategies for Data Retrieval from Data Warehouses
Decision Support Systems 42 (2006) 267–282 www.elsevier.com/locate/dsw Denormalization strategies for data retrieval from data warehouses Seung Kyoon Shina,*, G. Lawrence Sandersb,1 aCollege of Business Administration, University of Rhode Island, 7 Lippitt Road, Kingston, RI 02881-0802, United States bDepartment of Management Science and Systems, School of Management, State University of New York at Buffalo, Buffalo, NY 14260-4000, United States Available online 20 January 2005 Abstract In this study, the effects of denormalization on relational database system performance are discussed in the context of using denormalization strategies as a database design methodology for data warehouses. Four prevalent denormalization strategies have been identified and examined under various scenarios to illustrate the conditions where they are most effective. The relational algebra, query trees, and join cost function are used to examine the effect on the performance of relational systems. The guidelines and analysis provided are sufficiently general and they can be applicable to a variety of databases, in particular to data warehouse implementations, for decision support systems. D 2004 Elsevier B.V. All rights reserved. Keywords: Database design; Denormalization; Decision support systems; Data warehouse; Data mining 1. Introduction houses as issues related to database design for high performance are receiving more attention. Database With the increased availability of data collected design is still an art that relies heavily on human from the Internet and other sources and the implemen- intuition and experience. Consequently, its practice is tation of enterprise-wise data warehouses, the amount becoming more difficult as the applications that data- of data that companies possess is growing at a bases support become more sophisticated [32].Cur- phenomenal rate. -
A Comprehensive Analysis of Sybase Powerdesigner 16.0
white paper A Comprehensive Analysis of Sybase® PowerDesigner® 16.0 InformationArchitect vs. ER/Studio XE2 Version 2.0 www.sybase.com TABLe OF CONTENtS 1 Introduction 1 Product Overviews 1 ER/Studio XE2 3 Sybase PowerDesigner 16.0 4 Data Modeling Activities 4 Overview 6 Types of Data Model 7 Design Layers 8 Managing the SAM-LDM Relationship 10 Forward and Reverse Engineering 11 Round-trip Engineering 11 Integrating Data Models with Requirements and Processes 11 Generating Object-oriented Models 11 Dependency Analysis 17 Model Comparisons and Merges 18 Update Flows 18 Required Features for a Data Modeling Tool 18 Core Modeling 25 Collaboration 27 Interfaces & Integration 29 Usability 34 Managing Models as a Project 36 Dependency Matrices 37 Conclusions 37 Acknowledgements 37 Bibliography 37 About the Author IntrOduCtion Data modeling is more than just database design, because data doesn’t just exist in databases. Data does not exist in isolation, it is created, managed and consumed by business processes, and those business processes are implemented using a variety of applications and technologies. To truly understand and manage our data, and the impact of changes to that data, we need to manage more than just models of data in databases. We need support for different types of data models, and for managing the relationships between data and the rest of the organization. When you need to manage a data center across the enterprise, integrating with a wider set of business and technology activities is critical to success. For this reason, this review will use the InformationArchitect version of Sybase PowerDesigner rather than their DataArchitect™ version. -
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. -
Database Design Solutions
spine=1.10" Wrox Programmer to ProgrammerTM Wrox Programmer to ProgrammerTM Beginning Stephens Database Design Solutions Databases play a critical role in the business operations of most organizations; they’re the central repository for critical information on products, customers, Beginning suppliers, sales, and a host of other essential information. It’s no wonder that Solutions Database Design the majority of all business computing involves database applications. With so much at stake, you’d expect most IT professionals would have a firm understanding of good database design. But in fact most learn through a painful process of trial and error, with predictably poor results. This book provides readers with proven methods and tools for designing efficient, reliable, and secure databases. Author Rod Stephens explains how a database should be organized to ensure data integrity without sacrificing performance. He shares procedures for designing robust, flexible, and secure databases that provide a solid foundation for all of your database applications. The methods and techniques in this book can be applied to any database environment, including Oracle®, Microsoft Access®, SQL Server®, and MySQL®. You’ll learn the basics of good database design and ultimately discover how to design a real-world database. What you will learn from this book ● How to identify database requirements that meet users’ needs ● Ways to build data models using a variety of modeling techniques, including Beginning entity-relational models, user-interface models, and -
Star and Snowflake Schema Tutorialpoint
Star And Snowflake Schema Tutorialpoint Tweedy and close-lipped Moise segregating: which Skye is daimen enough? Is Doyle ungallant or herbless when pricing some Honduras fordoing patchily? Fulgid and coiled Derick cleats her riffs pleonasm glue and overemphasizing distastefully. Of disparate data on those systems columns that are used to extract. Introduction to Slowly Changing Dimensions SCD Types. 1 a diagrammatic presentation broadly a structured framework where plan outline 2 a mental codification of miss that includes a particular organized way of perceiving cognitively and responding to substantial complex authority or decay of stimuli. Work smarter to authorize time they solve problems. The organized data helps is reporting and preserve business decision effectively. Real data warehouse consists of star schema eliminates many types of a search engines read our experts follow these columns in a specific interval of. Pembangunan data storage requirements are commenting using our library is snowflaked outward into mental shortcuts are. Liquibase tutorialspoint. Which data model is lowest level? Star and Snowflake Schema in warehouse Warehouse with Examples. In star schema is snowflaked outward into our schema gives optimal disk space to build road maps the! Data Warehouse Modeling Snowflake Schema. Cross pollination is water transfer of pollen grains from the anther of free flower use the stigma of a genetically different flower. Adding structured data give your website can glide quite daunting. The difference is process the dimensions themselves. Discuss the advantages Disadvantages of star snowflake. Learn and snowflake schemas can see what is snowflaked into additional lookup tables of courses platform, the primary key, partition in the. -
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. -
Data Warehousing
DMIF, University of Udine Data Warehousing Andrea Brunello [email protected] April, 2020 (slightly modified by Dario Della Monica) Outline 1 Introduction 2 Data Warehouse Fundamental Concepts 3 Data Warehouse General Architecture 4 Data Warehouse Development Approaches 5 The Multidimensional Model 6 Operations over Multidimensional Data 2/80 Andrea Brunello Data Warehousing Introduction Nowadays, most of large and medium size organizations are using information systems to implement their business processes. As time goes by, these organizations produce a lot of data related to their business, but often these data are not integrated, been stored within one or more platforms. Thus, they are hardly used for decision-making processes, though they could be a valuable aiding resource. A central repository is needed; nevertheless, traditional databases are not designed to review, manage and store historical/strategic information, but deal with ever changing operational data, to support “daily transactions”. 3/80 Andrea Brunello Data Warehousing What is Data Warehousing? Data warehousing is a technique for collecting and managing data from different sources to provide meaningful business insights. It is a blend of components and processes which allows the strategic use of data: • Electronic storage of a large amount of information which is designed for query and analysis instead of transaction processing • Process of transforming data into information and making it available to users in a timely manner to make a difference 4/80 Andrea Brunello Data Warehousing Why Data Warehousing? A 3NF-designed database for an inventory system has many tables related to each other through foreign keys. A report on monthly sales information may include many joined conditions. -
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.