On the Logic of SQL Nulls
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Further Normalization of the Data Base Relational Model
FURTHER NORMALIZATION OF THE DATA BASE RELATIONAL MODEL E. F. Codd IBM Research Laboratory San Jose, California ABSTRACT: In an earlier paper, the author proposed a relational model of data as a basis for protecting users of formatted data systems from the potentially disruptive changes in data representation caused by growth in the data base and changes in traffic. A first normal form for the time-varying collection of relations was introduced. In this paper, second and third normal forms are defined with the objective of making the collection of relations easier to understand and control, simpler to operate upon, and more informative to the casual user. The question "Can application programs be kept in a viable state when data base relations are restructured?" is discussed briefly and it is conjectured that third normal form will significantly extend the life expectancy of appli- cation programs. Fu909umxk7) August 31,197l Information technolow (IR, Documentetion, etc.) 1. 1. Introduction 1.1 Objectives of Normalization In an earlier paper [l] the author proposed a relational model of data as a basis for protecting users of formatted data systems from the potentially disruptive changes in data representation caused by growth in the variety of data types in the data base and by statistical changes in the transaction or request traffic. Using this model, both the appli- cation programmer and the interactive user view the data base as a time-varying collection of normalized relations of assorted degrees. Definitions of these terms and of the basic relational operations of projection and natural join are given in the Appendix. -
Using Relational Databases in the Engineering Repository Systems
USING RELATIONAL DATABASES IN THE ENGINEERING REPOSITORY SYSTEMS Erki Eessaar Department of Informatics, Tallinn University of Technology, Raja 15,12618 Tallinn, Estonia Keywords: Relational data model, Object-relational data model, Repository, Metamodeling. Abstract: Repository system can be built on top of the database management system (DBMS). DBMSs that use relational data model are usually not considered powerful enough for this purpose. In this paper, we analyze these claims and conclude that they are caused by the shortages of SQL standard and inadequate implementations of the relational model in the current DBMSs. Problems that are presented in the paper make usage of the DBMSs in the repository systems more difficult. This paper also explains that relational system that follows the rules of the Third Manifesto is suitable for creating repository system and presents possible design alternatives. 1 INTRODUCTION technologies in one data model is ROSE (Hardwick & Spooner, 1989) that is experimental data "A repository is a shared database of information management system for the interactive engineering about the engineered artifacts." (Bernstein, 1998) applications. Bernstein (2003) envisions that object- These artifacts can be software engineering artifacts relational systems are good platform for the model like models and patterns. Repository system contains management systems. ORIENT (Zhang et al., 2001) a repository manager and a repository (database) and SFB-501 Reuse Repository (Mahnke & Ritter, (Bernstein, 1998). Bernstein (1998) explains that 2002) are examples of the repository systems that repository manager provides services for modeling, use a commercial ORDBMS. ORDBMS in this case retrieving, and managing objects in the repository is a system which uses a database language that and therefore must offer functions of the Database conforms to SQL:1999 or later standard. -
Database Concepts 7Th Edition
Database Concepts 7th Edition David M. Kroenke • David J. Auer Online Appendix E SQL Views, SQL/PSM and Importing Data Database Concepts SQL Views, SQL/PSM and Importing Data Appendix E All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. Appendix E — 10 9 8 7 6 5 4 3 2 1 E-2 Database Concepts SQL Views, SQL/PSM and Importing Data Appendix E Appendix Objectives • To understand the reasons for using SQL views • To use SQL statements to create and query SQL views • To understand SQL/Persistent Stored Modules (SQL/PSM) • To create and use SQL user-defined functions • To import Microsoft Excel worksheet data into a database What is the Purpose of this Appendix? In Chapter 3, we discussed SQL in depth. We discussed two basic categories of SQL statements: data definition language (DDL) statements, which are used for creating tables, relationships, and other structures, and data manipulation language (DML) statements, which are used for querying and modifying data. In this appendix, which should be studied immediately after Chapter 3, we: • Describe and illustrate SQL views, which extend the DML capabilities of SQL. • Describe and illustrate SQL Persistent Stored Modules (SQL/PSM), and create user-defined functions. • Describe and use DBMS data import techniques to import Microsoft Excel worksheet data into a database. E-3 Database Concepts SQL Views, SQL/PSM and Importing Data Appendix E Creating SQL Views An SQL view is a virtual table that is constructed from other tables or views. -
CONNOLLY R CAROLYN E. BEGG a Practical Approach to Design
•.••:.... ••:••.•; ••••• •:• ; •.•••:•. • ..• .: . • •• ••••:..• CONNOLLY r CAROLYN E. BEGG 1VERS1TYOF PA1SL К m A Practical Approach to Design, Implementation, and Management Third Edition Contents Preface xxxv Part 1 Background Chapter 1 Introduction to Databases 1.1 Introduction 1.2 Traditional File-Based Systems D 1.2.1 File-Based Approach 7 1.2.2 Limitations of the File-Based Approach 12 1.3 Database Approach 14 1.3.1 The Database 14 1.3.2 The Database Management System (DBMS) 16 1.3.3 Components of the DBMS Environment 18 1.3.4 Database Design: The Paradigm Shift 20 1.4 Roles in the Database Environment 21 1.4.1 Data and Database Administrators 21 1.4.2 Database Designers 22 1.4.3 Application Developers 23 1.4.4 End-Users 23 1.5 History of Database Management Systems 23 1.6 Advantages and Disadvantages of DBMSs 25 Chapter Summary 30 Review Questions 31 Exercises 31 Chapter 2 Database Environment 33 2.1 The Three-Level ANSI-SPARC Architecture 34 2.1.1 External Level 35 2.1.2 Conceptual Level 36 2.1.3 Internal Level 36 xii Contents 2.1.4 Schemas, Mappings, and Instances 37 2.1.5 Data Independence 38 2.2 Database Languages 40 2.2.1 The Data Definition Language (DDL) 40 2.2.2 The Data Manipulation Language (DML) 41 2.2.3 Fourth-Generation Languages (4GL) 42 2.3 Data Models and Conceptual Modeling 43 2.3.1 Object-Based Data Models 44 2.3.2 Record-Based Data Models 45 2.3.3 Physical Data Models 47 2.3.4 Conceptual Modeling 47 2.4 Functions of a DBMS 48 2.5 Components of a DBMS 53 2.6 Multi-User DBMS Architectures 56 2.6.1 Teleprocessing -
Applied Mathematics for Database Professionals
7451FM.qxd 5/17/07 10:41 AM Page i Applied Mathematics for Database Professionals Lex de Haan and Toon Koppelaars 7451FM.qxd 5/17/07 10:41 AM Page ii Applied Mathematics for Database Professionals Copyright © 2007 by Lex de Haan and Toon Koppelaars All rights reserved. No part of this work may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage or retrieval system, without the prior written permission of the copyright owner and the publisher. ISBN-13: 978-1-59059-745-3 ISBN-10: 1-59059-745-1 Printed and bound in the United States of America 9 8 7 6 5 4 3 2 1 Trademarked names may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, we use the names only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. Lead Editor: Jonathan Gennick Technical Reviewers: Chris Date, Cary Millsap Editorial Board: Steve Anglin, Ewan Buckingham, Gary Cornell, Jonathan Gennick, Jason Gilmore, Jonathan Hassell, Chris Mills, Matthew Moodie, Jeffrey Pepper, Ben Renow-Clarke, Dominic Shakeshaft, Matt Wade, Tom Welsh Project Manager: Tracy Brown Collins Copy Edit Manager: Nicole Flores Copy Editor: Susannah Davidson Pfalzer Assistant Production Director: Kari Brooks-Copony Production Editor: Kelly Winquist Compositor: Dina Quan Proofreader: April Eddy Indexer: Brenda Miller Artist: April Milne Cover Designer: Kurt Krames Manufacturing Director: Tom Debolski Distributed to the book trade worldwide by Springer-Verlag New York, Inc., 233 Spring Street, 6th Floor, New York, NY 10013. -
How to Get Data from Oracle to Postgresql and Vice Versa Who We Are
How to get data from Oracle to PostgreSQL and vice versa Who we are The Company > Founded in 2010 > More than 70 specialists > Specialized in the Middleware Infrastructure > The invisible part of IT > Customers in Switzerland and all over Europe Our Offer > Consulting > Service Level Agreements (SLA) > Trainings > License Management How to get data from Oracle to PostgreSQL and vice versa 19.06.2020 Page 2 About me Daniel Westermann Principal Consultant Open Infrastructure Technology Leader +41 79 927 24 46 daniel.westermann[at]dbi-services.com @westermanndanie Daniel Westermann How to get data from Oracle to PostgreSQL and vice versa 19.06.2020 Page 3 How to get data from Oracle to PostgreSQL and vice versa Before we start We have a PostgreSQL user group in Switzerland! > https://www.swisspug.org Consider supporting us! How to get data from Oracle to PostgreSQL and vice versa 19.06.2020 Page 4 How to get data from Oracle to PostgreSQL and vice versa Before we start We have a PostgreSQL meetup group in Switzerland! > https://www.meetup.com/Switzerland-PostgreSQL-User-Group/ Consider joining us! How to get data from Oracle to PostgreSQL and vice versa 19.06.2020 Page 5 Agenda 1.Past, present and future 2.SQL/MED 3.Foreign data wrappers 4.Demo 5.Conclusion How to get data from Oracle to PostgreSQL and vice versa 19.06.2020 Page 6 Disclaimer This session is not about logical replication! If you are looking for this: > Data Replicator from DBPLUS > https://blog.dbi-services.com/real-time-replication-from-oracle-to-postgresql-using-data-replicator-from-dbplus/ -
Handling Missing Values in the SQL Procedure
Handling Missing Values in the SQL Procedure Danbo Yi, Abt Associates Inc., Cambridge, MA Lei Zhang, Domain Solutions Corp., Cambridge, MA non-missing numeric values. A missing ABSTRACT character value is expressed and treated as a string of blanks. Missing character values PROC SQL as a powerful database are always same no matter whether it is management tool provides many features expressed as one blank, or more than one available in the DATA steps and the blanks. Obviously, missing character values MEANS, TRANSPOSE, PRINT and SORT are not the smallest strings. procedures. If properly used, PROC SQL often results in concise solutions to data In SAS system, the way missing Date and manipulations and queries. PROC SQL DateTime values are expressed and treated follows most of the guidelines set by the is similar to missing numeric values. American National Standards Institute (ANSI) in its implementation of SQL. This paper will cover following topics. However, it is not fully compliant with the 1. Missing Values and Expression current ANSI Standard for SQL, especially · Logic Expression for the missing values. PROC SQL uses · Arithmetic Expression SAS System convention to express and · String Expression handle the missing values, which is 2. Missing Values and Predicates significantly different from many ANSI- · IS NULL /IS MISSING compatible SQL databases such as Oracle, · [NOT] LIKE Sybase. In this paper, we summarize the · ALL, ANY, and SOME ways the PROC SQL handles the missing · [NOT] EXISTS values in a variety of situations. Topics 3. Missing Values and JOINs include missing values in the logic, · Inner Join arithmetic and string expression, missing · Left/Right Join values in the SQL predicates such as LIKE, · Full Join ANY, ALL, JOINs, missing values in the 4. -
Chapter 11 Querying
Oracle TIGHT / Oracle Database 11g & MySQL 5.6 Developer Handbook / Michael McLaughlin / 885-8 Blind folio: 273 CHAPTER 11 Querying 273 11-ch11.indd 273 9/5/11 4:23:56 PM Oracle TIGHT / Oracle Database 11g & MySQL 5.6 Developer Handbook / Michael McLaughlin / 885-8 Oracle TIGHT / Oracle Database 11g & MySQL 5.6 Developer Handbook / Michael McLaughlin / 885-8 274 Oracle Database 11g & MySQL 5.6 Developer Handbook Chapter 11: Querying 275 he SQL SELECT statement lets you query data from the database. In many of the previous chapters, you’ve seen examples of queries. Queries support several different types of subqueries, such as nested queries that run independently or T correlated nested queries. Correlated nested queries run with a dependency on the outer or containing query. This chapter shows you how to work with column returns from queries and how to join tables into multiple table result sets. Result sets are like tables because they’re two-dimensional data sets. The data sets can be a subset of one table or a set of values from two or more tables. The SELECT list determines what’s returned from a query into a result set. The SELECT list is the set of columns and expressions returned by a SELECT statement. The SELECT list defines the record structure of the result set, which is the result set’s first dimension. The number of rows returned from the query defines the elements of a record structure list, which is the result set’s second dimension. You filter single tables to get subsets of a table, and you join tables into a larger result set to get a superset of any one table by returning a result set of the join between two or more tables. -
Firebird 3 Windowing Functions
Firebird 3 Windowing Functions Firebird 3 Windowing Functions Author: Philippe Makowski IBPhoenix Email: pmakowski@ibphoenix Licence: Public Documentation License Date: 2011-11-22 Philippe Makowski - IBPhoenix - 2011-11-22 Firebird 3 Windowing Functions What are Windowing Functions? • Similar to classical aggregates but does more! • Provides access to set of rows from the current row • Introduced SQL:2003 and more detail in SQL:2008 • Supported by PostgreSQL, Oracle, SQL Server, Sybase and DB2 • Used in OLAP mainly but also useful in OLTP • Analysis and reporting by rankings, cumulative aggregates Philippe Makowski - IBPhoenix - 2011-11-22 Firebird 3 Windowing Functions Windowed Table Functions • Windowed table function • operates on a window of a table • returns a value for every row in that window • the value is calculated by taking into consideration values from the set of rows in that window • 8 new windowed table functions • In addition, old aggregate functions can also be used as windowed table functions • Allows calculation of moving and cumulative aggregate values. Philippe Makowski - IBPhoenix - 2011-11-22 Firebird 3 Windowing Functions A Window • Represents set of rows that is used to compute additionnal attributes • Based on three main concepts • partition • specified by PARTITION BY clause in OVER() • Allows to subdivide the table, much like GROUP BY clause • Without a PARTITION BY clause, the whole table is in a single partition • order • defines an order with a partition • may contain multiple order items • Each item includes -
Using the Set Operators Questions
UUSSIINNGG TTHHEE SSEETT OOPPEERRAATTOORRSS QQUUEESSTTIIOONNSS http://www.tutorialspoint.com/sql_certificate/using_the_set_operators_questions.htm Copyright © tutorialspoint.com 1.Which SET operator does the following figure indicate? A. UNION B. UNION ALL C. INTERSECT D. MINUS Answer: A. Set operators are used to combine the results of two ormore SELECT statements.Valid set operators in Oracle 11g are UNION, UNION ALL, INTERSECT, and MINUS. When used with two SELECT statements, the UNION set operator returns the results of both queries.However,if there are any duplicates, they are removed, and the duplicated record is listed only once.To include duplicates in the results,use the UNION ALL set operator.INTERSECT lists only records that are returned by both queries; the MINUS set operator removes the second query's results from the output if they are also found in the first query's results. INTERSECT and MINUS set operations produce unduplicated results. 2.Which SET operator does the following figure indicate? A. UNION B. UNION ALL C. INTERSECT D. MINUS Answer: B. UNION ALL Returns the combined rows from two queries without sorting or removing duplicates. sql_certificate 3.Which SET operator does the following figure indicate? A. UNION B. UNION ALL C. INTERSECT D. MINUS Answer: C. INTERSECT Returns only the rows that occur in both queries' result sets, sorting them and removing duplicates. 4.Which SET operator does the following figure indicate? A. UNION B. UNION ALL C. INTERSECT D. MINUS Answer: D. MINUS Returns only the rows in the first result set that do not appear in the second result set, sorting them and removing duplicates. -
SQL Version Analysis
Rory McGann SQL Version Analysis Structured Query Language, or SQL, is a powerful tool for interacting with and utilizing databases through the use of relational algebra and calculus, allowing for efficient and effective manipulation and analysis of data within databases. There have been many revisions of SQL, some minor and others major, since its standardization by ANSI in 1986, and in this paper I will discuss several of the changes that led to improved usefulness of the language. In 1970, Dr. E. F. Codd published a paper in the Association of Computer Machinery titled A Relational Model of Data for Large shared Data Banks, which detailed a model for Relational database Management systems (RDBMS) [1]. In order to make use of this model, a language was needed to manage the data stored in these RDBMSs. In the early 1970’s SQL was developed by Donald Chamberlin and Raymond Boyce at IBM, accomplishing this goal. In 1986 SQL was standardized by the American National Standards Institute as SQL-86 and also by The International Organization for Standardization in 1987. The structure of SQL-86 was largely similar to SQL as we know it today with functionality being implemented though Data Manipulation Language (DML), which defines verbs such as select, insert into, update, and delete that are used to query or change the contents of a database. SQL-86 defined two ways to process a DML, direct processing where actual SQL commands are used, and embedded SQL where SQL statements are embedded within programs written in other languages. SQL-86 supported Cobol, Fortran, Pascal and PL/1. -
“A Relational Model of Data for Large Shared Data Banks”
“A RELATIONAL MODEL OF DATA FOR LARGE SHARED DATA BANKS” Through the internet, I find more information about Edgar F. Codd. He is a mathematician and computer scientist who laid the theoretical foundation for relational databases--the standard method by which information is organized in and retrieved from computers. In 1981, he received the A. M. Turing Award, the highest honor in the computer science field for his fundamental and continuing contributions to the theory and practice of database management systems. This paper is concerned with the application of elementary relation theory to systems which provide shared access to large banks of formatted data. It is divided into two sections. In section 1, a relational model of data is proposed as a basis for protecting users of formatted data systems from the potentially disruptive changes in data representation caused by growth in the data bank and changes in traffic. A normal form for the time-varying collection of relationships is introduced. In Section 2, certain operations on relations are discussed and applied to the problems of redundancy and consistency in the user's model. Relational model provides a means of describing data with its natural structure only--that is, without superimposing any additional structure for machine representation purposes. Accordingly, it provides a basis for a high level data language which will yield maximal independence between programs on the one hand and machine representation and organization of data on the other. A further advantage of the relational view is that it forms a sound basis for treating derivability, redundancy, and consistency of relations.