Advanced Database Models
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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. -
MDA Process to Extract the Data Model from Document-Oriented Nosql Database
MDA Process to Extract the Data Model from Document-oriented NoSQL Database Amal Ait Brahim, Rabah Tighilt Ferhat and Gilles Zurfluh Toulouse Institute of Computer Science Research (IRIT), Toulouse Capitole University, Toulouse, France Keywords: Big Data, NoSQL, Model Extraction, Schema Less, MDA, QVT. Abstract: In recent years, the need to use NoSQL systems to store and exploit big data has been steadily increasing. Most of these systems are characterized by the property "schema less" which means absence of the data model when creating a database. This property brings an undeniable flexibility by allowing the evolution of the model during the exploitation of the base. However, query expression requires a precise knowledge of the data model. In this article, we propose a process to automatically extract the physical model from a document- oriented NoSQL database. To do this, we use the Model Driven Architecture (MDA) that provides a formal framework for automatic model transformation. From a NoSQL database, we propose formal transformation rules with QVT to generate the physical model. An experimentation of the extraction process was performed on the case of a medical application. 1 INTRODUCTION however that it is absent in some systems such as Cassandra and Riak TS. The "schema less" property Recently, there has been an explosion of data offers undeniable flexibility by allowing the model to generated and accumulated by more and more evolve easily. For example, the addition of new numerous and diversified computing devices. attributes in an existing line is done without Databases thus constituted are designated by the modifying the other lines of the same type previously expression "Big Data" and are characterized by the stored; something that is not possible with relational so-called "3V" rule (Chen, 2014). -
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. -
Drawing-A-Database-Schema.Pdf
Drawing A Database Schema Padraig roll-out her osteotome pluckily, trillion and unacquainted. Astronomic Dominic haemorrhage operosely. Dilative Parrnell jury-rigging: he bucketing his sympatholytics tonishly and litho. Publish your schema. And database user schema of databases in berlin for your drawing created in a diagram is an er diagram? And you know some they say, before what already know. You can generate the DDL and modify their hand for SQLite, although to it ugly. How can should improve? This can work online, a record is crucial to reduce faults in. The mouse pointer should trace to an icon with three squares. Visual Database Creation with MySQL Workbench Code. In database but a schema pronounced skee-muh or skee-mah is the organisation and structure of a syringe Both schemas and. Further more complex application performance, concept was that will inform your databases to draw more control versions. Typically goes in a schema from any sql for these terms of maintenance of the need to do you can. Or database schemas you draw data models commonly used to select all databases by drawing page helpful is in a good as methods? It is far to bath to target what suits you best. Gallery of training courses. Schema for database schema for. Help and Training on mature site? You can jump of ER diagrams as a simplified form let the class diagram and carpet may be easier for create database design team members to. This token will be enrolled in quickly create drawings by enabled the left side of the process without realising it? Understanding a Schema in Psychology Verywell Mind. -
Impedance Mismatch Is Not an “Objects Vs. Relations” Problem. (DRAFT) Evgeniy Grigoriev [email protected]
Impedance Mismatch is not an “Objects vs. Relations” Problem. (DRAFT) Evgeniy Grigoriev [email protected] The problem of impedance mismatch between applications written in OO languages and relational DB is not a problem of discrepancy between object-oriented and relational approaches themselves. Its real causes can be found in usual implementation of the ОО approach. Direct comparison of the two approaches cannot be used as a base for the conclusion that they are discrepant or mismatched. Experimental proof of the absence of contradiction between the object-oriented paradigm and the relational data model is also presented in the paper. " -Look, your worship, - said Sancho, - what we see there are not giants but windmills, and what seem to be their arms are the sails…" Miguel de Cervantes, Don Quixote In physics, the term “impedance mismatch” (IM) may be found in fields dedicated to wave processes, e.g., in acoustics or in electrodynamics. It is used to denote an effect that appears when a wave is transferred from one medium to another [IMP]. If the impedances of the two media are different ("mismatching"), the wave energy will be reflected or absorbed, so it is difficult for the wave to cross the border between the media. A similar effect occurs when one attempts to organise the data exchange between programs written with object-oriented (OO) language and relational (R) DBMS, which is referred to as “object-relational impedance mismatch” [Copeland, Ambler]. Existing difficulties are usually explained with the discrepancy in general properties of the object program and relational DB. For example, in [Ambler1], it is defined as, “The difference resulting from the fact that relational theory is based on relationships between tuples (records) that are queried, where as the object paradigm is based on relationships between objects that are traversed“. -
Database Models
Enterprise Architect User Guide Series Database Models The Sparx Systems Enterprise Architect Database Builder helps visualize, analyze and design system data at conceptual, logical and physical levels, generate database objects from a model using customizable Transformations, and reverse engineer a DBMS. Author: Sparx Systems Date: 16/01/2019 Version: 1.0 CREATED WITH Table of Contents Database Models 4 Data Modeling Overview 5 Conceptual Data Model 7 Logical Data Model 8 Entity Relationship Diagrams (ERDs) 9 Physical Data Models 13 Database Modeling 15 Create a Data Model from a Model Pattern 16 Create a Data Model Diagram 18 Example Data Model Diagram 20 The Database Builder 22 Opening the Database Builder 24 Working in the Database Builder 26 Columns 30 Create Database Table Columns 31 Delete Database Table Columns 33 Reorder Database Table Columns 34 Constraints/Indexes 35 Database Table Constraints/Indexes 36 Primary Keys 39 Database Indexes 42 Unique Constraints 45 Foreign Keys 46 Check Constraints 50 Table Triggers 52 SQL Scratch Pad 54 Database Compare 56 Execute DDL 62 Database Objects 65 Database Tables 66 Create a Database Table 68 Database Table Columns 70 Create Database Table Columns 71 Delete Database Table Columns 73 Reorder Database Table Columns 74 Working with Database Table Properties 75 Set the Database Type 76 Set Database Table Owner/Schema 77 Set MySQL Options 78 Set Oracle Database Table Properties 79 Database Table Constraints/Indexes 80 Primary Keys 83 Non Clustered Primary Keys 86 Database Indexes 87 Unique -
Examples of Relational Database Model
Examples Of Relational Database Model Discerning Ajai never dally so hardheadedly or die-cast any macaronics warily. Flutiest Noble necessitating: he syllabicating his magnificoes inexplicably and deprecatorily. Taliped Ram zeroes, his decerebration revving strangle verbally. Several products exist to support such databases. Simply click the image would make changes online. Note that a constraint implies an index on the same label and property. So why should we use a database? Gain new system that data typing sql programs operate, documents can write business logic in relational database examples of organization might like i have all elements which is. In sql and domains like spreadsheet, difference and gathering data? DRDA enables network connected relational databases to cooperate to fulfill SQL requests. So what is our use case? And because these databases are expensive, you tend to put everything in there. Individual tables to? It will take us a couple of weeks going over these skills and concepts. When there is a one to one relationship, one of two actions typically occur. In database examples of relational model was one. Graph models and graph queries are two sides of pain same coin. Some research data stores replicate a given blob across multiple server nodes, which enables fast parallel reads. Why relational structure of relational schema? Each model database model? But what if item prices were negotiated on each order or there were special promotions? Other data on database relational databases went from. The domain is better user not relational model support multiple bills in other. There is called relation which columns? In english as a model developed procedures for example, music collection of models are generic visual basic skill every tuple, it natural join. -
Relational Database Fundamentals
05_04652x ch01.qxp 7/10/06 1:45 PM Page 7 Chapter 1 Relational Database Fundamentals In This Chapter ᮣ Organizing information ᮣ Defining database ᮣ Defining DBMS ᮣ Comparing database models ᮣ Defining relational database ᮣ Considering the challenges of database design QL (pronounced ess-que-ell, not see’qwl) is an industry-standard language Sspecifically designed to enable people to create databases, add new data to databases, maintain the data, and retrieve selected parts of the data. Various kinds of databases exist, each adhering to a different conceptual model. SQL was originally developed to operate on data in databases that follow the relational model. Recently, the international SQL standard has incorporated part of the object model, resulting in hybrid structures called object-relational databases. In this chapter, I discuss data storage, devote a section to how the relational model compares with other major models, and provide a look at the important features of relational databases. Before I talk about SQL, however, I need to nail down what I mean by the term database. Its meaning has changed as computers have changed the way people record and maintain information. COPYRIGHTED MATERIAL Keeping Track of Things Today, people use computers to perform many tasks formerly done with other tools. Computers have replaced typewriters for creating and modifying documents. They’ve surpassed electromechanical calculators as the best way to do math. They’ve also replaced millions of pieces of paper, file folders, and file cabinets as the principal storage medium for important information. Compared to those old tools, of course, computers do much more, much faster — and with greater accuracy. -
Chapter 9 – Designing the Database
Systems Analysis and Design in a Changing World, seventh edition 9-1 Chapter 9 – Designing the Database Table of Contents Chapter Overview Learning Objectives Notes on Opening Case and EOC Cases Instructor's Notes (for each section) ◦ Key Terms ◦ Lecture notes ◦ Quick quizzes Classroom Activities Troubleshooting Tips Discussion Questions Chapter Overview Database management systems provide designers, programmers, and end users with sophisticated capabilities to store, retrieve, and manage data. Sharing and managing the vast amounts of data needed by a modern organization would not be possible without a database management system. In Chapter 4, students learned to construct conceptual data models and to develop entity-relationship diagrams (ERDs) for traditional analysis and domain model class diagrams for object-oriented (OO) analysis. To implement an information system, developers must transform a conceptual data model into a more detailed database model and implement that model in a database management system. In the first sections of this chapter students learn about relational database management systems, and how to convert a data model into a relational database schema. The database sections conclude with a discussion of database architectural issues such as single server databases versus distributed databases which are deployed across multiple servers and multiple sites. Many system interfaces are electronic transmissions or paper outputs to external agents. Therefore, system developers need to design and implement integrity controls and security controls to protect the system and its data. This chapter discusses techniques to provide the integrity controls to reduce errors, fraud, and misuse of system components. The last section of the chapter discusses security controls and explains the basic concepts of data protection, digital certificates, and secure transactions. -
Data Modeling Guidelines for Nosql Document-Store Databases
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, No. 10, 2018 Data Modeling Guidelines for NoSQL Document-Store Databases Abdullahi Abubakar Imam1;a;b, Shuib Basri2;a, Rohiza Ahmad3;a, Junzo Watada4;a, Maria T. Gonzlez-Aparicio5;c, Malek Ahmad Almomani6;a aCIS Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31570, Perak, Malaysia bCS Department, Ahmadu Bello University, Zaria-Nigeria cComputing Department, University of Oviedo Gijon, Spain Abstract—Good database design is key to high data avail- [11], developer autonomy [1], [12] and inadequate modeling ability and consistency in traditional databases, and numerous guidelines [13], have posed numerous challenges in NoSQL techniques exist to abet designers in modeling schemas appropri- schema best-practice implementation. This has increasingly led ately. These schemas are strictly enforced by traditional database to erroneous database modeling and designs [1], [14], [15], engines. However, with the emergence of schema-free databases [16], [17], which defeats the notion of robustness in NoSQL (NoSQL) coupled with voluminous and highly diversified datasets databases and results in the production of low-performance, (big data), such aid becomes even more important as schemas in NoSQL are enforced by application developers, which requires a non-secure and less-durable systems. high level of competence. Precisely, existing modeling techniques and guides used in traditional databases are insufficient for big- For example, consider the security aspect of NoSQL data storage settings. As a synthesis, new modeling guidelines for document-oriented databases. The databases offer a query NoSQL document-store databases are posed. These guidelines language or an Application Program Interface (API) that has cut across both logical and physical stages of database designs.