QL: Object-Oriented Queries on Relational Data
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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. -
Declarative Languages for Big Streaming Data a Database Perspective
Tutorial Declarative Languages for Big Streaming Data A database Perspective Riccardo Tommasini Sherif Sakr University of Tartu Unversity of Tartu [email protected] [email protected] Emanuele Della Valle Hojjat Jafarpour Politecnico di Milano Confluent Inc. [email protected] [email protected] ABSTRACT sources and are pushed asynchronously to servers which are The Big Data movement proposes data streaming systems to responsible for processing them [13]. tame velocity and to enable reactive decision making. However, To facilitate the adoption, initially, most of the big stream approaching such systems is still too complex due to the paradigm processing systems provided their users with a set of API for shift they require, i.e., moving from scalable batch processing to implementing their applications. However, recently, the need for continuous data analysis and pattern detection. declarative stream processing languages has emerged to simplify Recently, declarative Languages are playing a crucial role in common coding tasks; making code more readable and main- fostering the adoption of Stream Processing solutions. In partic- tainable, and fostering the development of more complex appli- ular, several key players introduce SQL extensions for stream cations. Thus, Big Data frameworks (e.g., Flink [9], Spark [3], 1 processing. These new languages are currently playing a cen- Kafka Streams , and Storm [19]) are starting to develop their 2 3 4 tral role in fostering the stream processing paradigm shift. In own SQL-like approaches (e.g., Flink SQL , Beam SQL , KSQL ) this tutorial, we give an overview of the various languages for to declaratively tame data velocity. declarative querying interfaces big streaming data. -
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. -
Kodai: a Software Architecture and Implementation for Segmentation
University of Rhode Island DigitalCommons@URI Open Access Master's Theses 2017 Kodai: A Software Architecture and Implementation for Segmentation Rick Rejeleene University of Rhode Island, [email protected] Follow this and additional works at: https://digitalcommons.uri.edu/theses Recommended Citation Rejeleene, Rick, "Kodai: A Software Architecture and Implementation for Segmentation" (2017). Open Access Master's Theses. Paper 1107. https://digitalcommons.uri.edu/theses/1107 This Thesis is brought to you for free and open access by DigitalCommons@URI. It has been accepted for inclusion in Open Access Master's Theses by an authorized administrator of DigitalCommons@URI. For more information, please contact [email protected]. KODAI: A SOFTWARE ARCHITECTURE AND IMPLEMENTATION FOR SEGMENTATION BY RICK REJELEENE A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS OF MASTER OF SCIENCE IN COMPUTER SCIENCE UNIVERSITY OF RHODE ISLAND 2017 MASTER OF SCIENCE THESIS OF RICK REJELEENE APPROVED: Thesis Committee: Major Professor: Joan Peckham Lisa DiPippo Ruby Dholakia Nasser H Zawia DEAN OF GRADUATE COMMITTEE UNIVERSITY OF RHODE ISLAND 2017 ABSTRACT The purpose of this thesis is to design and implement a software architecture for segmen- tation models to improve revenues for a supermarket. This tool supports analysis of su- permarket products and generates results to interpret consumer behavior, to give busi- nesses deeper insights into targeted consumer markets. The software design developed is named as Kodai. Kodai is horizontally reusable and can be adapted across various indus- tries. This software framework allows testing a hypothesis to address the problem of in- creasing revenues in supermarkets. Kodai has several advantages, such as analyzing and visualizing data, and as a result, businesses can make better decisions. -
DARPA and Data: a Portfolio Overview
DARPA and Data: A Portfolio Overview William C. Regli Special Assistant to the Director Defense Advanced Research Projects Agency Brian Pierce Director Information Innovation Office Defense Advanced Research Projects Agency Fall 2017 Distribution Statement “A” (Approved for Public Release, Distribution Unlimited) 1 DARPA Dreams of Data • Investments over the past decade span multiple DARPA Offices and PMs • Information Innovation (I2O): Software Systems, AI, Data Analytics • Defense Sciences (DSO): Domain-driven problems (chemistry, social science, materials science, engineering design) • Microsystems Technology (MTO): New hardware to support these processes (neuromorphic processor, graph processor, learning systems) • Products include DARPA Program testbeds, data and software • The DARPA Open Catalog • Testbeds include those in big data, cyber-defense, engineering design, synthetic bio, machine reading, among others • Multiple layers and qualities of data are important • Important for reproducibility; important as fuel for future DARPA programs • Beyond public data to include “raw” data, process/workflow data • Data does not need to be organized to be useful or valuable • Software tools are getting better eXponentially, ”raw” data can be processed • Changing the economics (Forensic Data Curation) • Its about optimizing allocation of attention in human-machine teams Distribution Statement “A” (Approved for Public Release, Distribution Unlimited) 2 Working toward Wisdom Wisdom: sound judgment - governance Abstraction Wisdom (also Understanding: -
STORM: Refinement Types for Secure Web Applications
STORM: Refinement Types for Secure Web Applications Nico Lehmann Rose Kunkel Jordan Brown Jean Yang UC San Diego UC San Diego Independent Akita Software Niki Vazou Nadia Polikarpova Deian Stefan Ranjit Jhala IMDEA Software Institute UC San Diego UC San Diego UC San Diego Abstract Centralizing policy specification is not a new idea. Several We present STORM, a web framework that allows developers web frameworks (e.g., HAILS [4], JACQUELINE [5], and to build MVC applications with compile-time enforcement of LWEB [6]) already do this. These frameworks, however, have centrally specified data-dependent security policies. STORM two shortcomings that have hindered their adoption. First, ensures security using a Security Typed ORM that refines the they enforce policies at run-time, typically using dynamic (type) abstractions of each layer of the MVC API with logical information flow control (IFC). While dynamic enforcement assertions that describe the data produced and consumed by is better than no enforcement, dynamic IFC imposes a high the underlying operation and the users allowed access to that performance overhead, since the system must be modified to data. To evaluate the security guarantees of STORM, we build a track the provenance of data and restrict where it is allowed formally verified reference implementation using the Labeled to flow. More importantly, certain policy violations are only IO (LIO) IFC framework. We present case studies and end-to- discovered once the system is deployed, at which point they end applications that show how STORM lets developers specify may be difficult or expensive to fix, e.g., on applications diverse policies while centralizing the trusted code to under 1% running on IoT devices [7]. -
FOSS Philosophy 6 the FOSS Development Method 7
1 Published by the United Nations Development Programme’s Asia-Pacific Development Information Programme (UNDP-APDIP) Kuala Lumpur, Malaysia www.apdip.net Email: [email protected] © UNDP-APDIP 2004 The material in this book may be reproduced, republished and incorporated into further works provided acknowledgement is given to UNDP-APDIP. For full details on the license governing this publication, please see the relevant Annex. ISBN: 983-3094-00-7 Design, layout and cover illustrations by: Rezonanze www.rezonanze.com PREFACE 6 INTRODUCTION 6 What is Free/Open Source Software? 6 The FOSS philosophy 6 The FOSS development method 7 What is the history of FOSS? 8 A Brief History of Free/Open Source Software Movement 8 WHY FOSS? 10 Is FOSS free? 10 How large are the savings from FOSS? 10 Direct Cost Savings - An Example 11 What are the benefits of using FOSS? 12 Security 13 Reliability/Stability 14 Open standards and vendor independence 14 Reduced reliance on imports 15 Developing local software capacity 15 Piracy, IPR, and the WTO 16 Localization 16 What are the shortcomings of FOSS? 17 Lack of business applications 17 Interoperability with proprietary systems 17 Documentation and “polish” 18 FOSS SUCCESS STORIES 19 What are governments doing with FOSS? 19 Europe 19 Americas 20 Brazil 21 Asia Pacific 22 Other Regions 24 What are some successful FOSS projects? 25 BIND (DNS Server) 25 Apache (Web Server) 25 Sendmail (Email Server) 25 OpenSSH (Secure Network Administration Tool) 26 Open Office (Office Productivity Suite) 26 LINUX 27 What is Linux? -
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“. -
Multimedia Systems
MVC Design Pattern Introduction to MVC and compare it with others Gholamhossein Tavasoli @ ZNU Separation of Concerns o All these methods do only one thing “Separation of Concerns” or “Layered Architecture” but in their own way. o All these concepts are pretty old, like idea of MVC was tossed in 1970s. o All these patterns forces a separation of concerns, it means domain model and controller logic are decoupled from user interface (view). As a result maintenance and testing of the application become simpler and easier. MVC Pattern Architecture o MVC stands for Model-View-Controller Explanation of Modules: Model o The Model represents a set of classes that describe the business logic i.e. business model as well as data access operations i.e. data model. o It also defines business rules for data means how the data can be created, sotored, changed and manipulated. Explanation of Modules: View o The View represents the UI components like CSS, jQuery, HTML etc. o It is only responsible for displaying the data that is received from the controller as the result. o This also transforms the model(s) into UI. o Views can be nested Explanation of Modules: Controller o The Controller is responsible to process incoming requests. o It receives input from users via the View, then process the user's data with the help of Model and passing the results back to the View. o Typically, it acts as the coordinator between the View and the Model. o There is One-to-Many relationship between Controller and View, means one controller can handle many views. -
After the Storm After the Storm the Jobs and Skills That Will Drive the Post-Pandemic Recovery
After the Storm After the Storm The Jobs and Skills that will Drive the Post-Pandemic Recovery February 2021 © Burning Glass Technologies 2021 1 After the Storm Table of Contents Table of Contents 1. Executive Summary pg 3 2. Introduction pg 6 3. The Readiness Economy pg 14 4. The Logistics Economy pg 16 5. The Green Economy pg 19 6. The Remote Economy pg 22 7. The Automated Economy pg 24 8. Implications pg 27 9. Methodology pg 29 10. Appendix pg 30 By Matthew Sigelman, Scott Bittle, Nyerere Hodge, Layla O’Kane, and Bledi Taska, with Joshua Bodner, Julia Nitschke, and Rucha Vankudre ©© BurningBurning GlassGlass TechnologiesTechnologies 20212021 22 After the Storm Executive Summary 1 Executive Summary The recession left in the wake of the • These jobs represent significant fractions COVID-19 pandemic is unprecedented. of the labor market: currently 13% of The changes have been so profound that demand and 10% of employment, but fundamental patterns of how we work, in addition they are important inflection produce, move, and sell will never be the points for the economy. A shortage same. of talent in these fields could set back broader recovery if organizations can’t If the U.S. is going to have a recovery that cope with these demands. not only brings the economy back to where • Jobs in these new “economies” are it was but also ensures a more equitable projected to grow at almost double the future, it is crucial to understand what jobs rate of the job market overall (15% vs. and skills are likely to drive the recovery.