CPSC 3375 Database Concepts I

Total Page:16

File Type:pdf, Size:1020Kb

CPSC 3375 Database Concepts I CPSC 3375 Spring 2014 CPSC 3375 Database Concepts I Spring 2014 Department of Computer Science University of Arkansas at Little Rock 2801 South University Avenue Little Rock, Arkansas 72204-1099 Class Hours: Tuesday and Thursday, 3:05 PM − 4:20 PM Classroom: EIT 325 Instructor: Dr. Chia-Chu Chiang Office: EIT 570 Office Hours: Tuesday 2:00 − 3:00 PM, Wednesday 11:00 AM – 12:00 PM, Thursday 2:00 – 3:00 PM, Other hours by appointment Telephone: (501) 569-8142 Fax: (501) 569-8144 E-mail: [email protected] Course Descriptions Introduction to database management systems concepts including data models; data languages, relational database theory, database design such as functional dependency and normalization, and databases concurrency and recovery. Course Objectives Upon successful completion of this course, students should be able to • analyze the requirements of a database application • understand the underlying theory of the relational data model • develop a conceptual E-R model and generate relational schemas from E-R diagrams • analyze the English description of a query and develop a relational database schema, identify the database tables and attributes for creating a database • formulate a database query using SQL and generate the query results • understand recovery and concurrency control mechanisms in database transaction processing • have hands-on experience of a database application Prerequisites CPSC 2380 (Introduction to Data Structures) CPSC 2310 (Discrete Mathematics) Text 1. Fundamentals of Database Systems, 6 th Ed., 2011 By Ramez Elmasri and Shamkant B. Navathe Addison-Wesley - 1 - CPSC 3375 Spring 2014 References 1. Understanding Relational Database Query Languages, 2001 By Suzanne W. Dietrich Prentice Hall 2. MySQL, 2010 By Paul DuBois Addison Wesley Course Outline PART 1 – Introduction to Databases Chapter 1 – Databases and Database Users 1.1 Introduction 1.2 An Example 1.3 Characteristics of the Database Approach 1.4 Actors on the Scene 1.5 Workers behind the Scene 1.6 Advantages of Using the DBMS Approach Chapter 2 – Database System Concepts and Architecture 2.1 Data Models, Schemas, and Instances 2.2 Three-Schema Architecture and Data Independence 2.3 Database Languages and Interfaces 2.4 The Database System Environment - 2 - CPSC 3375 Spring 2014 2.5 Centralized and Client/Server Architectures for DBMSs 2.6 Classification of Database Management Systems PART 2 – The Relational Data Model and SQL Chapter 3 – The Relational Data Model and Relational Database Constraints 3.1 Relational Model Concepts 3.2 Relational Model Constraints and Relational Database Schemas 3.3 Update Operations, Transactions, and Dealing with Constraint Violations Chapter 4 – Basic SQL 4.1 SQL Data Definition and Data Types 4.2 Specifying Constraints in SQL 4.3 Basic Retrieval Queries in SQL 4.4 INSERT, DELETE, and UPDATE Statements in SQL PART 3 − Conceptual Modeling and Database Design Chapter 7 – Data Modeling Using the Entity-Relationship (ER) Model 7.1 Using High-Level Conceptual Data Models for Database Design 7.2 A Sample Database Application 7.3 Entity Types, Entity Sets, Attributes, and Keys 7.4 Relationship Types, Relationship Sets, Roles, and Structural Constraints 7.5 Weak Entity Types 7.6 Refining the ER Design for the COMPANY Database 7.7 ER Diagrams, Naming Conventions, and Design Issues 7.9 Relationship Types of Degree Higher Than Two PART 3 – Conceptual Modeling and Database Design Chapter 9 – Relational Database by ER-to-Relational Mapping 9.1 – Relational Database Design Using ER-to-Relational Mapping PART 2 – The Relational Data Model and SQL Chapter 5 – More SQL: Complex Queries, Triggers, Views, and Schema Modification 5.1 More Complex SQL Retrieval Queries 5.2 Specifying Constraints as Assertions and Triggers 5.3 Views (Virtual Tables) in SQL Chapter 6 – The Relational Algebra and Relational Calculus 6.2 Relational Algebra Operations from Set Theory 6.1 Unary Relational Operations: SELECT and PROJECT 6.3 Binary Relational Operations: JOIN and DIVISION 6.4 Additional Relational Operations 6.5 Examples of Queries in Relational Algebra PART 6 – Database Design Theory and Methodology Chapter 15 – Basics of Functional Dependencies and Normalization for Relational Databases 15.1 Informal Design Guidelines for Relation Schemas 15.2 Functional Dependencies 15.3 Normal Forms Based on Primary Keys 15.4 General Definitions of Second and Third Normal Forms - 3 - CPSC 3375 Spring 2014 15.5 Boyce-Codd Normal Form PART 5 – Database Programming Techniques Chapter 13 – Introduction to SQL Programming Techniques 13.1 Database Programming: Techniques and Issues 13.2 Embedded SQL, Dynamic SQL, and SQLJ PART 9 – Transaction Processing, Concurrency Control, and Recovery Chapter 21 – Introduction to Transaction Processing Concepts and Theory 21.1 Introduction to Transaction Processing 21.2 Transaction and System Concepts 21.3 Desirable Properties of Transactions 21.4 Characterizing Schedules Based on Recoverability 21.5 Characterizing Schedules Based on Serizability Grading Projects 30% Assignments 10% Midterm Exam 30% Final (Comprehensive) 30% 90-100% A 80-89% B 70-79% C 60-69% D < 60% F Exams All of the exams will be closed book and note exams. No make-up exams will be given except for documented illness or personal emergency. To be eligible for make-up, you must notify the instructor prior to the time of the exam and provide documentation for the situation when arranging the make-up. Tentative Schedule Week Tuesday Lecture Thursday Lecture 1 1/14/2014 • Chapter 1 1/16/2014 • Chapter 1 2 1/21/2014 • Chapter 2 1/23/2014 • Chapter 2 3 1/28/2014 • Chapter 3 1/30/2014 • Chapter 3 4 2/4/2014 • Chapter 4 2/6/2014 • Chapter 4 5 2/11/2014 • Chapter 7 2/13/2014 • Chapter 7 6 2/18/2014 • Chapters 7 and 9 2/20/2014 • Chapter 9 7 2/25/2014 • Chapter 5 2/27/2014 • Chapter 5 8 3/4/2014 • Chapter 5 3/6/2014 • Chapter 5 9 3/11/2014 • Chapter 6 3/13/2014 • Midterm 10 3/18/2014 • Chapter 6 3/20/2014 • Chapter 6 11 3/25/2014 ••• Spring Break 3/27/2014 ••• Spring Break 12 4/1/2014 • Chapter 6 4/3/2014 • Chapter 6 13 4/8/2014 • Chapter 15 4/10/2014 • Chapter 15 14 4/15/2014 • Chapter 15 4/17/2014 • Chapter 13 - 4 - CPSC 3375 Spring 2014 15 4/22/2014 • Chapter 21 4/24/2014 • Chapter 21 16 4/29/2014 • Chapter 21 5/1/2014 • Chapter 21 17 5/6/2014 Consultation Day ••• Last Day to Drop an individual Class 3/11/2014 by 5 P.M. ••• Last Day to Withdraw from All Classes 5/5/2014 by 5 P.M. ••• Last Day of Classes 5/5/2014 ••• Consultation Day 5/6/2014 • Final on 5/13/2014 1:30 PM – 3:30 PM Policy The Department of Computer Science expects all students to adhere to UALR’s policy on Academic Honesty. In particular, a solution to an assignment or exam must be your own work. You are encouraged to discuss the assignment with your instructor, and even your classmates. However, anything you turn in for grading must be your own work. Students with Disabilities Your success in this class is important to me, and it is the policy and practice of the University of Arkansas at Little Rock to create inclusive learning environments consistent with federal and state law. If you have a documented disability (or need to have a disability documented), and need an accommodation, please contact me privately as soon as possible, so that we can discuss with the Disability Resource Center (DRC) how to meet your specific needs and the requirements of the course. The DRC offers resources and coordinates reasonable accommodations for students with disabilities. Reasonable accommodations are established through an interactive process among you, your instructor(s) and the DRC. Thus, if you have a disability, please contact me and/or the DRC, at 501-569-3143 (V/TTY) or 501-683-7629 (VP). For more information, please visit the DRC website at www.ualr.edu/disability . Class Blackboard Site http://bb.ualr.edu Program Outcome, Course Outcome, and Performance Criteria ◊ O5: Be capable of problem solving. • CO5: Students will be able to analyze a variety of database problems and map to a logical solution to the problems. − Understand , collect , and document user requirements including functional requirements and data requirements − Choose appropriate strategy to analyze the problems such as problem decomposition and abstractions − Develop an enterprise data model that reflects the organization's business requirements ◊ O6: Be capable of formulating algorithmic solutions to a wide variety of problems normally encountered in real-world computing and also in academe. • (CO6) Students will be able to formulate a logical data model of the database and map to a relational data model. − Identify entities, relationships, and attributes − Identify data integrity and security requirements − Develop and refine Entity-Relationship (ER) Model to capture data requirements − Implement a database using MYSQL - 5 - CPSC 3375 Spring 2014 ◊ O7: Be capable of expressing their algorithms in at least one of several of the most important computer languages currently in use in academic and local computing environments. • (CO7) Students will be able to specify the algorithms including relational algebra, tuple relational calculus, domain relational calculus, and SQL in database creation and data access. − Translate a schema into a database application in relational algebra, tuple relational calculus, domain relational calculus, and SQL in MySQL. − Develop a solution to a case study in database design starting from a requirements description, to its conceptual design, its relational data model implementation, and queries in SQL. ◊ O8: Have a sufficient overview of the main subject areas of computer science, so that they can make intelligent decisions as to their future consideration of graduate education.
Recommended publications
  • GRAPH DATABASE THEORY Comparing Graph and Relational Data Models
    GRAPH DATABASE THEORY Comparing Graph and Relational Data Models Sridhar Ramachandran LambdaZen © 2015 Contents Introduction .................................................................................................................................................. 3 Relational Data Model .............................................................................................................................. 3 Graph databases ....................................................................................................................................... 3 Graph Schemas ............................................................................................................................................. 4 Selecting vertex labels .............................................................................................................................. 4 Examples of label selection ....................................................................................................................... 4 Drawing a graph schema ........................................................................................................................... 6 Summary ................................................................................................................................................... 7 Converting ER models to graph schemas...................................................................................................... 9 ER models and diagrams ..........................................................................................................................
    [Show full text]
  • Data Definition Language (Ddl)
    DATA DEFINITION LANGUAGE (DDL) CREATE CREATE SCHEMA AUTHORISATION Authentication: process the DBMS uses to verify that only registered users access the database - If using an enterprise RDBMS, you must be authenticated by the RDBMS - To be authenticated, you must log on to the RDBMS using an ID and password created by the database administrator - Every user ID is associated with a database schema Schema: a logical group of database objects that are related to each other - A schema belongs to a single user or application - A single database can hold multiple schemas that belong to different users or applications - Enforce a level of security by allowing each user to only see the tables that belong to them Syntax: CREATE SCHEMA AUTHORIZATION {creator}; - Command must be issued by the user who owns the schema o Eg. If you log on as JONES, you can only use CREATE SCHEMA AUTHORIZATION JONES; CREATE TABLE Syntax: CREATE TABLE table_name ( column1 data type [constraint], column2 data type [constraint], PRIMARY KEY(column1, column2), FOREIGN KEY(column2) REFERENCES table_name2; ); CREATE TABLE AS You can create a new table based on selected columns and rows of an existing table. The new table will copy the attribute names, data characteristics and rows of the original table. Example of creating a new table from components of another table: CREATE TABLE project AS SELECT emp_proj_code AS proj_code emp_proj_name AS proj_name emp_proj_desc AS proj_description emp_proj_date AS proj_start_date emp_proj_man AS proj_manager FROM employee; 3 CONSTRAINTS There are 2 types of constraints: - Column constraint – created with the column definition o Applies to a single column o Syntactically clearer and more meaningful o Can be expressed as a table constraint - Table constraint – created when you use the CONTRAINT keyword o Can apply to multiple columns in a table o Can be given a meaningful name and therefore modified by referencing its name o Cannot be expressed as a column constraint NOT NULL This constraint can only be a column constraint and cannot be named.
    [Show full text]
  • A Dictionary of DBMS Terms
    A Dictionary of DBMS Terms Access Plan Access plans are generated by the optimization component to implement queries submitted by users. ACID Properties ACID properties are transaction properties supported by DBMSs. ACID is an acronym for atomic, consistent, isolated, and durable. Address A location in memory where data are stored and can be retrieved. Aggregation Aggregation is the process of compiling information on an object, thereby abstracting a higher-level object. Aggregate Function A function that produces a single result based on the contents of an entire set of table rows. Alias Alias refers to the process of renaming a record. It is alternative name used for an attribute. 700 A Dictionary of DBMS Terms Anomaly The inconsistency that may result when a user attempts to update a table that contains redundant data. ANSI American National Standards Institute, one of the groups responsible for SQL standards. Application Program Interface (API) A set of functions in a particular programming language is used by a client that interfaces to a software system. ARIES ARIES is a recovery algorithm used by the recovery manager which is invoked after a crash. Armstrong’s Axioms Set of inference rules based on set of axioms that permit the algebraic mani- pulation of dependencies. Armstrong’s axioms enable the discovery of minimal cover of a set of functional dependencies. Associative Entity Type A weak entity type that depends on two or more entity types for its primary key. Attribute The differing data items within a relation. An attribute is a named column of a relation. Authorization The operation that verifies the permissions and access rights granted to a user.
    [Show full text]
  • Shadow Theory: Data Model Design for Data Integration
    Shadow Theory: Data Model Design for Data Integration Jason T. Liu* (Draft. Date:9/12/2012) ABSTRACT In information ecosystems, semantic heterogeneity is known as General Terms Algorithms, Design, Human Factors, Theory. the root issue for the difficulties of data integration, and the Relational Model is not designed for addressing such challenges, i.e. to re-use data that is modeled from other sources in the local Keywords data model design. Although researchers have proposed many Data integration, information ecosystems, shadows, meanings, different approaches, and software vendors have designed tools to mental entity, semantic heterogeneity, tags, help the data integration task, it remains an art relying on human labors. Due to lacks of a comprehensive theory to guide the overall modeling process, the quality of the integrated data heavily depends on the data integrators’ experiences. 1. Introduction Based on observations of practical issues for enterprise customer Data integration is essential for most enterprise systems that rely data integration, we believe that the needed solution is a new data on data from multiple sources. Lenzerini defined data integration model. This new data model needs to manage the difficulties of as to combine data residing at different sources in order to provide semantic heterogeneity: different data collected from different users with a unified view of these data [1]. Specifically, the goal perspectives about the same subject matter can naturally be of Enterprise Information Integration (EII) is to provide a uniform inconsistencies or even in conflicts. In other words, existing data access to multiple data sources without having to load the data models are designed to support single version of the truth, and into a central place [2].
    [Show full text]
  • Datalog on Infinite Structures
    Datalog on Infinite Structures DISSERTATION zur Erlangung des akademischen Grades doctor rerum naturalium (Dr. rer. nat.) im Fach Informatik eingereicht an der Mathematisch-Naturwissenschaftlichen Fakultät II Humboldt-Universität zu Berlin von Herr Dipl.-Math. Goetz Schwandtner geboren am 10.09.1976 in Mainz Präsident der Humboldt-Universität zu Berlin: Prof. Dr. Christoph Markschies Dekan der Mathematisch-Naturwissenschaftlichen Fakultät II: Prof. Dr. Wolfgang Coy Gutachter: 1. Prof. Dr. Martin Grohe 2. Prof. Dr. Nicole Schweikardt 3. Prof. Dr. Thomas Schwentick Tag der mündlichen Prüfung: 24. Oktober 2008 Abstract Datalog is the relational variant of logic programming and has become a standard query language in database theory. The (program) complexity of datalog in its main context so far, on finite databases, is well known to be in EXPTIME. We research the complexity of datalog on infinite databases, motivated by possible applications of datalog to infinite structures (e.g. linear orders) in temporal and spatial reasoning on one hand and the upcoming interest in infinite structures in problems related to datalog, like constraint satisfaction problems: Unlike datalog on finite databases, on infinite structures the computations may take infinitely long, leading to the undecidability of datalog on some infinite struc- tures. But even in the decidable cases datalog on infinite structures may have ar- bitrarily high complexity, and because of this result, we research some structures with the lowest complexity of datalog on infinite structures: Datalog on linear orders (also dense or discrete, with and without constants, even colored) and tree orders has EXPTIME-complete complexity. To achieve the upper bound on these structures, we introduce a tool set specialized for datalog on orders: Order types, distance types and type disjoint programs.
    [Show full text]
  • A Theory of Regular Queries
    A Theory of Regular Queries Moshe Y. Vardi Rice University [email protected] ABSTRACT [22], he proposed using first-order logic as a declarative database A major theme in relational database theory is navigat- query language [23]. This led to the development of both ing the tradeoff between expressiveness and tractability for SEQUEL [16] and QUEL [54], as practical relational query query languages, where the query-containment problem is languages realizing Codd's idea, ultimately giving in 1986 considered a benchmark of tractability. The query class rise to the SQL standard [27], which has continued to evolve UCQ, consisting off unions of conjunctive queries, is a frag- over the past 30 years. ment of first-order logic that has a decidable query contain- Starting in the late 1970s, Codd's definition of first-order ment problem, but its expressiveness is limited. Extend- logic as \expressively complete" came under serious criti- ing UCQ with recursion yields Datalog, an expressive query cism [4], and various proposals emerged on how to extend language that has been studied extensively and has recently its expressive power [4, 6, 17], whose essence was to extend become popular in application areas such as declarative net- first-order logic with recursion, which was added to SQL in working. Unfortunately, Datalog has an undecidable query 1999 by way of common table expressions [29]. On the re- containment problem. Identifying a fragment of Datalog search side, the most popular relational language embodying that is expressive enough for applications but has a decid- recursion is Datalog [15], describe in more details below.
    [Show full text]
  • Database Theory
    DATABASE THEORY Lecture 1: Introduction / Relational data model Markus Krotzsch¨ TU Dresden, 4 April 2016 Course information • 14 lectures: Thursday, DS4 (13:00–14:30) • 13 excercise classes: Monday, DS3 (11:10–12:40) { taught by Francesco Kriegel • Oral examination (details based on applicable examination regulations) • Course homepage (dates, slides, excercise sheets): https://ddll.inf.tu-dresden.de/web/Database_ Theory_%28SS2016%29/en Markus Krötzsch, 4 April 2016 Database Theory slide 2 of 42 Aims of the course Obtain an understanding of key topics in database theory with a special focus on query formalisms: • Relational data model • Basic and advanced query languages • Expressive power of query languages • Complexity of query answering + some algorithmic approaches • Modelling with constraints Connect databases with other advanced topics in logic/KR/formal methods Markus Krötzsch, 4 April 2016 Database Theory slide 3 of 42 Literature, prerequisites, related courses • Serge Abiteboul, Richard Hull, Victor Vianu: Foundations of Databases. Addison-Wesley. 1994. – Available at http://webdam.inria.fr/Alice/ – Slight deviations in the lecture – Further literature will be given for advanced topics • Prerequisites: basics of first-order logic, Turing machines, worst-case complexity • Related courses at TUD: – Advanced Logic – Foundations of Semantic Web Technologies – Introduction to Logic Programming – Introduction to Constraint Programming – Datenbanken (Grundlagen) – Intelligent Information Systems Markus Krötzsch, 4 April 2016 Database
    [Show full text]
  • Database Theory
    DATABASE THEORY Lecture 11: Introduction to Datalog Markus Krotzsch¨ Knowledge-Based Systems TU Dresden, 12th June 2018 Announcement All lectures and the exercise on 19 June 2018 will be in room APB 1004 Markus Krötzsch, 12th June 2018 Database Theory slide 2 of 34 Introduction to Datalog Markus Krötzsch, 12th June 2018 Database Theory slide 3 of 34 Introduction to Datalog Datalog introduces recursion into database queries • Use deterministic rules to derive new information from given facts • Inspired by logic programming (Prolog) • However, no function symbols and no negation • Studied in AI (knowledge representation) and in databases (query language) Example 11.1: Transitive closure C of a binary relation r C(x, y) r(x, y) C(x, z) C(x, y) ^ r(y, z) Intuition: • some facts of the form r(x, y) are given as input, and the rules derive new conclusions C(x, y) • variables range over all possible values (implicit universal quantifier) Markus Krötzsch, 12th June 2018 Database Theory slide 4 of 34 Syntax of Datalog Recall: A term is a constant or a variable. An atom is a formula of the form R(t1, ::: , tn) with R a predicate symbol (or relation) of arity n, and t1, ::: , tn terms. Definition 11.2: A Datalog rule is an expression of the form: H B1 ^ ::: ^ Bm where H and B1, ::: , Bm are atoms. H is called the head or conclusion; B1 ^ ::: ^ Bm is called the body or premise. A rule with empty body (m = 0) is called a fact. A ground rule is one without variables (i.e., all terms are constants).
    [Show full text]
  • Database Theory in Practice: Learning from Cooperative Group Projects*
    DATABASE THEORY IN PRACTICE: LEARNING FROM COOPERATIVE GROUP PROJECTS* Suzanne W. Dietrich Susan D. Urban Department of Computer Science and Engineering Arizona State University BOX 875406 Tempe, AZ 85287-5406 U.S.A. [email protected] [email protected] ABSTRACT only because of the foundation it provides for research, but lhis paper describes the use of cooperative group learning also because it is a prerequisite for effective use of database concepts in support of an undergraduate database mrtnage- management systems [1]. Database practice is important ment course that emphasizes the theoretical and practical for preparing students to address the needs of real-world aspects of database application development. ‘Ihe course applications. There is. therefore, a wealth of both theoretical project is divided into three main phases, involving require- and practical material that must be covered within a typical ments analysis and conceptual design, relational database undergraduate course on database management systems. mapping and prototyping, and database system implemen- Another concern with respect to database education is the tation using Microsoft Access. The project deliverables overall approach to teaching database concepts, such as the are designed so that students not only develop a database use of cooperative learning [4] in the college classroom. In implementation, but also evaluate their design in terms of cooperative learning, in contrast to individual or competitive functional dependencies, normal forms, the lossless join learning, students work in small groups, helping each other property, and the dependency preservation property, thus learn the assigned material. Cooperative learning has advan- establishing the need for sound database design principles.
    [Show full text]
  • An Extension of the Relational Data Model to Incorporate Ordered Domains
    An Extension of the Relational Data Model to Incorporate Ordered Domains Wilfred Siu Hung Ng A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy o f th e University of London Department of Computer Science University College London 1, July 1997 ProQuest Number: 10055432 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. uest. ProQuest 10055432 Published by ProQuest LLC(2016). Copyright of the Dissertation is held by the Author. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code. Microform Edition © ProQuest LLC. ProQuest LLC 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, Ml 48106-1346 Abstract In this thesis, we extend the relational data model to incorporate partial orderings into data domains and present a comprehensive formalisation of the extended model. The main contributions of the thesis are that we show how and why such an extension can considerably improve the capabilities of capturing semantics in a wide spectrum of advanced applications such as tree-structured information, temporal information, in­ complete information, fuzzy information and spatial information, whilst preserving the elegance of the theoretical basis of the model. Within the extended model, we extend the relational algebra to the Partially Or­ dered Relational Algebra (FORA) and the relational calculus to the Partially Ordered Relational Calculus (PORC), respectively.
    [Show full text]
  • Foundations of Databases
    Foundations of Databases Foundations of Databases Serge Abiteboul INRIA–Rocquencourt Richard Hull University of Southern California Victor Vianu University of California–San Diego Addison-Wesley Publishing Company Reading, Massachusetts . Menlo Park, California New York . Don Mills, Ontario . Wokingham, England Amsterdam . Bonn . Sydney . Singapore Tokyo . Madrid . San Juan . Milan . Paris Sponsoring Editor: Lynne Doran Cote Associate Editor: Katherine Harutunian Senior Production Editor: Helen M. Wythe Cover Designer: Eileen R. Hoff Manufacturing Coordinator: Evelyn M. Beaton Cover Illustrator: Toni St. Regis Production: Superscript Editorial Production Services (Ann Knight) Composition: Windfall Software (Paul C. Anagnostopoulos, Marsha Finley, Jacqueline Scarlott), using ZzTEX Copy Editor: Patricia M. Daly Proofreader: Cecilia Thurlow The procedures and applications presented in this book have been included for their instructional value. They have been tested with care but are not guaranteed for any purpose. The publisher does not offer any warranties or representations, nor does it accept any liabilities with respect to the programs and applications. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and Addison-Wesley was aware of a trademark claim, the designations have been printed in initial caps or all caps. Library of Congress Cataloging-in-Publication Data Abiteboul, S. (Serge) Foundations of databases / Serge Abiteboul. Richard Hull, Victor Vianu. p. cm. Includes bibliographical references and index. ISBN 0-201-53771-0 1. Database management. I. Hull, Richard, 1953–. II. Vianu, Victor. III. Title. QA76.9.D3A26 1995 005.74’01—dc20 94-19295 CIP Copyright © 1995 by Addison-Wesley Publishing Company, Inc.
    [Show full text]
  • Day 1: Introduction to Database Theory and Design
    Outline The Database as a Concept and Tool The Database model and its Evolution Database Applications Day 1: Introduction to Database Theory and Design Database Theory and Design Tyler Peterson International Summer School on Language Documentation and Description Leiden University Centre for Linguistics, Leiden November 26, 2011 Database Theory and Design Day 1: Introduction to Database Theory and Design Outline The Database as a Concept and Tool The Database model and its Evolution Database Applications My Details: Tyler Peterson office: LUCL Van Wijkplaats 4 Room 205a telephone: 071-5272059 email: [email protected] [email protected] (for Google docs) office hours: Most afternoons until 18:00 I Please fill out the short survey, and don't hesitate to contact me! Database Theory and Design Day 1: Introduction to Database Theory and Design I Theory: Terminology used in database theory; Understanding the design features of a database (entities and attributes). I Design: Assessing your goals; planning a database on paper (Entity-Relationship diagrams); Best practices. I Cross tabulation: a front-line analytical tool. I Trigger{Target Database for Phonological Processes I Programma de Fonologia Experimental e Hist´orica I Databases vs. spreadsheets. Types of databases. I Different commercial and free database programs: advantages and limitations. I To familiarize you with the concepts in database theory and design. I Looking at the practical implemetation of an analytical database in MS Access. Outline The Database as a Concept and Tool The Database model and its Evolution Database Applications Goals for the Course: I Databases: what they are, what linguists can use them for.
    [Show full text]