DATABASE PROCESSING Fundamentals, Design, and Implementation 15Th Edition
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Data Warehouse: an Integrated Decision Support Database Whose Content Is Derived from the Various Operational Databases
1 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in DATABASE MANAGEMENT Topic Objective: At the end of this topic student will be able to: Understand the Contrasting basic concepts Understand the Database Server and Database Specified Understand the USER Clause Definition/Overview: Data: Stored representations of objects and events that have meaning and importance in the users environment. Information: Data that have been processed in such a way that they can increase the knowledge of the person who uses it. Metadata: Data that describes the properties or characteristics of end-user data and the context of that data. Database application: An application program (or set of related programs) that is used to perform a series of database activities (create, read, update, and delete) on behalf of database users. WWW.BSSVE.IN Data warehouse: An integrated decision support database whose content is derived from the various operational databases. Constraint: A rule that cannot be violated by database users. Database: An organized collection of logically related data. Entity: A person, place, object, event, or concept in the user environment about which the organization wishes to maintain data. Database management system: A software system that is used to create, maintain, and provide controlled access to user databases. www.bsscommunitycollege.in www.bssnewgeneration.in www.bsslifeskillscollege.in 2 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in Data dependence; data independence: With data dependence, data descriptions are included with the application programs that use the data, while with data independence the data descriptions are separated from the application programs. Data warehouse; data mining: A data warehouse is an integrated decision support database, while data mining (described in the topic introduction) is the process of extracting useful information from databases. -
Chapter 2: Database System Concepts and Architecture Define
Chapter 2: Database System Concepts and Architecture define: data model - set of concepts that can be used to describe the structure of a database data types, relationships and constraints set of basic operations - retrievals and updates specify behavior - set of valid user-defined operations categories: high-level (conceptual data model) - provides concepts the way a user perceives data - entity - real world object or concept to be represented in db - attribute - some property of the entity - relationship - represents and interaction among entities representational (implementation data model) - hide some details of how data is stored, but can be implemented directly - record-based models like relational are representational low-level (physical data model) - provides details of how data is stored - record formats - record orderings - access path (for efficient search) schemas and instances: database schema - description of the data (meta-data) defined at design time each object in schema is a schema construct EX: look at TOY example - top notation represents schema schema constructs: cust ID; order #; etc. database state - the data in the database at any particular time - also called set of instances an instance of data is filled when database is populated/updated EX: cust name is a schema construct; George Grant is an instance of cust name difference between schema and state - at design time, schema is defined and state is the empty state - state changes each time data is inserted or updated, schema remains the same Three-schema architecture -
Data Models for Home Services
__________________________________________PROCEEDING OF THE 13TH CONFERENCE OF FRUCT ASSOCIATION Data Models for Home Services Vadym Kramar, Markku Korhonen, Yury Sergeev Oulu University of Applied Sciences, School of Engineering Raahe, Finland {vadym.kramar, markku.korhonen, yury.sergeev}@oamk.fi Abstract An ultimate penetration of communication technologies allowing web access has enriched a conception of smart homes with new paradigms of home services. Modern home services range far beyond such notions as Home Automation or Use of Internet. The services expose their ubiquitous nature by being integrated into smart environments, and provisioned through a variety of end-user devices. Computational intelligence require a use of knowledge technologies, and within a given domain, such requirement as a compliance with modern web architecture is essential. This is where Semantic Web technologies excel. A given work presents an overview of important terms, vocabularies, and data models that may be utilised in data and knowledge engineering with respect to home services. Index Terms: Context, Data engineering, Data models, Knowledge engineering, Semantic Web, Smart homes, Ubiquitous computing. I. INTRODUCTION In recent years, a use of Semantic Web technologies to build a giant information space has shown certain benefits. Rapid development of Web 3.0 and a use of its principle in web applications is the best evidence of such benefits. A traditional database design in still and will be widely used in web applications. One of the most important reason for that is a vast number of databases developed over years and used in a variety of applications varying from simple web services to enterprise portals. In accordance to Forrester Research though a growing number of document, or knowledge bases, such as NoSQL is not a hype anymore [1]. -
Modelling, Analysis and Design of Computer Integrated Manueactur1ng Systems
MODELLING, ANALYSIS AND DESIGN OF COMPUTER INTEGRATED MANUEACTUR1NG SYSTEMS Volume I of II ABDULRAHMAN MUSLLABAB ABDULLAH AL-AILMARJ October-1998 A thesis submitted for the DEGREE OP DOCTOR OF.PHILOSOPHY MECHANICAL ENGINEERING DEPARTMENT, THE UNIVERSITY OF SHEFFIELD 3n ti]S 5íamc of Allai]. ¿Hoot (gractouo. iHHoßt ¿Merciful. ACKNOWLEDGEMENTS I would like to express my appreciation and thanks to my supervisor Professor Keith Ridgway for devoting freely of his time to read, discuss, and guide this research, and for his assistance in selecting the research topic, obtaining special reference materials, and contacting industrial collaborations. His advice has been much appreciated and I am very grateful. I would like to thank Mr Bruce Lake at Brook Hansen Motors who has patiently answered my questions during the case study. Finally, I would like to thank my family for their constant understanding, support and patience. l To my parents, my wife and my son. ABSTRACT In the present climate of global competition, manufacturing organisations consider and seek strategies, means and tools to assist them to stay competitive. Computer Integrated Manufacturing (CIM) offers a number of potential opportunities for improving manufacturing systems. However, a number of researchers have reported the difficulties which arise during the analysis, design and implementation of CIM due to a lack of effective modelling methodologies and techniques and the complexity of the systems. The work reported in this thesis is related to the development of an integrated modelling method to support the analysis and design of advanced manufacturing systems. A survey of various modelling methods and techniques is carried out. The methods SSADM, IDEFO, IDEF1X, IDEF3, IDEF4, OOM, SADT, GRAI, PN, 10A MERISE, GIM and SIMULATION are reviewed. -
Ontology Transformation: from Requirements to Conceptual Model*
Justas Trinkunas, Olegas Vasilecas Ontology Transformation: from Requirements to .. SCIeNTIfIC PAPeRS, UNIVeRSITy of Latvia, 2009. Vol. 751 COMPUTER SCIENCE AND Information TECHNOLOGIES 52–64 P. Ontology Transformation: from Requirements to Conceptual Model* Justas Trinkunas1 and Olegas Vasilecas2, 3 1 Information Systems Research Laboratory, Faculty of Fundamental Sciences, Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius-40, Lithuania, [email protected] 2 Information Systems Research Laboratory, Faculty of Fundamental Sciences, Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius-40, Lithuania, [email protected] 3 Department of Computer Science, Faculty of Natural Sciences, Klaipeda University, Herkaus Manto 84, LT-92294 Klaipeda, [email protected] Information systems are increasingly complex, especially in the enormous growth of the volume of data, different structures, different technologies, and the evolving requirements of the users. Consequently, current applications require an enormous effort of design and development. The fast-changing requirements are the main problem in creating and/or modifying conceptual data models. To improve this process, we proposed to reuse already existing knowledge for conceptual modelling. In the paper, we have analysed reusable knowledge models. We present our method for creating conceptual models from various knowledge models. Keywords: transformation, ontology, data modelling. 1 Introduction Most of information systems analysts have at least once considered how many times they have to analyse the same problems, create and design the same things. Most of them ask, is it possible to reuse already existing knowledge? Our answer is yes. We believe that knowledge can and should be reused for information systems development. -
Enterprise Data Modeling Using the Entity-Relationship Model
Database Systems Session 3 – Main Theme Enterprise Data Modeling Using The Entity/Relationship Model Dr. Jean-Claude Franchitti New York University Computer Science Department Courant Institute of Mathematical Sciences Presentation material partially based on textbook slides Fundamentals of Database Systems (6th Edition) by Ramez Elmasri and Shamkant Navathe Slides copyright © 2011 1 Agenda 11 SessionSession OverviewOverview 22 EnterpriseEnterprise DataData ModelingModeling UsingUsing thethe ERER ModelModel 33 UsingUsing thethe EnhancedEnhanced Entity-RelationshipEntity-Relationship ModelModel 44 CaseCase StudyStudy 55 SummarySummary andand ConclusionConclusion 2 Session Agenda Session Overview Enterprise Data Modeling Using the ER Model Using the Extended ER Model Case Study Summary & Conclusion 3 What is the class about? Course description and syllabus: » http://www.nyu.edu/classes/jcf/CSCI-GA.2433-001 » http://cs.nyu.edu/courses/fall11/CSCI-GA.2433-001/ Textbooks: » Fundamentals of Database Systems (6th Edition) Ramez Elmasri and Shamkant Navathe Addition Wesley ISBN-10: 0-1360-8620-9, ISBN-13: 978-0136086208 6th Edition (04/10) 4 Icons / Metaphors Information Common Realization Knowledge/Competency Pattern Governance Alignment Solution Approach 55 Agenda 11 SessionSession OverviewOverview 22 EnterpriseEnterprise DataData ModelingModeling UsingUsing thethe ERER ModelModel 33 UsingUsing thethe EnhancedEnhanced Entity-RelationshipEntity-Relationship ModelModel 44 CaseCase StudyStudy 55 SummarySummary andand ConclusionConclusion -
International Standard Iso/Iec/ Ieee 31320-2
This is a previewINTERNATIONAL - click here to buy the full publication ISO/IEC/ STANDARD IEEE 31320-2 First edition 2012-09-15 Information technology — Modeling Languages — Part 2: Syntax and Semantics for IDEF1X97 (IDEFobject) Technologies de l'information — Langages de modélisation — Partie 2: Syntaxe et sémantique pour IDEF1X97 (IDEFobject) Reference number ISO/IEC/IEEE 31320-2:2012(E) © IEEE 1999 ISO/IEC/IEEE 31320-2:2012(E)This is a preview - click here to buy the full publication COPYRIGHT PROTECTED DOCUMENT © IEEE 1999 All rights reserved. Unless otherwise specified, no part of this publication may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying and microfilm, without permission in writing from ISO, IEC or IEEE at the respective address below. ISO copyright office IEC Central Office Institute of Electrical and Electronics Engineers, Inc. Case postale 56 3, rue de Varembé 3 Park Avenue, New York CH-1211 Geneva 20 CH-1211 Geneva 20 NY 10016-5997, USA Tel. + 41 22 749 01 11 Switzerland E-mail [email protected] Fax + 41 22 749 09 47 E-mail [email protected] Web www.ieee.org E-mail [email protected] Web www.iec.ch Web www.iso.org Published in Switzerland ii © IEEE 1999 – All rights reserved This is a preview - click here to buy the full publicationISO/IEC/IEEE 31320-2:2012(E) Foreword ISO (the International Organization for Standardization) and IEC (the International Electrotechnical Commission) form the specialized system for worldwide standardization. National bodies that are members of ISO or IEC participate in the development of International Standards through technical committees established by the respective organization to deal with particular fields of technical activity. -
Mapping Conceptual Models to Database Schemas
Chapter 4 Mapping Conceptual Models to Database Schemas David W. Embley and Wai Yin Mok 4.1 Introduction The mapping of a conceptual-model instance to a database schema is fun- damentally the same for all conceptual models. A conceptual-model instance describes the relationships and constraints among the various data items. Given the relationships and constraints, the mappings group data items to- gether into flat relational schemas for relational databases and into nested relational schemas for object-based and XML storage structures. Although we are particularly interested in the basic principles behind the mappings, we take the approach of first presenting them in Sections 4.2 and 4.3 in terms of mapping an ER model to a relational database. In these sections, for the ER constructs involved, we (1) provide examples of the constructs and say what they mean, (2) give rules for mapping the constructs to a relational schema and illustrate the rules with examples, and (3) state the underlying principles. We then take these principles and show in Section 4.4 how to apply them to UML. This section on UML also serves as a guide to applying the principles to other conceptual models. In Section 4.5 we ask and answer the question about the circumstances under which the mappings yield normalized relational database schemas. In Section 4.6 we extend the mappings beyond flat relations for relational databases and show how to map conceptual-model instances to object-based and XML storage structures. We provide pointers to additional readings in Section 4.7. Throughout, we use an application in which we assume that we are designing a database for a bed and breakfast service. -
Modeling Languages Part 2: Syntax and Semantics for IDEF1X97IDEF1X97 (IDEF(Idefobject)Object ) BS ISO/IEC/IEEE 31320-2:2012 BRITISH STANDARD
BSBS ISO/IEC/IEEEISO/IEC/IEEE 31320-2:2012 BSI Standards Publication Information technology — Modeling Languages Part 2: Syntax and Semantics for IDEF1X97IDEF1X97 (IDEF(IDEFobject)object ) BS ISO/IEC/IEEE 31320-2:2012 BRITISH STANDARD National foreword This British Standard is the UK implementation of ISO/IEC/IEEE 31320-2:2012. The UK participation in its preparation was entrusted to Technical Committee IST/15, Software and systems engineering. A list of organizations represented on this committee can be obtained on request to its secretary. This publication does not purport to include all the necessary provisions of a contract. Users are responsible for its correct application. © The British Standards Institution 2013. Published by BSI Standards Limited 2013 ISBN 978 0 580 81153 1 ICS 35.080 Compliance with a British Standard cannot confer immunity from legal obligations. This British Standard was published under the authority of the Standards Policy and Strategy Committee on 31 July 2013. Amendments issued since publication Date Text affected BS ISO/IEC/IEEE 31320-2:2012 INTERNATIONAL ISO/IEC/ STANDARD IEEE 31320-2 First edition 2012-09-15 Information technology — Modeling Languages — Part 2: Syntax and Semantics for IDEF1X97 (IDEFobject) Technologies de l'information — Langages de modélisation — Partie 2: Syntaxe et sémantique pour IDEF1X97 (IDEFobject) Reference number ISO/IEC/IEEE 31320-2:2012(E) © IEEE 1999 BS ISO/IEC/IEEE 31320-2:2012 ISO/IEC/IEEE 31320-2:2012(E) COPYRIGHT PROTECTED DOCUMENT © IEEE 1999 All rights reserved. Unless otherwise specified, no part of this publication may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying and microfilm, without permission in writing from ISO, IEC or IEEE at the respective address below. -
Define Conceptual Schema in Dbms
Define Conceptual Schema In Dbms Earthshaking Garv hydroplane early while Esteban always tub his liquidations oversimplified sorrily, he theologised so industrially. Devolution or tyrannical, Rinaldo never bottle-feed any stereochromy! Visitorial Barney sometimes politicises his cannibal dreadfully and landscapes so ago! In knowing the same data projects, while control module or schema conceptual. Implementation data model is used at local level. One badge to calculate cardinality constraints. Its functionality is its limit the number and entity occurrences associated in a relationship. Parallel database systems are used in the applications that chapter to query extremely large databases or a have people process an extremely large orchard of transactions per second. SALARY stretch a number. Data compression and data encryption techniques. Dynamic properties, for example, operations or rules defining new database states based on applied state changes. The scheduler is illicit for ensuring that concurrent operations on the database software without conflicting with me another. It also allows any differences in entity names, attributes names, attributes order, data types, and manual on, to pay determined. Thus, by inspecting the articulation set therefore a decomposition hypergraph, one immediately infers that certain MDs hold in on original relation. This is off very strong within which floor have developed our decomposition algorithm. Elementary FDs and multiple elementary MDs have and simple structure appealing to intuition and certain formal properties ACM Transactions on Database Systems. DOB is then date. Guarino is arguing for. We use cookies to hum the best possible experience equip our website. The information about the mapping requests among various schema levels are included in quality system catalog of DBMS. -
The Layman's Guide to Reading Data Models
Data Architecture & Engineering Services The Layman’s Guide to Reading Data Models A data model shows a data asset’s structure, including the relationships and constraints that determine how data will be stored and accessed. 1. Common Types of Data Models Conceptual Data Model A conceptual data model defines high-level relationships between real-world entities in a particular domain. Entities are typically depicted in boxes, while lines or arrows map the relationships between entities (as shown in Figure 1). Figure 1: Conceptual Data Model Logical Data Model A logical data model defines how a data model should be implemented, with as much detail as possible, without regard for its physical implementation in a database. Within a logical data model, an entity’s box contains a list of the entity’s attributes. One or more attributes is designated as a primary key, whose value uniquely specifies an instance of that entity. A primary key may be referred to in another entity as a foreign key. In the Figure 2 example, each Employee works for only one Employer. Each Employer may have zero or more Employees. This is indicated via the model’s line notation (refer to the Describing Relationships section). Figure 2: Logical Data Model Last Updated: 03/30/2021 OIT|EADG|DEA|DAES 1 The Layman’s Guide to Reading Data Models Physical Data Model A physical data model describes the implementation of a data model in a database (as shown in Figure 3). Entities are described as tables, Attributes are translated to table column, and Each column’s data type is specified. -
Managing Data Models Is Based on a “Where-Used” Model Versus the Traditional “Transformation-Mapping” Model
Data Model Management Whitemarsh Information Systems Corporation 2008 Althea Lane Bowie, Maryland 20716 Tele: 301-249-1142 Email: [email protected] Web: www.wiscorp.com Data Model Management Table of Contents 1. Objective ..............................................................1 2. Topics Covered .........................................................1 3. Architectures of Data Models ..............................................1 4. Specified Data Model Architecture..........................................3 5. Implemented Data Model Architecture.......................................7 6. Operational Data Model Architecture.......................................10 7. Conclusions...........................................................10 8. References............................................................11 Copyright 2006, Whitemarsh Information Systems Corporation Proprietary Data, All Rights Reserved ii Data Model Management 1. Objective The objective of this Whitemarsh Short Paper is to present an approach to the creation and management of data models that are founded on policy-based concepts that exist across the enterprise. Whitemarsh Short Papers are available from the Whitemarsh website at: http://www.wiscorp.com/short_paper_series.html Whitemarsh’s approach to managing data models is based on a “where-used” model versus the traditional “transformation-mapping” model. This difference brings a very great benefit to data model management. 2. Topics Covered The topics is this paper include: ! Architectures of Data