DOCSLIB.ORG

Data Warehouse: an Integrated Decision Support Database Whose Content Is Derived from the Various Operational Databases

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
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. Data; information: Data consist of facts, text, and other multimedia objects, while information is data that have been processed in such a way that it can increase the knowledge of the person who uses it. Repository; database: A repository is a centralized storehouse for all data definitions, data relationships, and other system components, while a database is an organized collection of logically related data. Entity; enterprise data model: An entity is an object or concept that is important to the business, while an enterprise data model is a graphical model that shows the high-level entities for the organization and the relationship among those entities. WWW.BSSVE.IN www.bsscommunitycollege.in www.bssnewgeneration.in www.bsslifeskillscollege.in 3 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in Data warehouse; ERP system:Both use enterprise level data. Data warehouses store historical data at a chosen level of granularity or detail, and are used for data analysis purposes, to discover relationships and correlations about customers, products, and so forth that may be used in strategic decision making. ERP systems integrate operational data at the enterprise level, integrating all facets of the business, including marketing, production, sales, and so forth. Key Points: 1. Contrasting basic concepts 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 chapter introduction) is the process of extracting useful information from databases. Data; information. Data consist of facts, text, and other multimedia objects, while information is data that have been processed in such a way that it can increase the knowledge of the person who uses it. Repository; database. A repository is a centralized storehouse for all data definitions, data relationships, and other system components, while a database is an organized collection of logically related data. Entity; enterpriseWWW.BSSVE.IN data model. An entity is an object or concept that is important to the business, while an enterprise data model is a graphical model that shows the high-level entities for the organization and the relationship among those entities. Data warehouse; ERP system. Both use enterprise level data. Data warehouses store historical data at a chosen level of granularity or detail, and are used for data analysis purposes, to discover relationships and correlations about customers, products, and so forth that may be used in strategic decision making. ERP systems integrate operational data at the enterprise level, integrating all facets of the business, including marketing, production, sales, and so forth. www.bsscommunitycollege.in www.bssnewgeneration.in www.bsslifeskillscollege.in 4 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in 2. Database Server and Database Specified If you specify both a database server and a database, your application connects to the database server, which locates and opens the database. The dbname option establishes a connection to the default database server or to another database server in the DBPATH environment variable. It also locates and opens the specified database. (The same is true of the db_var option if this specifies only a database name.) If you specify only dbname, its database server is read from the DBPATH environment variable. The database server in the INFORMIXSERVER environment variable is always added before the DBPATH value. On UNIX, set the INFORMIXSERVER and DBPATH environment variables as the following example (for the C shell) shows: setenv INFORMIXSERVER srvA setenv DBPATH //srvB://srvC On Windows, choose Start > Programs > Informix > setnet32 from the Task Bar and set the INFORMIXSERVER and DBPATH environment variables: set INFORMIXSERVER = srvA set DBPATH = //srvA://srvB://srvC The next example shows the resulting DBPATH that your application uses: //srvA://srvB://srvC The application first establishes a connection to the database server that INFORMIXSERVER specifies. The database server uses parameters in the configuration file to locate the database. If the database does not reside on the default database server, or if the default database server is not online, the application connects to the next database server in DBPATH. In the previous example, that database server would be srvB 3. USER Clause The USER clause specifies information that is used to determine whether the application can access the target computer on a remote host. USER Clause: WWW.BSSVE.IN |--USER--+-'user_id'---+--USING--validation_var-----------------| '-user_id_var-' ELEMENT DESCRIPTION RESTRICTIONS SYNTAX user_id Valid login name See Restrictions on the User Quoted Identifier Parameter. String user_id_var Host variable that contains Must be a fixed-length character Language user_id data type; same restrictions as specific user_id www.bsscommunitycollege.in www.bssnewgeneration.in www.bsslifeskillscollege.in 5 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in validation_var Host variable that contains Must be a fixed-length character Language a valid password for login type. See Restrictions on the specific name in user_id or Validation Variable Parameter. user_id_var [Table 1] The USER clause is required when the CONNECT statement connects to the database server on a remote host. Subsequent to the CONNECT statement, all database operations on the remote host use the specified user name. In DB-Access, the USING clause is valid within files executed from DB-Access. In interactive mode, DB-Access prompts you for a password, so the USING keyword and validation_var are not used. Topic : The Database Development Process Topic Objective: At the end of this topic student will be able to: Understand the Development Process Basics Understand the Key Components of the Information Systems Architecture Understand the SystemWWW.BSSVE.IN Planning Understand the CASE and Repository Understand the Three-Schema Architecture. Definition/Overview: Information systems architecture (ISA): A conceptual blueprint or plan that expresses the desired future structure for the information systems in an organization. Systems development life cycle (SDLC): A traditional methodology used to develop, maintain, and replace information systems. www.bsscommunitycollege.in www.bssnewgeneration.in www.bsslifeskillscollege.in 6 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in Client/server architecture:: A local area network-based environment in which database software on a server (called a database server or database engine) performs database commands sent to it from client workstations, and application programs on each client concentrate on user interface functions. Incremental commitment:: A strategy in systems development projects in which the project is reviewed after each phase and continuation of the project is rejustified in each of these reviews. Enterprise data model:: The first step in database development, in which the scope and general contents of organizational databases are specified. WWW.BSSVE.IN www.bsscommunitycollege.in www.bssnewgeneration.in www.bsslifeskillscollege.in 7 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in Conceptual data model (or schema):: A detailed, technology-independent specification of the overall structure of organizational data. Logical data model
Load more

Recommended publications
  • Bi-Directional Transformation Between Normalized Systems Elements and Domain Ontologies in OWL
    Bi-directional Transformation between Normalized Systems Elements and Domain Ontologies in OWL Marek Suchanek´ 1 a, Herwig Mannaert2, Peter Uhnak´ 3 b and Robert Pergl1 c 1Faculty of Information Technology, Czech Technical University in Prague, Thakurova´ 9, Prague, Czech Republic 2Normalized Systems Institute, University of Antwerp, Prinsstraat 13, Antwerp, Belgium 3NSX bvba, Wetenschapspark Universiteit Antwerpen, Galileilaan 15, 2845 Niel, Belgium Keywords: Ontology, Normalized Systems, Transformation, Model-driven Development, Ontology Engineering, Software Modelling. Abstract: Knowledge representation in OWL ontologies gained a lot of popularity with the development of Big Data, Artificial Intelligence, Semantic Web, and Linked Open Data. OWL ontologies are very versatile, and there are many tools for analysis, design, documentation, and mapping. They can capture concepts and categories, their properties and relations. Normalized Systems (NS) provide a way of code generation from a model of so-called NS Elements resulting in an information system with proven evolvability. The model used in NS contains domain-specific knowledge that can be represented in an OWL ontology. This work clarifies the potential advantages of having OWL representation of the NS model, discusses the design of a bi-directional transformation between NS models and domain ontologies in OWL, and describes its implementation. It shows how the resulting ontology enables further work on the analytical level and leverages the system design. Moreover, due to the fact that NS metamodel is metacircular, the transformation can generate ontology of NS metamodel itself. It is expected that the results of this work will help with the design of larger real-world applications as well as the metamodel and that the transformation tool will be further extended with additional features which we proposed.
    [Show full text]
  • A Survey and Classification of Controlled Natural Languages
    A Survey and Classification of Controlled Natural Languages ∗ Tobias Kuhn ETH Zurich and University of Zurich What is here called controlled natural language (CNL) has traditionally been given many different names. Especially during the last four decades, a wide variety of such languages have been designed. They are applied to improve communication among humans, to improve translation, or to provide natural and intuitive representations for formal notations. Despite the apparent differences, it seems sensible to put all these languages under the same umbrella. To bring order to the variety of languages, a general classification scheme is presented here. A comprehensive survey of existing English-based CNLs is given, listing and describing 100 languages from 1930 until today. Classification of these languages reveals that they form a single scattered cloud filling the conceptual space between natural languages such as English on the one end and formal languages such as propositional logic on the other. The goal of this article is to provide a common terminology and a common model for CNL, to contribute to the understanding of their general nature, to provide a starting point for researchers interested in the area, and to help developers to make design decisions. 1. Introduction Controlled, processable, simplified, technical, structured,andbasic are just a few examples of attributes given to constructed languages of the type to be discussed here. We will call them controlled natural languages (CNL) or simply controlled languages.Basic English, Caterpillar Fundamental English, SBVR Structured English, and Attempto Controlled English are some examples; many more will be presented herein. This article investigates the nature of such languages, provides a general classification scheme, and explores existing approaches.
    [Show full text]
  • A Conceptual Framework for Constructing Distributed Object Libraries Using Gellish
    A Conceptual Framework for Constructing Distributed Object Libraries using Gellish Master's Thesis in Computer Science Michael Rudi Henrichs [email protected] Parallel and Distributed Systems group Faculty of Electrical Engineering, Mathematics and Computer Science Delft University of Technology June 1, 2009 Student Michael Rudi Henrichs Studentnumber: 9327103 Oranjelaan 8 2264 CW Leidschendam [email protected] MSc Presentation June 2, 2009 at 14:00 Lipkenszaal (LB 01.150), Faculty EWI, Mekelweg 4, Delft Committee Chair: Prof. Dr. Ir. H.J. Sips [email protected] Member: Dr. Ir. D.H.J. Epema [email protected] Member: Ir. N.W. Roest [email protected] Supervisor: Dr. K. van der Meer [email protected] Idoro B.V. Zonnebloem 52 2317 LM Leiden The Netherlands Parallel and Distributed Systems group Department of Software Technology Faculty of Electrical Engineering, Mathematics and Computer Science Delft University of Technology Mekelweg 4 2826 CD Delft The Netherlands www.ewi.tudelft.nl Sponsors: This master's thesis was typeset with MiKTEX 2.7, edited on TEXnicCenter 1 beta 7.50. Illustrations and diagrams were created using Microsoft Visio 2003 and Corel Paint Shop Pro 12.0. All running on an Acer Aspire 6930. Copyright c 2009 by Michael Henrichs, Idoro B.V. Cover photo and design by Michael Henrichs c 2009 http:nnphoto.lemantle.com All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without the prior permission of the author.
    [Show full text]
  • A Pattern-Based Approach to Ontology-Supported Semantic Data Integration
    A pattern-based approach to ontology-supported semantic data integration Andreas A. Jordan Wolfgang Mayer Georg Grossmann Markus Stumptner Advanced Computing Research Centre University of South Australia, Mawson Lakes Boulevard, Mawson Lakes, South Australia 5095, Email: [email protected] Email: (wolfgang.mayer|georg.grossmann)@unisa.edu.au, [email protected] Abstract RSM2, ISO 159263 and Gellish4 standards are all used to represent information about engineering assets and Interoperability between information systems is associated maintenance information. However, there rapidly becoming an increasingly difficult issue, in exists considerable variation in the scope and level of particular where data is to be exchanged with exter- detail present in each standard, and where the infor- nal entities. Although standard naming conventions mation content covered by different standards over- and forms of representation have been established, laps, their vocabulary and representation used vary significant discrepancies remain between different im- considerably. Furthermore, standards like ISO 15926 plementations of the same standards and between dif- are large yet offer little guidance on how to represent ferent standards within the same domain. Further- particular asset information in its generic data struc- more, current approaches for bridging such hetero- tures. Heterogeneities arising from continued evolu- geneities are incomplete or do not scale well to real- tion of standards have further exacerbated the prob- world problems. lem. As a consequence, tool vendors formally support We present a pragmatic approach to interoperabil- the standard, yet their implementations do not inter- ity, where ontologies are leveraged to annotate proto- operate due to different use of the same standard or typical information fragments in order to facilitate varying assumptions about the required and optional automated transformation between fragments in dif- information held in each system.
    [Show full text]
  • 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
    [Show full text]
  • Geographic Information Services
    Note: In order to continue to make this intellectual content available to the public, the National Transportation Library (NTL) copied the contents of this document from its original HTML file. The link to the original HTML file, which was last accessed on 2020-07-29, is https://ntlrepository.blob.core.windows.net/lib/4000/4200/4256/report.html NTL has preserved this content as it was presented in the HTML file, and has checked this document to ensure as complete a representation as possible. Questions about this file can be directed through the NTL Ask a Librarian service at: https://transportation.libanswers.com/form.php?queue_id=1810 _____________________________________________________________________________________ Geographic Information Services ON THE RESULTS OF A WORKSHOP ON GENERIC DATA MODEL FOR LINEAR REFERENCING SYSTEMS Authored by Workshop Participants with Alan Vonderohe (Principal Investigator Supported by The National Cooperative Highway Research Program Project) PREFACE The first draft of this report was prepared from notes and other materials developed during the workshop, audio tape recordings of the workshop sessions, and follow-up discussions with workshop participants. All participants were given the opportunity to review the first draft and provide responses. These responses were assimilated to identify both consensus revisions to the first draft and significant points of contention. The responders were informed of these results and asked to provide their opinions on each of the points of contention. The responders were also asked to provide measures of the relative importance of their positions on each of the points of contention (i.e., "critical", "strong preference", "weak preference"). The revised model presented in this second draft is true to the model as developed during the workshop and to the revisions on which there was consensus.
    [Show full text]
  • 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].
    [Show full text]
  • 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.
    [Show full text]
  • A Framework for GIS-T Data Sharing
    Portland State University PDXScholar Center for Urban Studies Publications and Reports Center for Urban Studies 6-2000 A Framework for GIS-T Data Sharing Kenneth Dueker Portland State University J. Allison Butler Follow this and additional works at: https://pdxscholar.library.pdx.edu/cus_pubs Part of the Transportation Commons, and the Urban Studies and Planning Commons Let us know how access to this document benefits ou.y Citation Details Dueker, Kenneth and Butler, J. Allison, "A Framework for GIS-T Data Sharing" (2000). Center for Urban Studies Publications and Reports. 13. https://pdxscholar.library.pdx.edu/cus_pubs/13 This Working Paper is brought to you for free and open access. It has been accepted for inclusion in Center for Urban Studies Publications and Reports by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. A Framework for GIS-T Data Sharing Kenneth J. Dueker Portland State University J. Allison Butler Hamilton County Tennessee December 1999 (Revised June 2000) Submitted to Transportation Research Part C: Emerging Technologies Center for Urban Studies Portland State University Portland OR 97207-0751 www.upa.pdx.edu/cus/ Abstract This paper develops a framework and principles for sharing of transportation data. The framework is intended to clarify roles among participants, data producers, data integrators, and data users. The principles are intended to provide guidance for the participants. Both the framework and the principles are based on an enterprise GIS-T data model that defines relations among transportation data elements. The data model guards against ambiguities and provides a basis for the development of the framework and principles for sharing of transportation data.
    [Show full text]
  • A CASE STUDY EXAMINATION DATA MODELLING in PRACTICE Paul
    A CASE STUDY EXAMINATION DATA MODELLING IN PRACTICE Paul Groves A report submitted in partial fulfilment of the requirements of the degree of Master of Commerce (Honours) to the University of New South Wales 1988 CERTIFICATION "I hereby declare that this submission is my own work and that, to the best of my knowledge and belief, it contains no material previously published or written by another person nor material which to a substantial extent has been accepted for the award of any other degree or diploma of a University or any other institute of higher learning, except where due acknowledgement is made in the text." ABSTRACT Data modelling for analysis and data base design is increasingly being viewed as a critical phase in the systems development process. This report is a comparative analysis of data modelling theory and practice. It investigates the nature of data and examines several data modelling methododologies. Current international standards for the conceptual data model are reviewed and based on this a reference framework is defined. This framework is used to compare four contemporary data modelling theories. Field testing of three of the methods is conducted, two case studies from a commercial environment and one from an academic setting. The case studies are conducted on a descriptive research basis. Results from the case studies confirm that data modelling represents a technique of growing impor­ tance in the systems development process. Increasing resources applied to the practice of relational database should ensure ensure ongoing theoretical interest and development. Although in the for­ mative stages of implementation and use, binary data modelling was seen to have achieved notable sucess in enhancing communication between project participants and in increasing user participation.
    [Show full text]
  • Universidad Carlos III De Madrid Escuela Politécnica Superior
    Universidad Carlos III de Madrid Escuela Politécnica Superior Ingeniería en Informática Proyecto Fin de Carrera DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE Autor: Verónica Casado Manzanero Tutor: Anabel Fraga Vázquez Diciembre, 2009 DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE Agradecimientos En primer lugar querría dar las gracias a mis padres, por su apoyo, comprensión, generosidad, por darme todo lo que necesito y más, y sobre todo, por enseñarme a ser como soy y servirme de ejemplo para convertirme en mejor persona cada día. También me gustaría recordar a mi hermana por ayudarme siempre en lo he necesitado con esa gran dosis de paciencia que sé que ha de tener. A mis amigas Carol y Raquel, por permanecer siempre a mi lado a pesar de estar semanas sin vernos. A mi novio Marcos, por pasarme esa paciencia y tranquilidad suya que tanto aprecio, por apoyarme en todo momento, por creer en mí y por permanecer a mi lado durante estos seis largos años. Por último, agradecerle a mi tutora Anabel toda la ayuda prestada, tanto en las asignaturas como en este proyecto. Gracias a su inestimable ayuda y entusiasmo he podido completar con éxito el trabajo aquí propuesto. Ha sido un verdadero placer trabajar con ella. Verónica Casado Manzanero 3/254 Universidad Carlos III de Madrid DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE Verónica Casado Manzanero 4/254 Universidad Carlos III de Madrid DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE Contenido 1. Introducción y motivación .........................................................................................
    [Show full text]
  • 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
    [Show full text]