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P1 capitulo 5

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    • 1. TecnologiasDe Informacion I Prof. Fdo. Edgar Diaz-Prado Depto de Informatica Universidad Regiomontana
    • 2. Chapter 5 DATA BASES &Data Warehouse Prof. Fdo. Edgar Diaz-Prado Depto de Informatica Universidad Regiomontana
    • 3. Chapter 5 Data Resource Management
    • 4. Why Study Data Resource Management?• Today’s business enterprises cannot survive or succeed without data and quality data about their internal operations and external environment.• Data at companies, is the blood!
    • 5. Data Resource ManagementDefinition:• A managerial activity that applies information systems technologies to the task of managing an organization’s data resources to meet the information´s needs of the business.
    • 6. Foundation Data Concepts• Character – single alphabetic, numeric or other symbol T, %, Ñ, 4, +• Field – group of related characters Lolita, Student, 34,290.45, 70-04-12
    • 7. Foundation Data Concepts • Entity – person, place, object or event • Attribute – characteristic of an entity • Relationship – the way two or more entities can be related or associatedCopyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved. 5-7
    • 8. Foundation Data Concepts Entidad Atributos • NumEmpleada – 242726 • Nombre – MaryJose Schoedra • Dirección – Sierra Barrada 20-C • Fecha-Nacimiento – 1990-05-29 • Puesto – Chef-A • Salario – 54,860.45Copyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved. 5-8
    • 9. Foundation Data Concepts – Relationship between Entities Is Employee of Employee CompanyCopyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved. 5-9
    • 10. Foundation Data Concepts • Database
    • 11. Data Vs Information• Data – a collection of facts made up of text, numbers and dates: Villareal 35000 7/18/86• Information - the meaning given to data in the way it is interpreted: Mr. Villareal is a sales person whose annual salary is $35,000 and whose hire date is July 18, 1986.
    • 12. Foundation Data ConceptsWhat is aDatabase?
    • 13. An Example of a Table (or File) Fields or AttributesRecords Name E-mail-Link Phone College Graff rgraff 392-3900 Pharmacy Harris bharris 392-5555 Medicine Ipswich zipswich 846-5656 PHHP
    • 14. Basic Database Concepts• Table Name: Barry Harris • A set of related College: Medicine records Tel: 392-5555x Record – A collection of data Name: Barry Harris College: Medicine about an individual item Tel: 392-5555x Field – A single item of data Name: Barry Harris common to all records
    • 15. Foundation Data Concepts • Record – collection of attributes that describe an entity • File – group of related records • Database – integrated and related collection of files.Copyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved. 5 - 15
    • 16. Entities and Relationships
    • 17. What is a Database Systems• Database: a very large, integrated collection of data.• Models a real-world enterprise • Entities (e.g., Doctors, patientes) • Relationships (e.g., The Doctor is attending patients) •
    • 18. What is a Database Systems Relationships
    • 19. ? Why Study Databases??Need for DB has exploded in the last years in many fields, such as: • Corporate: retail sector, customer relationship mgmt, supply chain mgmt, data warehouses, enterprise management, human resources, finance and accounting, etc. • Scientific: digital libraries, Human Genome project, NASA Mission to Planet Earth, physical sensors, grid physics network
    • 20. Labels of Abstraction Architecture of Data Bases Users• Views describe how users see the data.• Conceptual schema View 1 View 2 View 3 defines logical structure Conceptual Schema• Physical schema describes the files and Physical Schema indexes used. DB• (sometimes called the ANSI/SPARC model)
    • 21. Example: University Database• External Schema (View): • Course_info(cid:string, cname:string, cteacher: string)• Conceptual schema: View 1 View 2 View 3 • Students(sid: string, name: string, login: string, age: integer, gpa:real) Conceptual Schema • Courses(cid: string, cname:string, credits:integer) Physical Schema • Teachers(tid:string, tname:string, tdepart:string)• Physical schema (in physical DB): DB • Relations stored as unordered files. • Index on first column of Students.
    • 22. Data Independence• Applications insulated from how data is structured and View 1 View 2 View 3 stored.• Logical data independence: Protection from changes in Conceptual Schema logical structure of data. Physical Schema• Physical data independence: Protection from changes in physical structure of data. DB
    • 23. Database Systems: Years ago.
    • 24. Database Systems: Today From Friendster.com on-line tour
    • 25. Databases in action
    • 26. Types of DatabasesCopyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved. 5 - 26
    • 27. Types of Databases
    • 28. Types of Databases • Operational – store detailed data needed to support the business processes and operations of a companyCopyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved. 5 - 28
    • 29. Types of Databases • Distributed – databases that are replicated and-or distributed in whole or in part to network servers at a variety of sitesCopyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved. 5 - 29
    • 30. Types of Databases • External – contain a wealth of information available from commercial online services and from many sources on the World Wide Web • Hypermedia – consist of hyperlinked pages of multimediaCopyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved. 5 - 30
    • 31. Hypermedia Database
    • 32. Data Warehouse Definition: • Large database that stores data that have been extracted from the various operational, external, and other databases of an organizationCopyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved. 5 - 32
    • 33. Data MartDefinition:• Databases that hold subsets of data from a data warehouse that focus on specific aspects of a company, such as a department or a business process
    • 34. Data Warehouse & Data Marts Data Mart Marketing Data Data MartWarehouse Production Data Mart sales
    • 35. Data Warehouse & Data Marts
    • 36. Chapter 5Copyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved. 5 - 36
    • 37. Chapter 5Copyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved. 5 - 37
    • 38. Chapter 5 End of Chapter´s First Part.Copyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved. 5 - 38