MSU HB 337 - Database and EDP (3 pages)

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Database and EDP



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Database and EDP

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Goes into how data is processed in a computer. Types of databases is also covered.


Lecture number:
2
Pages:
3
Type:
Lecture Note
School:
Michigan State University
Course:
Hb 337 - Hospitality Information Sys
Edition:
1

Unformatted text preview:

HB 337 1st Edition Lecture 2 Today s Lecture Electronic Data Processing EDP Database Electronic Data Processing EDP Input Memory Process Output Advantages of EDP 1 Speed 2 Reliability 3 Accuracy 4 Retention 5 Control Signals Digital vs Analog o Digital is on of o Analog is adjustable o Computers can only recognize digital o Human voice is an analog signal robot monotonous voice would be digital Database Database A collection of interrelated data collection of bits Start with 1 s and 0 s these are called bits These notes represent a detailed interpretation of the professor s lecture GradeBuddy is best used as a supplement to your own notes not as a substitute 8 bits bite Bunch of characters or bites are called a Field A bunch of fields are called a record A bunch of records are called a file A bunch of files are called a database DEF an application for rapidly storing and retrieving data Flat Files Text Files that have no structured interrelationship 4 Types of Databases 1 Hierarchical 2 Network 3 Relational 4 Object oriented Hierarchical Database Parent Child relationship among data Starts at top moves downward Network Database Have circular less ordered relationships Not used very much today Relational Database Most common database today Organize in tables Flexible Object oriented database New form of database Multimedia and graphic based data Linking Tables 2 types of keys Primary Key unique identifier Foreign Key Connector Combines finals record and enable users to manage data Data Extraction Structured Query Language SQL A query format providing instructions for building and modifying the structure of a database or for modifying data stored in tables Data Storage Data Warehouse massive collection of enterprise data Data mart limited to a single division or line of business or functional area Data cube data segmentation for intense application analysis Data Mining Process of finding relationship in data o Profiling o Clustering o Targeting



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