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ITM > Chapter 6 > Flashcards

Chapter 6 Flashcards

1
Q

Bit

A

Smallest unit of data a computer can handle

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2
Q

Byte

A

Group of bit. Single charactere: letter, number, etc..

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3
Q

Field

A

Grouping of charactere into a word, a group of words, or a complete number / Columns in a relational DBMS

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4
Q

Record

A

A group of related fields, such as the student’s name, the course taken, the date, and the grade, it is a row in a relational DBMS

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5
Q

File

A

A Group of related records / A talbe in a relational DBMS

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6
Q

Database

A

Def 1: Group of related files Def 2: A collection of data organized to serve many applications efficiently by centralizing the data and managing redundant data

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7
Q

Entity

A

A person, place, thing, or event about which we store and maintain information

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8
Q

Attribute

A

Characteristic or quality describing a particular entity

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9
Q

Problems with traditional file environment

A
  • Data redundancy and Inconsistency - Program Data dependance - Lack of flexibility - Poor security - Lack of Data Sharing and Availability
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10
Q

Data Redundancy

A

The presence of duplicate data in multiple data files so that the same data are stored in more than one place or location

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11
Q

Data Inconsistency

A

The same attribute may have different values

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12
Q

Program–Data Dependence

A

The coupling of data stored in files and the specific programs required to update and maintain those files so that changes in programs require changes to the data

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13
Q

Lack of flexibility

A

A traditional file system can deliver routine scheduled reports after extensive programming efforts, but it cannot deliver ad hoc reports or respond to unanticipated information requirements in a timely fashion

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14
Q

Poor security

A

Because there is little control or management of data, access to and dissemination of information may be out of control

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15
Q

Database Management System (DBMS)

A

Software that permits an organization to centralize data, manage them efficiently, and provide access to the stored data by application programs It relieves the programmer or end user from the task of understanding where and how the data are actually stored by separating the logical and physical views of the data

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16
Q

Logical View

A

Presents data as they would be perceived by end users or business specialists

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17
Q

Physical View

A

Shows how data are actualy organized and structured on physical storage media

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18
Q

Relational DBMS

A

Represents data as a 2 dimentiona table called relations

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19
Q

Key Field / Primary Key

A

A field in a record that uniquely identifies instances of that record so that it can be retrieved, updated, or sorted

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20
Q

Foreign Key

A

a lookup fi eld to look up data about another relational database

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21
Q

3 Operations of the relational database

A
  • Select - Joint - Project
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22
Q

Select operation of a relational database

A

Creates a subset consisting of all records in the file that meet stated criteria

23
Q

Join operation of the relational database

A

Combines relational tables to provide the user with more information than is available in individual tables

24
Q

Project operation of a relational database

A

Creates a subset consisting of columns in a table, permitting the user to create new tables that contain only the information required

25
Q

Multimedia

A

The integration of two or more types of media such as text, graphics, sound, voice, full-motion video, or animation into a computer-based application

26
Q

object-oriented DBMS (OODBMS)

A

Better suited than simple DBSM to handle graphics-based or multimedia applications.It stores the data and procedures that act on those data as objects that can be automatically retrieved and shared

27
Q

object-relational DBMS

A

provide capabilities of both object-oriented and relational DBMS

28
Q

Data Definition

A

The component of a database management system that defines each data element as it appears in the database

29
Q

Data Dictionary

A

an automated or manual file that stores definitions of data elements and their characteristics

30
Q

data Manipulation Language

A

A language associated with a database management system that end users and programmers use to manipulate data in the database

31
Q

Structured Query Language (SQL)

A

The most prominent data manipulation language today

32
Q

Normalization

A

process of creating small, stable, yet flexible and adaptive data structures from complex groups of data

33
Q

Referential Integrity Rules

A

ensure that relationships between coupled tables remain consistent

34
Q

Entity-Relationship Diagram

A

A methodology for documenting databases illustrating the relationship between various entities in the database

35
Q

Data Warehouse

A

Database that stores current and historical data of potential interest to decision makers throughout the company. It consolidates and standardize information which can be easily accessed, but can no longer be altered. Also provides a range of ad hoc and standardized query tools, analytical tools, and graphical reporting facilities

36
Q

Data Mart

A

A subset of the data warehouse in which a summarized or highly focused portion of the organization’s data is put in a separate database for a specific population of users

37
Q

Online aAnalytical Processing (OLAP)

A

Supports multidimensional data analysis, enabling users to view the same data in different ways using multiple dimensions. Each aspect of information—product, pricing, cost, region, or time period—represents a different dimension

38
Q

Data mining

A

Data mining provides insights into corporate data that cannot be obtained with OLAP by finding hidden patterns and relationships in large databases and inferring rules from them to predict future behaviour. The types of information obtainable from data mining include associations, sequences, classifications, clusters, and forecasts

39
Q

Data mining Association

A

Occurrences linked to a single event

40
Q

Data mining Sequences

A

Findes relationships between events that are linked over time

41
Q

Data mining Classification

A

Recognizes patterns that describe the group to which an item belongs by examining existing items that have been classified and by inferring a set of rules

42
Q

Data Mining Clustering

A

Can discover different groupings within data

43
Q

Data Mining Forecasting

A

It uses a series of existing values to forecast what other values will be

44
Q

Predctive analytics

A

Use data mining techniques, historical data, and assumptions about future conditions to predict outcomes of events, such as the probability that a customer will respond to an offer or purchase a specific product

45
Q

Text Mining

A

extract key elements from large unstructured data sets, discover patterns and relationships, and summarize the information

46
Q

Web Mining

A

The discovery and analysis of useful patterns and information from the World Wide Web

47
Q

Database server

A

In a client/ server environment, the DBMS resides on a dedicated computer

48
Q

Information Policy

A

specifies the organization’s rules for sharing, disseminating, acquiring, standardizing, classifying, and inventorying information

49
Q

Data Administration

A

Is responsible for the specific policies and procedures through which data can be managed as an organizational resource

50
Q

Data Governance

A

Deals with the policies and processes for managing the availability, usability, integrity, and security of the data employed in an enterprise, with special emphasis on promoting privacy, security, data quality, and compliance with government regulations

51
Q

Database Administration

A

Refers to the more technical and operational aspects of managing data, including physical database design and maintenance

52
Q

Data Quality Audit

A

a structured survey of the accuracy and level of completeness of the data in an information system

53
Q

Data Cleansing

A

consists of activities for detecting and correcting data in a database that are incorrect, incomplete, improperly formatted, or redundant. It not only corrects errors but also enforces consistency

ITM (13 decks)

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