DataBase Management Exam 1

Data

Raw facts that have not yet been processed

Information

Data that has been organized and given context to reveal meaning

Knowledge

Awareness and understanding of information that supports decision-making

Database

Shared, integrated structure that stores end-user data and metadata

End-user Data

Raw facts of interest to the end user

Metadata

Data about data; describes characteristics and relationships

DBMS (Database Management System)

Collection of programs that manage database structure and access

Advantages of DBMS

Improved data sharing, security, integration, consistency, access, decision-making, productivity

Single-user Database

Supports one user at a time

Multiuser Database

Supports multiple users simultaneously

Workgroup Database

Supports a small number of users or a department

Enterprise Database

Supports many users across departments

Centralized Database

Data stored at a single site

Distributed Database

Data spread across multiple sites

Cloud Database

Created and maintained using cloud services

General-purpose Database

Contains broad variety of data

Discipline-specific Database

Focuses on specific subject area

Operational Database

Supports day-to-day company operations

Analytical Database

Stores historical data and business metrics for decision making

Data Warehouse

Stores data optimized for decision support

OLAP (Online Analytical Processing)

Tools for retrieving, processing, and modeling warehouse data

Business Intelligence

Approach to capture and process business data for decision making

Structured Data

Data formatted for storage and use

Unstructured Data

Data in its raw state

Semistructured Data

Partially processed data

XML (Extensible Markup Language)

Language to represent data elements in text format

XML Database

Supports storage and management of XML data

NoSQL

Non-relational DBMS designed for high volume, variety, velocity of data

Database Design

Process of designing database structure to store and manage data

Well-designed Database

Facilitates management, ensures accuracy and valuable information

Poorly-designed Database

Causes errors and poor decision making

Structural Dependence

File access depends on file structure

Structural Independence

File structure can change without affecting access

Data Dependence

Programs must change when data storage changes

Data Independence

Storage can change without affecting programs

Data Redundancy

Unnecessary duplicate data storage

Data Anomaly

Error caused by inconsistent/duplicate data

Update Anomaly

Not all updates applied correctly

Insertion Anomaly

Cannot add data without other data present

Deletion Anomaly

Deleting data unintentionally removes important info

DBMS Functions

Dictionary management, storage management, performance tuning, security, multiuser access, backup, integrity, query languages

SQL

Standard query language used to access/manipulate database data

Disadvantages of DBMS

Higher costs, complexity, vendor dependence, frequent upgrades

Data Modeling

Process of creating a specific data model for a problem domain

Data Model

Simplified representation of real-world data

Entity

Person, place, thing, or event about which data is stored

Attribute

Characteristic of an entity

Relationship

Association among entities (1:M, M:N, 1:1)

Constraint

Restriction placed on data to ensure integrity

Business Rule

Precise description of a policy or principle guiding database design

Schema

Conceptual organization of entire database

Subschema

Portion of database seen by application programs

DML (Data Manipulation Language)

Language to manage and work with data

DDL (Data Definition Language)

Language to define schema components

Relational Model

Based on relations (tables of rows and columns)

Tuple

Row in a relation

RDBMS (Relational DBMS)

DBMS implementing relational model

ER Model (Entity Relationship Model)

Graphical representation of entities and their relationships

ERD (Entity Relationship Diagram)

Diagram showing entities, attributes, and relationships

Chen Notation

One ER diagram style

Crow’s Foot Notation

Common ER diagram style

UML Class Diagram

ER-style diagram from object-oriented design

OODM (Object-Oriented Data Model)

Combines data and relationships in objects

Object

Abstraction of a real-world entity

Class

Collection of similar objects

Method

Action performed by an object

Inheritance

Ability of objects to inherit attributes/methods from parent classes

OODBMS

Object-oriented DBMS

ERDM (Extended Relational Data Model)

Relational model extended with OO features

O/R DBMS

Object-relational DBMS combining relational with OO features

IoT (Internet of Things)

Web of connected devices exchanging and collecting data

Big Data

Movement to manage/analyze huge data; defined by 3Vs: Volume, Velocity, Variety

Hadoop

Open-source distributed storage and computation framework

HDFS (Hadoop Distributed File System)

Distributed file system for managing large data

MapReduce

API for fast distributed data analytics

External Model

End users’ view of the data environment

External Schema

Specific representation of an external view

Conceptual Model

Global, organization-wide view of the database

Conceptual Schema

High-level description of main data objects

Internal Model

Database representation as seen by DBMS, software-dependent

Internal Schema

Specific representation of the internal model

Physical Model

Lowest-level abstraction describing physical data storage

Logical Independence

Change internal model without affecting conceptual model

Physical Independence

Change physical model without affecting internal model

High-Quality Data

Improves decisions, satisfaction, innovation, productivity, compliance

Nine Characteristics of Quality Data

Accuracy, completeness, consistency, uniqueness, timeliness, relevancy, accessibility, clarity, value

Domain

Range of allowable values for an attribute

Record

Collection of attributes about an entity

File

Collection of entities

Schema (DBMS)

Defines tables, attributes, and relationships

Concurrency Control

DBMS ensures multiple users access data safely

QBE (Query by Example)

Visual approach to database queries

Data Dictionary

Detailed description of data stored in DB

Data Cleansing

Detects and corrects/deletes inaccurate, incomplete, irrelevant data

Enterprise Data Model

Organization-wide model defining entities, attributes, and rules

ER Diagram

Graphical model for entities, attributes, and relationships

Normalization

Process to eliminate data redundancy

Selecting (Relational Op)

Eliminating rows based on criteria

Projecting (Relational Op)

Eliminating columns in a table

Joining (Relational Op)

Combining tables through common attributes