Mis Quiz 2

Front 1

Data Warehouse

Back 1

- A logical collection of information– gathered from many different operationaldatabases– that supports business analysis activities anddecision-making tasks

- The primary purpose of a data warehouse is– to aggregate information throughout anorganization– into a single repository for decision-making purposes

Front 2

Data Warehouse Fundamentals

Back 2

- Extraction, Transformation, and loading (ETL)- Aprocess that Extracts information from internal and externaldatabases– Transforms the information using a common setof enterprise definitions– Load the information into a data warehouse

-Data Mart– A subset of data warehouse particular to theneeds of a given business unit (finance,marketing, accounting, etc.)

Front 3

Information and Cleansing

Back 3

Information cleansing or scrubbing– a process that weeds out and fixes or discardsinconsistent, incorrect, or incomplete information

• Information cleansing activities

Front 4

Characteristics of High-Quality Information

Back 4

Accuracy , Completeness , Consistency , Uniqueness , timeliness

Front 5

Multidimensional Analysis

Back 5

Database contain information in a series of twodimensionaltables• In a data warehouse and data mart, informationis multidimensional, it contains layers of columnsand rows– Dimension: a particular attribute of information

Front 6

Cube

Back 6

common term for the representation ofmultidimensional information

Front 7

Business Intelligence

Back 7

applications andtechnologies used to gather, provide access to, andanalyze data and information to support decisionmakingefforts

Front 8

Business Intelligence 3 categories

Back 8

Operational/ Tactical/ Strategic

Front 9

Data mining

Back 9

the process of analyzing data toextract information not offered by the raw dataalone

Front 10

Data-mining tools

Back 10

– use a variety of techniquesto find patterns and relationships in largevolumes of information– Classification– Estimation– Affinity grouping– Clustering

Front 11

Data Mining Capabilities

Back 11

Cluster analysis , Association detection , Statistical analysis

Front 12

Cluster analysis

Back 12

A technique used to divide an information set intomutually exclusive groups

Front 13

Association detection

Back 13

reveals the degree to which variables are related andthe nature and frequency of these relationships in theinformation

Front 14

Statistical analysis

Back 14

Perform such functions as information correlations,distributions, calculations, and variance analysis

Front 15

Clustering Analysis 2

Back 15

Figuring out groups in a way that– The members in one group are as close to each other aspossible– and those in different groups are as far as possible.

• Segment customer information and identify behavioraltraits

Front 16

Association Detection (Market-Basket Analysis)

Back 16

Determines groups ofproducts that customerstend to purchase together

Front 17

Association Detection (Market-Basket Analysis )

Back 17

Cross selling , Up selling

Front 18

Cross selling:

Back 18

the action ofpersuading customers to purchasean additional product or servicethat is in relation to the productthey are already purchasing

Front 19

Up-selling:

Back 19

the action ofpersuading customers to purchasemore expensive items, upgradesor other add-ons in attempt tomake amore profitable sale.

Front 20

Statistical Analysis 2

Back 20

– performs such functions asinformation correlations, distributions,calculations, and variance analysis

Front 21

Forecast:

Back 21

predictions made on the basis oftime-series information

Front 22

Time-series information:

Back 22

time-stampedinformation collected at a particular frequency

Front 23

3 V’s of Big Data (IBM)

Back 23

Volume: Data quantity

Velocity: Data speed

Variety: Data Types

Front 24

How to mine precious information?

Back 24

Examining large amount of data

– Aggregation and Statistics

• Data warehouse and OLAP

– Indexing, Searching, and Querying

• Keyword based search• Pattern matching– Knowledge discovery• Data Mining• Statistical Modeling

– Identification of hidden pattern, unknown correlations

– Better business decisions/ Effective marketing,customer satisfaction, increased revenue

Front 25

Acquiring

Back 25

- Obtain data

– Cleanse data

– Organize and relate data

– Catalog data

Front 26

How Do BI Information Systems Support BI Activities?

Back 26

- Analyzing: Reporting & Data mining

-

Front 27

RFM analysis

Back 27

- Recency: How recently a customer purchased an item

– Frequency: How frequently she purchases an item

– Monetary value: How much she spends each time

Front 28

Interactive

Back 28

Users allowed to change both the analysis and structure ofthe report

OLAP – online analytical processing

– OLAP slice and dice

– Drill down

Front 29

How Do BI Information Systems Support BI Activities?

Back 29

Publishing– Visualization

Front 30

BI Success Factors

Back 30

• Right Focus– Have to have a clear business problem

• Right People– Employees have to have an ability to understanddata and information

• Right Technology– Not all software tools are right for you.

• Right Culture– Data-driven decision making should beacceptable through the organization.

Front 31

Collaboration Filtering

Back 31

Predict what movies/books/.. a person may be interested in,on the basis of– Past preferences of the person– Other people with similar past preferences– The preferences of such people for a new movie/book/

Front 32

Artificial Intelligence (AI)

Back 32

Simulates human thinking and behavior, such asthe ability to reason and learn. Its ultimate goalis to build a system that can mimic humanintelligence. (ex. AlphaGo)

Front 33

Categories of AI

Back 33

Expert systems– Neural networks– Genetic algorithms– Intelligent agents

Front 34

Types of Data

Back 34

Structured , unstructured ,

Front 35

Structured Data

Back 35

Information stored in databases (Main Frame/SQL Server,Oracle, Access, Excel)– all records have same format as defined in the relational schema• Rows and columns• Data is consistent, uniform– Data mining friendly

Front 36

Unstructured Data

Back 36

Refers to information that doesn’t reside in a traditional rowcolumndatabase (not structured into “cells”)– All those things that can’t be so readily classified.– Text and multimedia content, e-mail messages, graphic images,videos, streaming, web pages, PDF files, Social media data(text, blogs, tweets, comments, tags), user-generated contentreviews

Front 37

Text Mining

Back 37

application of data mining to textual documents– The process of deriving high-quality information from text– Leveraging text improve decisions and predictions

Front 38

Seven Types of Text Mining

Back 38

Search and Information Retrieval (IR) , Document Clustering , Document classification, Web mining , Information Extraction (IE):, Natural Language Processing (NLP)

Front 39

Web Mining

Back 39

Discover useful information or knowledge fromthe Web hyperlink structure, page content, andusage data

Front 40

Three types of web mining tasks

Back 40

– Web structure mining

– Web content mining

– Web usage mining

Front 41

Two main types of textual information

Back 41

– Fact and Opinions

Front 42

social network

Back 42

is a social structure of people, related(directly or indirectly) to each other through a commonrelation or interest

Front 43

Social network analysis (SNA)

Back 43

is the study of socialnetworks to understand their structure and behavior