Cis Test 2

Front 1

AI

Back 1

artificial intelligence

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INTELLIGENT SYSTEM-

Back 2

various commercial applications of artificial intelligence

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ARTIFICIAL INTELLIGENCE (AI)-

Back 3

simulates human intelligence such as the ability to reason and learn

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ultimate goal of AI:

Back 4

the ability to build a system that can mimic human intelligence

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4 most common categories of AI:

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expert system
neural network
genetic algorithm
intelligent agent

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expert system-

Back 6

computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems

Front 7

neural network-

Back 7

attempts to emulate the way the human brain works

Front 8

fuzzy logic-

Back 8

a mathematical method of handling imprecise or subjective information

part of NEURAL NETWORK

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genetic algorithm-

Back 9

an artificial intelligent system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem

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intelligent agent-

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special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users

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**EIS**

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EXECUTIVE INFORMATION SYSTEMS

-a specialized DSS that supports senior level executives within the organization

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**most EIS's offer these capabilities: (3)**

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-consolidation
-drill-down
-slice-and-dice

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**CONSOLIDATION**-

Back 13

involves the aggregation of information and features simple roll ups to complex groupings of interrelated information

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**DRILL-DOWN**-

Back 14

enables users to get details, and details of deatils, of information

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**SLICE AND DICE**-

Back 15

looks at information from different perspectives

Front 16

**TPS**

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Transaction Processing Systems
-the basic business system that serves the operational level (analysts) in an oranization
-moving up through the organizational pyramid, users move from requiring transactional information to analytical information

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**OLTP**

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OnLine Transaction Processing

-the capturing of transaction and event information using technology to...
(1) process the info according to defined business rules
(2) store the info, and
(3) update existing info to reflect the new info

Front 18

**OLAP**

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OnLine Analytical Processing

the manipulation of information to create business intelligence in support of strategic decision making

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**DIGITAL DASHBOARD**-

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integrates info from multiple components and presents it in a unified display

Front 20

**DSS**-

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Decision Support System

-models info to support managers and business professionals during the decision-making process

Front 21

**3 quantitative models used by DSS's**:

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SENSITIVITY analysis
WHAT-IF analysis
GOAL-SEEKING analysis

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**SENSITIVITY ANALYSIS**-

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the study of the impact that changes in 1+ parts of the model have on other parts of the model

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**WHAT-IF ANALYSIS**-

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checks the impact of a change in an assumption on the proposed solution

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**GOAL-SEEKING ANALYSIS**-

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finds the inputs necessary to achieve a goal, such as a desired level of output

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**EIS is a type of ____

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DSS

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Data Mining

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the process of analyzing data to extract information not offered by the raw data alone

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To perform data mining, users need _____

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DATA MINING TOOLS

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DATA MINING TOOLS-

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uses a variety of techniques to find patterns and relationships in large volumes of information and infers rules that predict future behavior and guide decision making

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Data-mining software includes many forms of AI such as...(2)

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neural networks and
expert systems

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average organizational spending on data mining tools is....

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INCREASING

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Common forms of data-mining analysis capabilities:

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-Cluster analysis
-Association detection
-Statistical analysis

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CLUSER ANALYSIS

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a technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible

Front 33

CRM systems depend on cluster analysis to ______

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segment customer information and identify behavioral traits

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ASSOCIATION DETECTION

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

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MARKET BASKET ANALYSIS

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analyzes such items as Web sites and checkout scanner information to detect customers’ buying behavior and predict future behavior by identifying affinities among customers’ choices of products and services

part of ASSOCIATION DETECTION

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STATISTICAL ANALYSIS

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

Front 37

FORECAST

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predictions made on the basis of time-series information

part of STATISTICAL ANALYSIS

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TIME-SERIES INFORMATION

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time-stamped information collected at a particular frequency

part of STATISTICAL ANALYSIS

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what is DATA MINING according to the gartner group??

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“Process of discovering meaningful new CORRELATIONS, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques."

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FORENSIC ACCOUNTING-

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understanding what went wrong financially with a company

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SIX TASKS OF DATAMINING:

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-classification
-estimation
-prediction
-affinity grouping
-clustering
-description

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CLASSIFICATION-

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similar characteristics

attribtues of an object -->assign it to a class

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ESTIMATION-

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to INFER

-assign some continuoulsy value

ie, credit risk percentage

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PREDICTION

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expected future behavior

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AFFINITY GROUPING-

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if you try one product, your willing to try a different one

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CLUSTERING-

Back 46

group exhibiting some similarty

classes NOT defined beforehand

good if your not sure what you are looking for

Front 47

DESCRIPTION-

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characterize what has been discovered and try to explain the results

Front 48

SIX DATAMINING TECHNIQUES:

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-Market Basket Analysis
-Memory-Based Reasoning
-Cluster Detection
-Link Analysis
-Rule Induction
-Neural Networks

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MARKET BASKET ANALYSIS

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look for gorups of objects that frequently appear together

EX: friday nights, males who buy diapers are also likely to buy beer..... WHAT CUSTOMERS TEND TO BUY TOGETHER

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MEMORY-BASED REASONING

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use one data set to create a model from which predictions/assumptions can be made bout newly introduced objects

measuring SIMILARITY B/W PAIRS OF OBJECTS

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CLUSTER DETECTION

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divide a set of objects into a number of smaller, more "alike" groups

use of statistics- like the K-MEANS CLUSTERING TECHNIQUE

agglomerative clustering

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K-MEANS CLUSTERING TECHNIQUE-

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identify the exact middle of the clusters

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AGGLOMERATIVE CLUSTERING-

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start w/ all objects as thier own cluster, then merge the most similar

Front 54

LINK ANALYSIS

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look for and establish links b/w objects within a data set; characterize the weight associated w/ any link between two objects

uses GRAPH THEORY

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GRAPH THEORY-

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are some links stronger than others within a data set??

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RULE INDUCTION

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the DECISION TREE

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DECISION TREE-

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root node and other nodes
rules
associative rules (probability)

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ASSOCIATIVE RULES-

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probability

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NEURAL NETWORKS

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weighted input that results with a wighted output to other neurons

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NEURAL NETWORKS are used for...(3)

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classification
estimation and
prediciton

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UNDIRECTED/UNSUPERVISED techniques of datamining: (3)

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-association rules
-clustering analysis (includes k-mean clustering)
-market basket analysis

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DIRECTED/SUPERVISED techniques of datamining: (3)

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-classification (classification tree, logistic regression, neural network)
-estimation (regression, neural network)
-prediciton (classification tree, regression, neural network)

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_____ is the process of uncovering actionable intelligence from available data

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KNOWLEDGE DISCOVERY

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THE Approach to knowledge discovery can be either

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DIRECTED or UNDIRECTED

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The basis for all data mining activities is ____

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CORRELATION

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Correlation coefficient (R) (-1 to +1) shows...

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the strength and direction of the relationship

Front 67

even a weak correllation can be interesting if....

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it shows a trend over time

Front 68

METHODS USED TO DETERMINE CORRELATION: (6)

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-data element VS data element
-data element VS unit of time
-data element VS data element groups
-data element VS geogrpahy
-data element VS external trends
-data element VS demographics

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DATA ELEMENT vs DATA ELEMENT:

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consider analysis where amount of a sale is correlated to whether the sale is paid for in cash or with a credit card. When a sale is below a certain amount, it is found that the payment is made with cash. When the sale is over a certain amount, the payment is made with a credit card. When the sale is within a certain range, it may be paid for either way.

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DATA ELEMENT vs UNIT OF TIME:

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Consider airline flights throughout the year. The length of the flight and the cost of the flight can be correlated with the month of the year in which a passenger flies. Do people make more expensive trips in January? As the holidays approach, do people make shorter and less expensive trips?

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DATA ELEMENT vs DATA ELEMENT GROUPS:

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Does the purchase of automobiles correlate to the sale of large ticket items in general such as washers and dryers, television sets, and refrigerators? It has been verified that men who buy beer on Friday nights also by diapers.

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DATA ELEMENT vs GEOGRAPHY:

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The beer drinking habits of those living in the south versus those living in the southwest.

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DATA ELEMENT vs EXTERNAL TRENDS:

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Comparison of internal sales figures to industry-wide sales figures

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DATA ELEMENT vs DEMOGRAPHICS:

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Comparison of savings rate for those with a college education with those without a college education

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An infinite number of combinations of correlations can be calculated and explored. Some correlations are very revealing; however, others are just interesting and have no potential for exploitation. They are not _______!!

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actionable

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earliest known examples of data visualization:

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in LONDON during the 1854 CHOLERA EPIDEMIC

a map helped to i.d. the source of the disease ((a water pump))

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modern data visualization techniques grew from the twin technologies of ____ and ____ in the _____'s and _____'s.

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computer graphics and
high performance computeing in the 1970s and 1980s

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alternative input devices such as ___, ___, and ___ began to appear in the 1960s

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light pen
sketch pad
mouse

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in the 1970s, flight simulators became much more realistic when ___ replaced ___

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graphics replaced

film

Front 80

in the 1980s, data visualization grew more dynamic with applications liek the ____

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animation of Los Angeles smog patterns

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one of today's more useful types of data visualization is in ____

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SIMULATORS

both games and in practice


only way most of us will every fly an airplane

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b/c of data visualization, it is now both cheaper and safer to train commercial pilots on ____.

w/ good softward, pilots can be ___

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SIMULATORS.

placed in situations they may not ever see- until too late in the cockpit

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in the 1990s, rapid advances in _____ put data visualization EVERYWHERE

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CHIP TECHNOLOGY
(both at the CPU and the graphics processor)

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CONTROL ENVIRONMENT

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creating a culture of accountability by establishing a positive and supportive attitude toward improvement and the achievment of established program outcomes

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RISK ASSESSMENT

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performing comprehensive reviews and analyses of program operations to determine if risks exist and the nature and extend of the risks identified

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CONTROL ACTIVITIES

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taking actions to address identified risk areas and help ensure tha tmanagement's decisions and plans are carried out and program objectives are met

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INFORMATION AND COMMUNICATION

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using and sharing relevent, reliable, and timely financial and nonfinancial info in managing improper payment related activities

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MONITORING

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tracking improvement initiatives, over time, nd identifying additional actions needed to further improve program efficiency and effectiveness

Front 89

GOAL OF DATAMINING IN THE ARTICLE:

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to identify/manage improper payments

Front 90

THROTTLING-

Back 90

AKA FAIRNESS ALGORITHM-

how the company blaances the distribution of shipping requests across frequen use and infrequent use customers- infrequent use are given priority

NETFLIX

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ANALYTICS-

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the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact based management to drive decisions and actions

a subset of business intelligence

Front 92

BUSINESS INTELLIGENCE-

Back 92

set of technologies and processes that use data to understand and analyze business performance

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ANALYTICAL COMPETITORS-

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organizations that have selected one or a few distinctive capabilities on which to base their strategies, and have appplied extensive data, statistical and quantitiative analysis, and fact based decision making to support the selected capabilities