School of CS
Western Illinois University
IS 524
Amaravadi
Topics for the Final Exam
Management Support Systems—I
The trend towards increased decision making in organizations:
Business complexity, competition, increasing products/services, more competition, globalization.
Definition and characteristics of MSS:
Interactive, customizable, model based, intended for operational and mid-level decisions.
MSS evolution:
From TP and reporting systems…
Types of Decisions:
Structured vs Unstructured [class discussion of differences such as scope, frequency, decision impact,
solution known, unknown etc.]
Decision Making Styles:
Analytical (systematic problem solving process, focus on methods & models) vs Intuitive (non sequential,
focus on cues and soft information);
Individual Model of D.M. (IDC):
Intelligence, Design and Choice.
Organizational models of D.M. :
Rational, Bureaucratic, political, garbage can.
Biases in D.M.:
Bounded rationality, anchoring and adjustment, uncertainty avoidance, heuristics, constraints.
Definition and Overview of DSS:
Supports structured and semi-structured; basic concepts: decision, decision parameter, decision model,
decision utility.
Classical DSS Architecture:
Dialog management, Data management, Model management.
DSS Analysis capabilities:
What-if, optimization, sensitivity and goal seeking.
Classification of Models:
D.M. under certainty -- L.P., integer programming, non-linear programming models, graph models
D.M. under risk -- decision tree, Bayesian, Queing, dimulation, Markovian analysis
D.M. under uncertainty - causal models and influence diagrams, SAST.
Illustration of models
L.P. model of aircraft manufacturing
Case of the SS Kuniang
Causal model of stock variables
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Final Outline
DSS Applications and Extensions.
BI system,
Geographical information systems
Collaborative systems
Expert systems
Data mining/warehousing
BI system and Dashboard:
BI defined; Dashboard defined; BI characteristics: intuitive display, customizable, automatic updates,
info. from multiple sources.
BI architecture:
Workstation, internal and external databases
Dashboard vendors:
FYI
Collaborative systems:
Definition; uses: Strategic planning, reports, research, software development.
Collaborative systems provide support for:
Decision making, Document sharing, Design.
Advantages of Collaborative systems:
Supports 21st century, reduces travel, time savings, improves end products.
Social Networking:
Example Twitter, Facebook, Linked in;
Useful when social interaction and work interaction is combined.
Can be a marketing tool.
Management Support Systems—II
Data Warehouses:
Definition of a warehouse, DW environment and data life cycle.
Characteristics of a warehouse:
Subject-oriented, integrated, time-variant, non-volatile.
Design of warehouse:
Similar to databases, schema not different, designed for queries, converted to cube at run time.
W/H organization:
Star, snowflake, constellation.
Example of warehouse and data marts.
OLAP:
Definition of OLAP; aggregation and slice and dice
Dimensions and concept hierarchies.
Aggregation and cube organization
Multi-dimensional organization illustrations.
Cube organization (description)
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Final Outline
Data mining:
Definition of data mining;
Applications of Data mining:
FYI
Data mining as a step in Knowledge Discovery:
Cleaning and integration, data selection, data transformation, mining, evaluation and presentation –
knowledge as a final product.
OLAP vs Mining vs Warehousing
Comparison of OLAP vs mining vs warehousing.
Types of Analysis
sequence, association, classification and clustering
Sequence:
Time-series, Neural networks and Genetic Algorithms.
Associative analysis:
A-priori method
Classification:
Discriminant analysis, Bayesian Classification and others.
Probability:
Definition of probability, prior and conditional probabilities, Bayes theorem
Clustering:
Based on Euclidean distance, similar to centroid, trial and error, need to guess # of clusters
Artificial intelligence
Definition of AI [ignore milestones]
Early research:
Logic, Perceptrons, Chess & Blocks World.
Search strategies:
Breadth-first, depth-first, heuristic, hill-climbing.
Other branches of AI:
FYI
The test for machine intelligence:
Turing Test
Nature of intelligence:
Knowledge + reasoning power = intelligence
Knowledge and Nature of knowledge:
Declarative (facts, relationships among facts) vs procedural (procedures to do something or how to use
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Final Outline
knowledge)
Knowledge representation:
Logic, frames, scripts, semantic nets, rules.
Logic:
Predicates as symbols to relate objects, make assertions with predicates [class discussion];
Semantic nets:
Node & link notation, nodes represent concepts or values, links represent structure or describe an object
(attribute). Useful for relationships.
Frames:
Slot and filler, fillers can be values, is-a links, procedures; useful in classification.
Rules:
If-then format; if condition then action/conclusion, useful if knowledge is narrowly defined or has
conditions.
Branches of AI:
Natural language processing, robotics, vision/perception systems, expert systems, machine learning.
Expert System Architecture:
User interface, inference engine, knowledge base, knowledge acquisition subsystem.
Neural Network:
Neural net as a mathematical representation of the brain. Activation function.
Business applications of AI:
In marketing, production, accounting and finance.
Industrial applications of AI:
Driverless vehicles, facial recognition, crime prevention….
Knowledge Management
Drivers of KM:
Competitive pressures, need to avoid re-inventing the wheel, manage intellectual assets.
Difference between data and information:
Filtration, summarization, contextualization, categorization.
Difference between information and knowledge:
Interpretation, analysis, annotated, refined
Case of TI:
Manages more than 3,100 data sheets, 50 user guides, 400 application notes etc.
Defn. of KM:
Explicit management of organizational knowledge; tacit (implicit) and explicit knowledge; differences
between them.
KM Cycle:
Identify, generate, capture/codify, transfer/absorb, use/measure, define strategy.
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Expert identification:
Collect names of SMEs via questionnaires to department heads, chiefs, managers and team leaders.
Knowledge generation methods (‘soft’ methods):
Training, interviewing, de-briefing, war stories, COP.
Capture and codification (‘hard’ methods):
Social networking, document based, ontology based, AI based.
Document based systems:
Store knowledge as documents, simple approach, retrieval and maintenance issues.
Ontology based systems:
Examples of DAST and Dutch legal ontology.
AI based systems:
Use traditional schemes e.g. AEI-3: A system for storing administrative knowledge.
AEI-3 scheme:
Node types are classes and instances, link types are ‘S’ and ‘D’ links.
KM Assessment:
Purpose is to evaluate knowledge in subject areas.
Case studies
HP and Siemens
[Please note that there might be questions based on the case studies of Continental Airlines, Hewlett
Packard, Siemens, Rolls Royce]
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Final Outline
EXAMPLE TEST
PART I – EXAMPLE MULTIPLE CHOICE/TRUE FALSE (40 x 1 pt)
1.
During the design phase of the IDC model, the decision maker collects information on the
problem.
a. True
b. False
2.
Perceptrons are concerned with recognizing speech and written text.
a. True
b. False
3.
Which of the following is a model of decision making under certainty?
a. Linear programming
b. Decision tree
c. Bayesian Analysis
d. Causal models
4.
Decision problem and parameters are entered through this component of a DSS:
a. knowledge base
b. inference engine
c. dialog management
d. data management
5.
Which of these comes closest to the concept of a dimension for a warehouse?
a. schema of a warehouse
b. decision parameters
c. attributes whose values can vary
d. results of slice and dice
6.
A data mart can be considered a mini warehouse.
a. True
b. False
7.
Which of the following is an example of a search strategy in AI?
a. neural network
b. chess
c. heuristic
d. activation
8.
Tacit knowledge is usually visible and easily accessable.
a. True
b. False
9.
Some types of knowledge in AI cannot be represented.
a. True
b. False
10.
A cosmetics company is interested in making its ingredient information available online for each
type of product e.g. lipsticks, powder etc. It is known that some of these ingredients are common
across products. The type of representation most suitable for this application is.
a. frame
b. script
c. semantic net
d. neural net
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Answers for Section I
1F; 2F; 3a; 4c; 5c; 6T; 7c; 8F; 9a; 10c;
PART II – Example short answer questions (10 x 4pts)
In general, for short answer questions, you must base your answer on frameworks discussed in class,
perhaps extending/synthesizing or applying them to new situations in the process. Answers need to be
relevant to class discussions/powerpoints/notes i.e. please do not pull answers “off the top of your heads”.
1.
In what way is a spreadsheet an example of a DSS?
2.
Compare data mining capabilities with DBMS capabilities.
3.
A large cellular carrier wants to predict customers who might subscribe to its data plan. What
techniques are suitable to identify such customers? Briefly explain.
4.
Thessalonika Travels (TT) is an e-tailer that offers low cost vacation packages in the Mediterranean.
The marketing manager at TT wants to know if the company’s existing banner advertising on Yahoo
is an effective way of attracting prospective vacationers to the site. Based on analysis of site traffic,
he/she has estimated that out of every 1,000 visitors to Yahoo, 50 of them visit the TT site. Out of
this traffic (i.e. coming from the Yahoo site) 75% of the visitors are purchasers whereas in general
15% of all visitors for sites of this type are purchasers. Given that a visitor at TT is a buyer, what is
the probability that the individual is someone from the banner site? Assume also that out of every
1,000 visitors to TT, 40 are from the banner site. In your view then, is it worthwhile to use banner
advertising, given only the numbers and not costs?
5.
What BI concepts are covered in the Continental Case Study? – identify and explain.
6.
Discuss the technical feasibility and time frame for an artificial eye that can see clearly for a distance
of two miles (when the skies are clear and there are no obstructions).
7.
Identify the representation scheme you will use for the following items of knowledge providing
appropriate rationale and then model the knowledge using hypothetical examples:
a. listing components and sub-components of an automobile engine and their functions
b. lab policies (such as drives must be virus checked before being inserted etc.)
c. providing physical details of a workshop/training that you attended recently.
8.
What might be some challenges posed by a KM initiative in an organization?
9.
A car company makes three types of models, sports cars, suvs and sedans. The profit margins on
these are $5000, $1,000 and $600. The sports car requires 2,000 hours of labor while the SUV and
sedan require 1,500 and 1,000 respectively. There is a production limit of 40,000 hours of labor.
The sports car costs $15,000 in components while the suv and sedan cost $12,000 and $10,000
respectively. There is a budgetary restriction of $100,000 per month for components. The company
needs to know how many of each type of car it should make each month in order to maximize its
profits while fulfilling the constraints.
10. Draw a Causal Model, using correct notation, to show relationships between “stock market” (an
index such as Dow Jones), “oil price”, “housing starts” (new houses built) and “economy”. Briefly
explain your model. Make assumptions as necessary.
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11. In Orange County, 51% of the adults are males. (It doesn't take too much advanced mathematics to
deduce that the other 49% are females.) One adult is randomly selected for a survey involving credit
card usage.
a. what is the probability that the selected person is a male?
b. It is later learned that the selected survey subject was smoking a cigar. Also, 9.5% of males smoke
cigars, whereas 1.7% of females smoke cigars (based on data from the Substance Abuse and Mental
Health Services Administration). Use this additional information to find the probability that the
selected subject is a male
12. Items a…g are grocery store items purchased by customers as shown in the table below, By making
use of a-priori algorithm, identify the most frequently purchased pair(s) of items across all
customers. Use a minimum support of 2. Clearly illustrate all steps.
Customer
Items purchased
Isaac
George
Jennifer
Mark
Stephanie
Jessica
a, b, d,
a, c, e
a, c, d
a, c, e, f
e, f, g
b, c, f
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