Transcript IS 425
IS 425
Enterprise Information I
LECTURE 4
Autumn 2004-2005
2004 Norma Sutcliffe
Agenda
Exercise
HCI / Usability Engineering
Data Mining
Quiz
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Exercise
Each team debates and comes up with the
tradeoffs between doing the risk analysis in
the management inception phase and doing it
in the deployment phase of a large scale IT
project.
Is it possible to do risk analysis on different
security threats at different times? If so, then
indicate which view/phase is best for threat.
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HCI – Usability Engineering
HCI –
Grew out of shared interest between
Cognitive scientists
Computer scientists
Learning challenges of interactive systems
Using them
Designing them
Usability –
The quality of a system with respect to:
Ease of learning
Ease of use
User satisfaction
Scope expands to cover social/organizational aspects of
systems development/use
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Usability
Three distinct, complementary perspectives
contribute:
Human performance
time and error
Learning and cognition
mental models of
plans and actions
Collaborative activity
dynamics and
workplace context
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Usability Engineering
Focus on
Design of the user interface
Requirements analysis
Envisioning the system
Relies on use of:
Iterative development
Tradeoff analysis resulting in design rationale
User Interaction Scenarios
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User Interaction Scenario
Describes behaviors and experiences of
actors
Has a plot – sequences of
Actions
Events
Task goals:
High-level is the primary goal of the scenario
Sub-goals are the lower-level goals
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User Interaction Scenarios
Stories about people and their activities
Elements
Setting –details that motivate/explain or starting state
Actors – humans interacting
Task goals – motivate actions
Plans – mental activity directed at converting goal into
a behavior
Evaluation – mental activity directed at interpreting
features of the situation
Actions – observable behavior
Events – external actions or reactions
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User Interaction Scenario
Analysis is to find those things that affect goal
achievement by
Aiding
Obstructing
Being irrelevant
Is type of Use Case which is:
More general
Includes multiple responses (not just one)
Intended to describe what system will do
Can specify the user-system exchanges for scenario
examination
Useful in Tradeoff analysis
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Tradeoffs
Addressed by scenarios
5 mentioned in text
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Scenario-Based Usability Engineering
Overview
Iterative
Interleaved
Idealized
progression
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Scenario Based Analysis Phase
Used to evoke reflection / discussion
Claims
Stimulate analysis and refinement
Lists important features of a situation
Lists impacts on users experiences
Organize / documents “what-ifs” for prioritizing
alternatives
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Scenario Based Design Phase
3 sub-stages of scenarios
Activities
narratives of typical or critical services
Information
details about info provided
Interaction
details of user action and feedback
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Scenario Based Prototyping/Evaluation
Assumption – design ideas in scenarios
continually evaluated using prototyping
Evaluation
Formative – guides redesign
Summative – system verification
“go/no-go” test
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Summary
Combination of structured development and
prototyping thru scenarios
Scenarios organize analysis of user needs
Scenarios help in uncovering tradeoffs
Major focus of development are tradeoff
analysis
Thru scenarios can develop measurable
usability objectives
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Data Mining
Definition – process by which analysts apply
technology to historical date (mining) to
determine statistically reliable relationships
between variables.
This lets data tell what is happening rather
than testing the validity of rigorous theory
against samples of data.
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Data Mining
Required – data warehouses with huge
volumes of information to access for finding
hidden relationships
patterns,
affiliations.
Utilize tools of
mathematics and
statistical testing
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Major Data Mining Technologies
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Data Mining Approaches & Aims
Directed – identify relationships between drivers and targets (DIR)
Undirected – tools unleashed on data with no guidance (UDIR)
Strategic Insight – tools that reduce data into a few key perceptions (HESI)
Just-In-Time – tools that analyze data as it arrives at the organization (JIT)
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Data Mining Technologies in Use
Clustering algorithms – group data on basis of similarity -- UDIR
Association analysis – used to assist sales –JIT
Visualization – graphical representation for easy digestion – JIT
Slice & dice – extract summary data quickly “on the fly” – DIR
Segmentation algorithms – group data by target – DIR
Forecasting algorithms – probability of future actions – DIR
Regression – finding the relationship between variables – HESI
Neural Nets – AI – more intensive analysis using linear, nonlinear and
patterned relationships to identify relationships – HESI
Optimization – uses output from other DM to find best strategy given –
HESI
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Insights
Who will HCI professionals interact with?
Who will DM professionals interact with?
What aptitudes are required of HCI
professionals?
What aptitudes are required of data mining
professionals?
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Quiz
Section 703 – DL students should download
the homework assignment from COL and
then complete on the form and then submit
on COL. Please note due date on COL.
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