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 –
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Grew out of shared interest between
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Cognitive scientists
Computer scientists
Learning challenges of interactive systems
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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:
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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
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Design of the user interface
Requirements analysis
Envisioning the system
 Relies on use of:
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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
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Actions
Events
Task goals:
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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
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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
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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
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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
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Formative – guides redesign
Summative – system verification
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“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
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hidden relationships
patterns,
affiliations.
 Utilize tools of
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mathematics and
statistical testing
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Major Data Mining Technologies
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Data Mining Approaches & Aims
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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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