IS 425 Enterprise Information I LECTURE 4 Autumn 2004-2005 2004 Norma Sutcliffe.

IS 425

Enterprise Information ILECTURE 4

Autumn 2004-2005 2004 Norma Sutcliffe

IS425 Autumn 2004-2005 Norma Sutcliffe

Session 4 2

Agenda

Exercise

HCI / Usability Engineering

Data Mining

Quiz


Session 4 3

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.


Session 4 4

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


Session 4 5

Usability

Three distinct, complementary perspectives contribute: Human performance

time and error Learning and cognition

mental models of plans and actions

Collaborative activitydynamics and workplace context


Session 4 6

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


Session 4 7

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


Session 4 8

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


Session 4 9

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


Session 4 10

Tradeoffs

Addressed by scenarios 5 mentioned in text


Session 4 11

Scenario-Based Usability Engineering

Overview Iterative Interleaved Idealized

progression


Session 4 12

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


Session 4 13

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


Session 4 14

Scenario Based Prototyping/Evaluation

Assumption – design ideas in scenarios continually evaluated using prototyping

Evaluation Formative – guides redesign Summative – system verification

“go/no-go” test


Session 4 15

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


Session 4 16

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.


Session 4 17

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


Session 4 18

Major Data Mining Technologies


Session 4 19

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)


Session 4 20

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


Session 4 21

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?


Session 4 22

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.

IS 425 Enterprise Information I LECTURE 4 Autumn 2004-2005 2004 Norma Sutcliffe.

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Transcript of IS 425 Enterprise Information I LECTURE 4 Autumn 2004-2005 2004 Norma Sutcliffe.