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IS 425

IS 425. Enterprise Information I LECTURE 4 Autumn 2004-2005 2004 Norma Sutcliffe. Agenda . Exercise HCI / Usability Engineering Data Mining Quiz. Exercise.

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IS 425

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  1. IS 425 Enterprise Information ILECTURE 4 Autumn 2004-20052004 Norma Sutcliffe

  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 performancetime and error • Learning and cognitionmental 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 • Idealizedprogression 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 • Activitiesnarratives of typical or critical services • Informationdetails about info provided • Interactiondetails 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. Session 4

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