1. IS 425 Enterprise Information LECTURE 4 Winter 2006-2007

2. 2 Agenda Review/Discussion Exercise HCI / Usability Engineering Data Mining Quiz

3. 3 Pulling It Together 1. What kind of aptitude does a security engineer need? 2. What skills does a security engineer need? 3. What kind of aptitude does a software engineer need? 4. What skills does a software architect need? 5. Are they different?

4. 4 Exercise 1. 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. 2. 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.

5. 5 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

6. 6 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

7. 7 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

8. 8 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

9. 9 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

10. 10 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

11. 11 Tradeoffs Addressed by scenarios 5 mentioned in text

12. 12 Scenario-Based Usability Engineering Overview Iterative Interleaved Idealized progression

13. 13 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

14. 14 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

15. 15 Scenario Based Prototyping/Evaluation Assumption – design ideas in scenarios continually evaluated using prototyping Evaluation Formative – guides redesign Summative – system verification “go/no-go” test

16. 16 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

17. 17 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.

18. 18 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

19. 19 Major Data Mining Technologies

20. 20 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)

21. 21 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

22. 22 Personalization Uses information from tracking, mining and data analysis to customize a person’s interactions with a company’s products, services, Web site and employees Collaborative filtering (Recommender System) Compare a present user’s interests with those of past users to offer recommendations Rules-based personalization : based on the subjection of a user’s profile to set rules or assumptions. Excite.com (My excite start page

23. 23 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?