1. 1 SY DE 542 Design Phase 3 : Multi-Variate Constraints Configural & Mass Data Displays Feb 7, 2005 R. Chow Email: chow@mie.utoronto.cachow@mie.utoronto.ca

2. 2 Multivariate Constraints Relationships between 2 or more variables May be at same abstraction level May be across levels Often equations

3. 3 Identifying Multivariate Constraints Re-visit Variable List For each AH level, look: –within a level –across levels

4. 4 Example: Conservation In – Out = Stored Holds for Mass, Energy, Money, Information Also: People, Aircraft, Requests … As long as nothing is transformed! Which AH level? Think laws and principles …

5. 5 Example: Transformation If transformation occurs, identify defining relationship … Example: Food manufacturing ? Butter + ? Sugar + ? Flour = ? Cookies Which AH level? Relationships may be identified empirically (based on experiments or history)

6. 6 Constraints Across Levels Shows how low level elements work towards high level purposes Examples from DURESS: –(1) Mass from 2 feedwater streams –(2) Energy leaving reservoir –(3) Flow split (Vicente, 1999)

7. 7 Example (1): Mass from 2 Feedwater Streams MI1(t) = FA1(t) + FB1(t) MI1(t): Which AH level? FA1(t), FB1(t): Which AH level? MI1(t) = FI1(t) ?? Why should we be interested in MI1(t)?

8. 8 Example (2): Energy Leaving Reservoir EO1(t) = MO1(t) c p T1(t) EO1(t): Which AH level? MO1(t)? C p :? T1(t)? Why should we be interested in EO1(t)?

9. 9 Example (3): Flow Split FA1(t) = FVA(t) * VA1(t) VA1(t) + VA2(t) FA1(t), FVA(t): Which AH level? VA1(t), VA2(t): ? Why would FA1(t) not be equal to VA1(t)?

10. 10 Designing for Multivariate Constraints Visually show relationships between variables Eliminate / reduce need for real-time computation by user Eliminate / reduce need for real-time lookup (of data tables, other documentation) Show context for relationships

11. 11 Configural Displays Idea is display of information for larger systems Individual pieces of data interact in a more global relationship - “higher order relationship” Right mapping makes that relationship emerge

12. 12 Definitions Low level data: usually individual sensor data High level relation: a more global and general display of what the data means Emergent property or emergent feature: a pattern or shape that is created from the low level data, is recognizable and has meaning

13. 13 AH -> Design Phase 3 Bottom of abstraction hierarchy tells you what lower level data should be displayed Higher levels of the hierarchy tell you why those data are important, what relation has meaning Emergent feature must mean something to the task

14. 14 Examples Network health Network parameters Heat transfer efficiency T1, T2, T3, T4, water flow

15. 15 Configural/Separable/Integral? Separable –show each variable as a single output –equivalent to single sensor single indicator display (SSSI) –integration or higher level relations must be derived

16. 16 Integral Displays Show high level information but not low level information Low level information must be derived. NormalNot normal

17. 17 Configural Displays Arrange low level data into a meaningful form whole is greater than the sum of the parts based on principles of gestalt psychology

18. 18 Separable vs Configural Separable generally makes it easier to extract low level information –harder to integrate Configural makes it harder to extract low level information –easier to integrate

19. 19 Bar Graphs Can be configural and separable Each element can be separated Pattern can be configural

20. 20 Configural Displays Configural displays typically form an object Sometimes called object displays The emergent property is the shape of the object Emergent property can be found from your abstraction hierarchy

21. 21 Emergent Features Example two variables, x, y could map x*y only meaningful if area, x*y has meaning for the task good mapping x=mass, y=velocity, area=momentum x y

22. 22 Visual Mathematics Equality –Does x=y=z –horizontal line Addition –Does x+y=z xyz x y z

23. 23 Visual Mathematics Simple average –Does z=(x+y)/2 Multiplication –Does z=x*y xyz x y z

24. 24 Visual Mathematics Division –does z=x/y Mapping –x - vertical –y - horizontal –z - tan(Ø) Ø = tan -1 (z) x y Ø

25. 25 N-gon Feature Construction: Select key variables that measure overall status. Get normal values. Normalize x/xnormal. Determine alarm limits, colour coding. Normalization creates the shape.

26. 26 Another Polar Display Constant angle Variant radius Not configural Designed by Florence Nightingale

27. 27 Straight Line Feature Individual temperaturesVessel temp profile, vessel state

28. 28 Design Exercise You have been hired as an interface designer to work for Mrs. Field’s cookies. Mrs. Field’s cookie plant is aging and the company has realised that they are losing production and potential profits whenever cookies turn out flawed. Sugar (S kg), butter (B kg) and flour (F kg) are mixed to make dough which is then dropped onto a conveyor belt. The conveyor belt runs through an oven at temperature T and the finished cookies exit the oven. To make the best possible cookie, Mrs. Fields’ cookie research team has determined that the dough must consist of a consistent relation between the amounts of butter, sugar, and flour.. This is a general property of cookie dough which holds over all different kinds of cookies. Precisely, Butter = ½ sugar, Sugar = 1/3 flour

29. 29 Mass Data Displays Basic Idea: to show large amounts of data in a way that is quickly understood to show global patterns in data without hiding data capitalize on human pattern recognition abilities and visual perception give an overview, a feeling for the behaviour of the process

30. 30 Comparison with Configural Displays Both are suited for overviews both show global relations both can make it hard to separate data, get individual values

31. 31 Comparison with Configural Displays MDD typically handle larger amounts of data don’t form an object so much as a pattern both show elemental data and don’t hide data

32. 32 MDDs Are somewhat under-used in computer displays have been used for years in paper based displays similar to the idea of alarm lights in power plants “get a feeling” of system state analogous to sounds, e.g. hums

33. 33 General Principles Show each piece of data as a simple mark on the screen (graphic atom) Establish the mapping of the dynamics –what changes? –how does the mark change? –graphic atom level changes such as size, shape, colour

34. 34 General Principles Determine the arrangement of marks –what is the organization? –what is the mapping to location in the display space? –Possibilities Topological - follow system connections Type of Data - organize temps, pressures, etc. Frame of Reference, scaling.

35. 35 General Principles What is the pattern that should emerge? What does the display look like under different conditions? Separability: To what extent must the operator be able to extract the individual value?

36. 36 ABB displays Mass Data Display Plant graphs Plant mimic Polar Star

37. 37 ABB MDD Data are normal Data are deviating Mark is line Change is in angle Organisation is plant topology Data are normalised so normal=horizontal

38. 38 ABB MDD Normalisation adds context Not normal is more salient Faults “cascade” through plant Experimental results –fault detection 20 times faster

39. 39 ABB MDD Other marks they considered Circle Lines change in thickness

40. 40 The Daisy Wheel Website use (access and errors) Mark is the line between elements Clustering of lines shows information

41. 41 www.smartmoney.com/www.smartmoney.com/marketmap Mass Data Display for Financial Market Mark is rectangular shape, “Tile map” Varies in Colour to show Gains and Losses

42. 42 Ozone levels in LA, 10 years Technique: Coloured tiles

43. 43 Scatterplots

44. 44 Design Exercise It is estimated that Mrs. Field's produces 500,000 cookies a day. Each cookie is inspected for size, shape, and baking quality (undercooked, cooked, and overcooked). Design a mass data display for this situation. What sort of dimensions could you organize your display with? (Note: you don't have to show all 500,000 cookies)

45. 45 Next Week Guest Lecturer: Prof. Greg Jamieson EID for Petrochemical Processing Work Domain + Task Analysis Design and Evaluation No slides will be posted Submit final checkpoint to Munira by email Before Wed. Feb. 16, 5pm