LECTURE 09: INTERACTION PT. 2: COST October 19, 2015 SDS235: Visual Analytics Note: slide deck adapted from R. Chang.

LECTURE 09:

INTERACTION PT. 2: COST

October 19, 2015

SDS235:

Visual Analytics

Note: slide deck adapted from R. Chang

Announcements

• Thanks for your comments on the MSA! Keep an eye out for associated changes

• Office hours this week: Tuesday/Thursday by Appointment

• Today’s Guest Speaker: • Morganne Ray, LICSW

JRI GRIP Community Based Services Program

Activity: Real World Problems

• This Wednesday: first final project design session

• Before then, we need to: • Generate a bunch of potential project ideas• Identify areas of common interest• Figure out what resources we might need

• Material needed: a piece of paper and a writing utensil

Step 1: Write a quick description of data science-y problem at the top of the page, and write your 99 number at the bottom

Activity: Real World Problems

Step 2: Pass your description clockwise to the next person

Activity: Real World Problems

Step 3: Read the problem, and underneath the description, write a data source you think you’d need to be able to solve it

Activity: Real World Problems

Step 4: Fold over the top of the paper (leaving just your dataset visible), and pass it clockwise. Now repeat!

Activity: Real World Problems

For next class: Pick a Topic

Before class on Wednesday, please take a moment and write a quick Piazza post about your final project topic.

Please include:• A little about the domain• The problem(s) you're trying to solve / question(s) you're trying to answer• The audience• The data you’ll be using (if you know)

Not 100% sure? Try a couple and get some feedback! You’re free to change your mind later.

Recap: Questions to Ask Yourself

1. What is the goal of the analysis?

2. What kinds of operations do we need to enable?

3. How can the visualization support those operations?

1. What is the goal of the analysis?

2. What kinds of operations do we need to enable?

Recap: Yi, Kang, Stasko and Jacko (2007)

1. Select: mark something as interesting

2. Explore: show me something else

3. Reconfigure: show me a different arrangement

4. Encode: show me a different representation

5. Abstract/Elaborate: show me more or less detail

6. Filter: show me something conditionally

7. Connect: show me related items

Yi, J. S., ah Kang, Y., Stasko, J. T., & Jacko, J. A. (2007). Toward a deeper understanding of the role of interaction in information visualization. Visualization and Computer Graphics, IEEE Transactions on, 13(6), 1224-1231.

1. Select: mark something as interesting

2. Explore: show me something else

3. Reconfigure: show me a different arrangement

4. Encode: show me a different representation

5. Abstract/Elaborate: show me more or less detail

6. Filter: show me something conditionally

7. Connect: show me related items

Discussion

• Is this really a taxonomy of interactions, or is it a taxonomy of visualizations?

• Are the two separable?

• How do we we handle interaction on different visualization types?

Case Study: Brushing and Linking

Questions? Thoughts?

Interaction: Benefits and Costs

• So far, we’ve talked about interaction (at all levels) in terms of what it enables- Maintaining context- Supporting hypothesis generation- Etc.

• Question: are there any downsides? Costs?

• Put another way: how do we decide when it’s worth it?

Interaction Costs

Lam (2008) surveyed 484 papers, tried to break down “cost” into logical parts:

1. Decision costs to form goals

2. System-power costs to form system operations

3. Multiple input mode costs to form physical sequences

4. Physical-motion costs to execute sequences

5. Visual-cluttering costs to perceive state

6. View-change costs to interpret perception

7. State-change costs to evaluate interpretation

• How hard is it to decide where to start?• Human intuition: give me more choices!• Caveat: decisions require effort• As interfaces become more complex and display more

data points, users may need to decide to decide on- a subset of data- interface options

Decision costs ( goals)

System-power costs ( system operations)

• Once a person has decide on a question they want answered, how hard is it to translate it into logical operations?

• Deciding on the correct operation sequences may be difficult (especially for complex systems)

• When the set of available operations isn’t immediately clear, users may have expectations based on previous systems (!!)

Multiple input mode costs ( physical sequences)

• Given a sequence of logical actions, how hard is it to figure out how to perform them?

• Translating system operations to device operations may be difficult due to:- inconsistent mode operations on multiple views (e.g. zooming)- mode change with inadequate visual feedback (e.g. MS ribbons)- overloaded input controls (e.g. gesture-based interaction)

Physical-motion costs ( execute sequences)

• Once you’ve set a sequence of actions to perform, how hard is it to physically execute them?

• Fitts’ Law can estimate actions performed with a mouse

MT = a+b*log2(A/W +1)

where MT is average movement time,

A is distance between the two targets,

W is target width, and a and b are experiment constants.

Visual-cluttering costs ( perceive state)

• Given a visual representation, how hard is it to perceive the system state?

• Interaction such as mouse hovering can cause visual cluttering that makes state perception difficult.

View-change costs ( interpret perception)

• Given a sequence of two views, how hard is it to reorient after changing between them?

• Interactions usually result in view changes that requires re-interpretation based on expectations:

• Interpretation requires object association of: - temporal objects, as in zooming; - spatial objects, as in view coordination, and - local and global objects, as in navigation

State-change costs ( evaluate interpretation)

• Given a sequence of two logical states, how hard is it to reorient yourself (or get back to where you started)?

• Data analysis often requires reflection on multiple data views or analysis states

• Lack of refinding support may inhibit exploration.• Fisheye vs. coordinated frames

Total Cost (according to Lam)

t_cost(…)= cost(startup) +cost(filtering) +cost(decomposing_to_actions) +cost(translating_to_logical_actions) +cost(translating_to_physical_actions) +cost(executing_physical_actions) +cost(percieve_system_state) +cost(reorient_after_view_change) +cost(reorient_after_state_change)

Ouch.

Discussion: what can we control?

t_cost(…)= cost(startup) +cost(filtering) +cost(decomposing_to_actions) +cost(translating_to_logical_actions) +cost(translating_to_physical_actions) +cost(executing_physical_actions) +cost(percieve_system_state) +cost(reorient_after_view_change) +cost(reorient_after_state_change)

For next class

• Remember to post to Piazza re: project topics

• I’ll be organizing teams for some activities, so give me a heads up if you’re not going to be in class

• If you’re stuck, or if you need contact information for any of our guest speakers, let me know!