1. Current Issues or Challenges in Visual Analytics

3. A Promising and Active Area, but
Facing many challenges
Process aspect
How does an analyst work?
“Don’t make me think”  “Help me think quickly and effectively”
How do a group of analysts work?
Group dynamics + visual analytics
Data aspect
Scalability of data
Tools for small data sets may work for large data sets.
Quality of data
Data is often messy.
Model aspect
Only considering data attributes
Distributions, types, etc.
How about user interaction?
Knowledge aspect
What new knowledge to provide
What do they know already?

4. Process Aspect

5. Theory on Visual Analytics
Visual analytics process always involves complex problem-solving, decision-making activities
Tasks, cognition, etc.
Not just another user interface design
Users often need to switch among different levels of skills, routines, and knowledge

7. Theory on Visual Analytics
Visual analytics process always involves complex problem-solving, decision-making activities
Tasks, cognition, etc.
Not just another user interface design
Users often need to switch among different levels of skills, routines, and knowledge
However, we don’t have a good theory, yet!

8. Collaborative Visual Analytics
Analyzing big data often requires team work.
How to work effectively as a team?
Social aspects
Complicated issues.
Task aspects
How to make collaborative analytics successful?

9. Visual Analytics Tasks
Some tasks can be easily managed in a team.
e.g., Team research reports in IST 220
Some may not.
e.g., your team project
How to support such collaboration?
Information artifacts
Visualization tools

10. Team Knowledge Distributed expertise Bias
Divide tasks into subtasks
Integrate results from individual experts
Bias
People favor information they are familiar with and down- play information they don’t know much
How to make information analysis more objective?
An Example: Collaborative Visual Analytics in Emergency Management

11. Public Works Environmental Mass Care

12. CIVIL: Support Collaborative Visual Analytics in Emergency Management
Chatting Tool Sorting Table Aggregation Chart Contribution Timeline
Private Map Public Map
Let’s review the main components of this interface. Users can draw and comment on both private map and public map. The difference is things on the private map can only be seen by the creator, while on public map, all the collaborators can see the shared comments, sketches, and also the others’ cursor.
Users can sort the information based on a certain attribute in sorting table, aggregation chart compare different options, here are the four shelters. Contribution timeline shows individual contribution history.
All the items are color-coded by roles and hyperlinked to each other. By clicking one item on the aggregation chart, it will highlight the corresponding items on the map, the sorting table, and the timeline.
Most importantly, we found that this web-based design is valuable when people using different machines to work together. No complex installation is needed as long as you have a browser and flash.

13. Video Demo

14. Data Aspect

15. What Did You Do to understand and structure data for Your Project Data?

16. Data
Understanding data is usually a prerequisite for using algorithms and visualization tools
Social media data: who, what, how, etc.
Intelligence reports
It is a challenge to have a deep understanding of big data.
Appropriate tools to get good, representative snippet.
E.g., twitter data
Find unique, but crucial information

17. Structuring Data for Visualization
Data can be structured different from what a visualization tool can support, or totally unstructured.
Example: Social networks
Data formats

18. Data Format <?xml version="1.0" encoding="UTF-8"?>
graph [   node   [    id A   ]   node   [    id B   ]   node   [    id C   ] 
node 
  [    id D   ]   node   [    id E   ]  
edge   [    source B    target A   ]   edge   [    source C    target A   ]
  edge   [    source C    target B   ]
  edge   [    source C    target E   ]
  edge   [    source D    target E   ]
  edge   [    source D    target B   ] ]
<?xml version="1.0" encoding="UTF-8"?>
<gexf xmlns=" version="1.2">
<meta lastmodifieddate=" ">
<creator>Gexf.net</creator>
<description>An Network Example</description>
</meta>
<graph mode="static" defaultedgetype="directed">
<nodes>
<node id="0" label="A" />
<node id="1" label="B " />
<node id="2" label="C" />
<node id="3" label="D" />
<node id="4" label="E" />
</nodes>
<edges>
<edge id="0" source=“1" target=“0" />
<edge id=“1" source=“2" target=“0" />
<edge id=“2" source=“2" target=“1" />
<edge id=“3" source=“2" target=“4" />
<edge id=“4" source=“3" target=“3" />
<edge id=“5" source=“3" target=“2" />
</edges>
</graph>
</gexf>

19. Structuring Data for Visualization
Data can be structured different from what a visualization tool can support, or totally unstructured.
Example: Social networks
Data formats
Constructing social networks based on different sources
public forum activities (e.g., reddit)
messages
co-presence in an event
other social ties (e.g., hometown, school, race, etc.)

24. Model Aspect

25. Models/Algorithms Data-oriented Have various assumptions
Data types, distributions, ranges, etc.
Usually don’t consider interactivity with users
A black box model

28. An Analogy for You to Think
How much do you trust results from search engines? Considering the fake news stories, do you trust search engines more or less?

29. An Analogy for You to Think
Visual Analytics Online Search Raw data Sources on Internet Data models Indexing algorithms Results Search results

30. How Much Should We Trust Algorithms/Models?

31. Knowledge Aspect

32. Who Are Our Audiences? How much do they know?
Data, visualization tools, algorithms, etc.
Think about graphic user interfaces
Different kind of users
Expert users: efficiency, almost error-free
Novice users: easy to use
People’s prior knowledge may not be what you think.

34. Effective Communications
chart-type=bubbles

35. Summary

36. Adding Them Up New theoretical models on visual analytics
Individual work process
Group process
New design practices
Integrating data (data collectors), algorithms (algorithm designers), users (analysts), and audiences together.
Considering these stakeholders in the beginning
New system models
Complex data and algorithms: web-based services may not be good enough. May demand super computers.
Supercomputers are not service-oriented!

37. Wednesday I will talk to individual groups.
I expect all group members show up.
Show me your progress and status of visualization designs
Should have something right now!

38. Extra Credit Opportunity
Participating a study on gesture-based user interaction
1 point