Transcript PPT

“Diagnosis” of Data Overload
• What is the definition of data overload?
• Why is it so difficult to address?
• Why have technological advances failed to solve
it?
Characterizations of the Data Overload Problem
1) Clutter: too much data
- Reduce the number of data bits displayed
2) Workload: too many activities to do
- Have semi-autonomous “agents” do things for you
3) Finding the meaningful significance of data in context
- Visualizations with model-based abstractions to organize
the data and identify patterns
Data Availability Paradox
• More and more data is available, but our ability to interpret
data has not improved
• Everyone recognizes that greater access to data is good in
principle
• The sheer amount of data that is available challenges the
ability to find what is informative
“I would have liked to have thrown away the alarm panel. It
wasn’t giving us any useful information.”
-- Three Mile Island nuclear power plant operator (Kemeny, 1979)
Associated “Solutions” to Data Overload
1) Reducing the amount of data (to reduce clutter)
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Strong commitment: filtering
Weak commitment: filing
2) Agents to perform tasks for you (to reduce workload)
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3)
Strong commitment: summarizing
Weak commitment: structuring, sorting, prioritizing, seeding,
reminding, critiquing, notifying, searching
Highlighting the significance of data (put in context)
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Information visualization techniques: focus + context, navigation,
information layering, coordinating views
Syntactic, context-free approaches to data structuring/mining:
statistically-based clustering and labeling
Model-based, context-bound representation aiding
Why Data Overload is Difficult:
Context Sensitivity
The meaning of a piece of data depends on context,
where “context” refers to
• what else is going on
• what else could be going on
• what has gone on
• what the observer expects to happen
The significance of a piece of data depends on:
• other data
• how related data can vary with larger context
• the goals and expectations of the observer
• the state of the problem solving process and stance of
others
Typical Finesses to Avoid
the Context-Sensitivity Problem
Finesse - a limited adaptation that represents a workaround
rather than directly addressing a problem
1) the scale reduction finesse
– reduce available data
2) the global, static prioritization finesse
– only show what is “important”
3) the intelligent agent finesse
– the machine will compute what is important for you
4) the syntactic finesse
– use syntactic/statistical properties as cues to content
Summary of “Diagnosis” of Data Overload
• Definition of data overload?
– A condition where a domain practitioner, supported by artifacts and
other human agents, finds it extremely challenging to focus in on,
assemble, and synthesize the significant subset of data for the problem
context into a coherent assessment of a situation, where the subset of
data is a small portion of a vast data field
• Why is it so difficult to address?
– Context sensitivity
• Why have technological advances failed to solve it?
– Finessing context sensitivity