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“Diagnosis” of Data Overload

“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

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“Diagnosis” of Data Overload

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  1. “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?

  2. 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

  3. 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)

  4. Associated “Solutions” to Data Overload • Reducing the amount of data (to reduce clutter) • Strong commitment: filtering • Weak commitment: filing 2) Agents to perform tasks for you (to reduce workload) • Strong commitment: summarizing • Weak commitment: structuring, sorting, prioritizing, seeding, reminding, critiquing, notifying, searching 3) Highlighting the significanceof data (put in context) • 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

  5. 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

  6. 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

  7. 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

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