From WikiViz
Jump to: navigation, search

Visualisation tasks defined by B. Shneiderman (1996)

  • Overview
  • Zoom
  • Filter
  • Details-on-demand
  • Relate
  • History
  • Extract

Visualisation tasks from Wehrend and Lewis (1990) (Cognitive tasks)

The task classification of Wehrend and Lewis (1990) is a low-level, domain-independent taxonomy of tasks that users might perform in a visual environment. Domain-independence allows generalizability. The Wehrend and Lewis classification consists of the following set of user actions.

  • identify
  • locate
  • distinguish
  • categorize
  • cluster
  • distribution
  • rank
  • compare with relations
  • compare between relations
  • associate
  • correlate

Task taxonomy by Zhou and Feiner (1998)

Zhou and Feiner (1998) have developed a visual task taxonomy. This taxonomy extends that of Wehrend and Lewis (1990) by defining additional tasks, by parameterizing the tasks, and by developing a set of dimensions by which the tasks can be grouped.

Zhou tasks.png

Low-level user analytic tasks defined by Amar et al. (2005) (Analytic task taxonomy)

  • Retrieve value. Given a set of specific cases, find attributes of those case.
  • Filter. Given some concrete conditions on attribute values, find data cases satisfying those conditions.
  • Compute derived value: Given a set of data cases, compute an aggregate numeric representation of those data cases.
  • Find extremum: Find data cases possessing an extreme value of an attribute over its range within the data set
  • Sort: Given a set of data cases, rank them according to some ordinal metric.
  • Determine range: Given a set of data cases and an attribute of interest, find the span of values within the se.
  • Characterize distribution: Given a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute interest values over the set.
  • Find anomalies: Identify any anomalies within a given set of data cases with respect to a given relationship or expectation e.g. statistical outliers
  • Cluster: Given a set of data cases, find clusters of similar attribute values.attribute values.
  • Correlate: Given a set of data cases and two attributes, determine useful relationships between the values of those attributes.