Contents
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.
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.