## 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.