Tasks Taxonomy for Graphs
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Low-Level Tasks
From [Amar et al. 2005] and [Lee et al 2006].
- General Tasks
Task | Description |
Retrieve Value | Given a set of cases, find attributes of those cases. |
Filter | Given some conditions on attributes 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.(e.g. average, median, and count) |
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 set. |
Characterize Distribution | Given a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute’s 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. |
Correlate | Given a set of data cases and two attributes, determine useful relationships between the values of those attributes. |
Scan | Quickly review a set of items. |
Set Operation | Given multiple sets of items, perform set operations on them. For example, find the intersection of the set of nodes. |
- Graph Specific Tasks
Task | Description |
Find Adjacent Nodes | Given a node, find its adjacent nodes. |
Graph Task Taxonomy
Topology-based Tasks
Attribute-based Tasks
Browsing Tasks
Overview Tasks
High-Level Tasks
References
- [Amar et al. 2005] Amar, R., Eagan, J., and Stasko, J. Low-Level Components of Analytic Activity in Information Visualization, Proceedings of the Symposium on Information Visualization (InfoVis ’05), pp. 111-117, 2005.
- [Lee et al. 2006] Lee, B., Plaisant, C., Parr, CS., Fekete, JD. and Henry, N. Task Taxonomy for Graph Visualization, In BEyond time and errors: novel evaLuation methods for Information Visualization (BELIV'06), Venice, Italy, May 2006, to be published.