A design space of visualization tasks

Note

A taxonomy for data visualization tasks. The author defines the design space dimensions as:

• Goal: Exploratory Analysis (e.g. undirected search), Confirmatory Analysis (directed search), Presentation (exhibiting confirmed analysis results) 
• Means: Navigation (e.g. browsing or searching), (Re-)organization (e.g. extraction, abstraction), Relation (e.g. variations, discrepancies)
• Characteristics: Low-level (e.g. values, objects) & High-level (e.g. trends, outliers, clusters, frequency, distribution, correlation, etc.) data characteristics
• Target: Attribute Relations (e.g. Temporal and Spatial relations), Structural relation (e.g. causal relations, topological relations)
• Cardinality: Single (highlight detail), Multiple (putting data into context), and All Instances (getting the overview). 

The classification can be used as the semantic tuple, i.e. (exploratory, search, trend, attrib(variable), all). This tuple is used to calculate the suitable techniques.

Reference

1. Schulz, Hans-Jörg, et al. "A design space of visualization tasks." IEEE Transactions on Visualization and Computer Graphics 19.12 (2013): 2366-2375.

Interactive dynamics for visual analysis

Note

A taxonomy of tools that support the fluent and flexible use of visualizations.

Pay attention more to Coordinate and Organize sections.

Reference

1. Heer, Jeffrey, and Ben Shneiderman. "Interactive dynamics for visual analysis." Queue 10.2 (2012): 30.

Task taxonomy for graph visualization

Note

A graph-specific visualization consists of Nodes, Links, Paths, Graphs, Connected Components, Clusters, and Groups. This paper discussed the possible tasks to examine the tool based on the given objects.

The low-level tasks, included:

• Retrieve value
• Filter
• Compute the Derived Value
• Find Extremum
• Sort
• Determine Range
• Characterize Distribution
• Find Anomalies
• Cluster
• Correlate

Tasks which commonly encountered while analyzing graph data: 

• Topology-based Tasks: adjacency (direct connection), accessibility (direct or indirect connection), common connection, connectivity
• Attribute-based Tasks: On the Nodes, On the Links
• Browsing Tasks: Follow path, Revisit

Some more high-level tasks: 

• compare two web graph for the difference, e.g. two recipe graph. 
• nodes duplication
• some tasks need users' interpretation

Reference

1. Lee, Bongshin, et al. "Task taxonomy for graph visualization." Proceedings of the 2006 AVI workshop on BEyond time and errors: novel evaluation methods for information visualization. ACM, 2006.

Design considerations for collaborative visual analytics.

Note

This paper discussed the factor to consist a collaborative visual analytics environment. Some of the theory is overlapping with the online community operation. A successful collaboration is an effective division of labor among participants, the author argue three factors here: modularity, granularity, and cost of integration. In other words, the tasks should split, conduct and integrate at a reasonable price. If each of the factors is too expensive, it may hard to be a success collaboration scenario. For modularity factor, the author provides an information visualization reference model; this model helps for decomposing the visualization process into data acquisition and representation visual encoding, display, and interaction. Each of the components can be a reasonable module to start the collaborative works. For granularity factor, the author discussed the sensemaking model, for instance, in cooperative scenarios, the collaborator can immediate benefit from the actions of others. It is hard to facilitate cooperation if a lack of the incentive.

The ground sense principle is listing below:

• discussion models, awareness 
• Reference & deixis, pointing
• Incentives & engagement, personal relevance, social-psychological incentives, gameplay, 
• Identity & trust & reputation, identity presentation 
• Group dynamics,  management, size, diversity 
• Consensus and decision making, information distribution & presentation

A good reference to consider the collaborative theory in different scenarios, e.g. business intelligence system. For social analysis, a extend reading at [2].

Reference

1. Heer, Jeffrey, and Maneesh Agrawala. "Design considerations for collaborative visual analytics." Information visualization 7.1 (2008): 49-62.
2. Wattenberg, Martin, and Jesse Kriss. "Designing for social data analysis." IEEE transactions on visualization and computer graphics 12.4 (2006): 549-557.

egoSlider: Visual analysis of egocentric network evolution.

Note

This paper proposes a tool to visualize the dynamic and temporal information of ego-network. The primary goal of this tool is to support the study of the exploratory pattern for cross domains. For instance, how the ego-network change among time to the relationship with personal health? The contribution lay in three layers: 1) macroscopic: summarize the entire ego-network; 2) mesoscopic: overviewing particular individuals' ego-network evolution; 3) microscopic: displaying detailed temporal information of egos and their alters.

The visualization idea may come from different discipline, e.g. the sociology research may focus on more social interaction with developed social theory. It may be a great contribution to design such a tool to help them better facilitate, utilize and digest the generated data.

Reference

1. Wu, Yanhong, et al. "egoSlider: Visual analysis of egocentric network evolution." IEEE transactions on visualization and computer graphics 22.1 (2016): 260-269.

Reducing snapshots to points: A visual analytics approach to dynamic network exploration.

Note

This paper uses the dimensional reduction technique to reduce the complex, multi-dimensional graph into points as 2-dimension plot. It shows the pattern with a different cluster, the user can further explore the generated points to see the detail of the network.

This may help the user to understand the deep learning through neural network, the feature extraction process. But the challenge is still remaining how to explain/label the projection cluster. It is not guarantee to have a meaningful (or at least human understandable) pattern in each round of exploration.

Reference

1. van den Elzen, Stef, et al. "Reducing snapshots to points: A visual analytics approach to dynamic network exploration." IEEE transactions on visualization and computer graphics 22.1 (2016): 1-10.

Information visualization and visual data mining

Note

A good survey paper to follow the trend of data visualization and mining. This paper provides a clear classification for visual data mining works.  The author describes: "The visual data exploration process be seen a hypothesis generation process". A visualization interface provides the user an overview of the dataset. Based on the insight, the user can explore/filter/verify the finding to answer the hypothesis, the hypothesis can be generated by user/statistics/machine learning. In another hand, a visual data exploration usually follows a three looping process: overview, filter, and detail-on-demand. The different insight will jump out while the user explores the data through designed interface.

A visual data mining has consisted with three components: 1) data type to be visualized:  1D, 2D, ND, Text and hypertext and algorithm data visualization; 2) visualization technique: standard 2/3D, geometrically transformed, icon-based, dense pixel and stacked display; 3) interaction and distortion technique: projection, filtering, zooming, interactive distortion, linking and brushing. Each categories is with a reference paper that worth to further reading.

Reference

1. Keim, Daniel A. "Information visualization and visual data mining." IEEE transactions on Visualization and Computer Graphics 8.1 (2002): 1-8.