cl04_task_abstractions.pdf
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Task abstractionsWhy?
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Abstract tasks Abstract data and views
MethodsHow?
What?Why?
How?
What?Why?
How?
What?Why?
How?
What?Why?
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Task abstraction: need
Transforming task descriptions from domain-specific language into abstract form allows you to compare them between different domains.
If you talk with domain-specific language used by end-users, everything may seem very different when its not true and its just misleading.
Furthermore, task abstraction can guide the design of data abstraction.
The reasons that justify why visual analytic tools are used can be decomposed in terms of the associated actions (verbs) and the targets (nouns).
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Actions: why?
There are three levels of actions that could be considered as targets for users:
1. Why is the visualization being used?2. What kind of search is performed based on
whether the target and the location are known or not?
3. What kind of query is made based on the results of the previous question?
Each of these levels has a separate set of options.
Why?
Use
Search
Query
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Actions: why is being used?
UseConsume
information
Presentinformation to third parties
Discovernew knowledge
Generate Verify
Enjoya casual intereset
Produceinformation
Use
Buscar
Preguntar
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Actions: what kind of search?
Search
Lookup
Locate
Browse
Explore
Target known
Locationknown
Locationunknown
Target unknown
Usar
Search
Preguntar
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Actions: what kind of query?
Query
IdentifyOne
CompareSome
SummarizeAll
Usar
Buscar
Query
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Targets: why?
Previous actions are performed on the targets, in the senses of those aspects of the data that are of interest to users.
The concept of target arises explicitly with the actions of search and query.
With the actions related to use, it can be more implicit, but still obvious: eg., items that the user has to present or to show.
Why?
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Targets: what is used?
All data
Trends Outliers Features
Attributes
One
Distribution
Multiple
Dependency Correlation Similarity
Networks
Topology
Path
Spatialdata
Shape
Extremes
Value
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Design choices: how?
The third element in the analysis is how the different design options are built to create and manipulate the visualizations.
In this case, we use verbs again.
How?
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Design choices: how?Encode
Arrange
Express
Separate
Order
Align
Use
Map
Define visual marksand channels
Manipulate
Change
Select
Navigate
Facet
Juxtapose
Partition
Superimpose
Reduce
Filter
Aggregate
Embed
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Analysis example: SpaceTree vs. TreeJuxtaposer
These are two systems that offer different solutions to the question how?, but have the same context for the questions why? and what?
SpaceTree (Grosjean02) TreeJuxtaposer (Munzner03)
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Analysis example: SpaceTree vs. TreeJuxtaposer What?: a large tree. Why?: provide colleagues a path of interest between two nodes of the tree.
More specifically, both tools allow highlight paths between nodes in the tree. Both systems allow users to navigate through the tree and select a path, with
the result that are coded differently from those not selected (highlighting). The systems differ in the way they manipulate and rearrange the visual
elements. SpaceTree implements the selection by changing what is displayed in the
view, adding and filtering automatically unselected items. TreeJuxtaposer allows users reorganize tree zones to ensure visibility of
certain areas of interest.
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Analysis example: SpaceTree vs. TreeJuxtaposer
How?
What?Why?
Tree
Actions
Present
Locate
Identify
Target
Path betweennodes
Encoding
Navigate
Select
Filter
AggregateSpaceTree
Encoding
Navigate
Select
Arrange
TreeJuxtaposer
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Example deriving one attribute: Strahlernumber One of the most common problems visualizing trees or complex
networks is to find a simpler representation of the topology of these structures to collect their most important features.
One option is to calculate a new derived attribute that characterizes the importance of each node in the graph and perform filtering based on this new attribute.
We can use the Strahler number, devised in hydrology to characterize the ramifications of river basins, which has been adapted and extended for visualizing trees and networks [Auber02].
The most central nodes have very high values, while more peripheral nodes have very low values.
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Example deriving one attribute: Strahlernumber
4.600 Nodes 520.000 Nodes
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Example deriving one attribute: Strahler number
How?
What?Why?
Input: Tree
Actions
Summarize
Target
Topology
Task 1Encoding
Reduce
Filter
Task 2
Output: Quantitativeattributes at
nodes
Derive
Input: Tree
Output: Quantitativeattributes at
nodes
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References
Tamara Munzner. Visualization Analysis and Design. A K Peters Visualization Series. CRC Press. Oct 2014.
Stuart K. Card, Jock Mackinlay and Ben Shneiderman. Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann, 1999.
[Grosjean02]: J. Grosjean, C. Plaisant, B. Bederson; SpaceTree: Supporting Exploration in Large Node Link Tree, Design evolution and Empirical Evaluation, In Proc. IEEE Symp. On Information Visualization (InfoVis), pp. 57-64, 2002.
[Munzner03]: T. Munzner et al.; TreeJuxtaposer: Scalable Tree Comparison Using Focus+Context With Guaranteed Visibility; ACM TOG, 22:3 (2003), 453-462
[Heer12]: J. Heer, B. Shneiderman; Interactive Dynamics for Visual Analysis; ACM Queue 10:2 (2012), 30-55
[Auber02]: D. Auber; Using Strahler numbers for real time visual exploration of huge graphs; In Int. Conf. On Computer Vision and Graphics, pp. 56-69, 2002.
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Actions: present
Present refers to the use of visualization to communicate information to third parties, to tell a story with data support or to guide an audience through a series of training activities.
Presentations can be used to make decisions, planning, forecasting or educate.
A crucial aspect of the presentations is that visualization is being used to communicate to an audience something well known a priori.
In other contexts, present can be synonymous to explain.
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Actions: discover
Discover refers to use visualization to find new knowledge. The output of this task will be the generation of new hypotheses. Its use also includes the verification of hypotheses. Explore is a synonym to discover.
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Actions: enjoy Enjoy refers to the use of visualization without any preconceived goal,
just to satisfy curiosity and enjoyment of its users.
A difficult issue to solve is to match the objectives of the visualization designer with those of its users.
Eg.: NameVoyager
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Actions: produce In this case, the goal for the visualization users will be to generate new
material. This time, three objectives are sought:
Annotate: Associate new textual or graphical information to one or more
preexisting visual elements. If the annotation is associated with data items, it can be considered
as a new attribute. Record:
Permanently save or capture the visual elements. Examples: screenshots, lists of items selected or marked locations,
parameter specification, interaction logs or annotations. Derive
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Actions: produce This time, three objectives are sought :
Annotate Record Derive:
New data are generated from existing ones, either in terms of attributes or datasets.
It is one of the key parts in the design process of visualizations: viewing the data as is or make any change.
This operation involves the definition of new abstract data types that havent been specified by users.
Sometimes it is of interest to create derived attributes that extend the attribute collection of the original dataset through a transformation that generates a new visual coding that solves the original problem.
Eg.: computing the difference between two functions.
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Actions: search All of the high-level use cases require the user to search for elements
of interest within the visualization as a mid-level goal. We can find four choices:
Lookup: when users know both what and where to look. Locate: when users know what to look, but dont know where.
Browse: when users know where to look, but dont know what. Explore: when users dont have specific references of what and
where to look.
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Actions: query Once you have found the target or targets of the search, the next
level will be to query about the items to: Identify: refers to a single target. Compare: refers to multiple targets. Usually a more complex task
than identification. Summarize: refers to all elements. A synonym could be an
overview.
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Targets: all data Three targets can be distinguished:
Trends: is a high level characterization of the data pattern. Eg.: increases, decreases, peaks, troughs, plateaus,
Outliers: elements out of a particular pattern or trend. Features: are any structure of interest defined at hand by the
specific task.
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Targets: one attribute Attributes are specific properties that can be visually coded. The following targets can be defined for attributes:
Find an individual value. Find the extremes across the range. Find the distribution of all the values.
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Targets: many attributes Working with many attributes, the following targets can be found:
Dependencies: one attribute depends on a second if the values of the first are directly dependent on the values of the second.
Correlations: there is a correlation between two attributes if you notice a trend in the values of the first and it is linked to the values of the second.
Similarities: for all values of two attributes a quantitative measure is computed that allows to establish a ranking of similar or different attributes.
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Targets: networks Networks define relationships between nodes and links. The fundamental target in a network is to understand networks
topology, i.e., the interconnections structure. A more specific topology target would be the study of paths, defined
as a sequence of one or more links that connect two nodes.
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Targets: spatial data The most common target of spatial data is to understand and
compare shapes.
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Design choices: encode The choice of how to visually encode data is one of the central
aspects of the design. In the visual encoding we distinguish between the definition of visual
marks and channels and the actions associated with the spatial arrangement of these elements.
Space arrangement can serve for: Express values. Separate, order or align elements. Use it directly when the positions of the data are part of the
dataset.
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Design choices: manipulate Manipulate design choices allow to:
Change any aspect of the view. Select elements in the view. Navigate to change the visualization point of view.
Such manipulations involve an interaction that goes beyond static visual encoding.
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Design choices: facet The view will be the visualization region where data are visually
encoded. Facet data means to define how data are decomposed and where are
they shown in the available views. One view can be a single region or be subdivided in different spatial
regions. With this choice, we can decide on:
Juxtapose and coordinate multiple views. Partition data between views. Superpose different layers.
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Design choices: reduce Control over the number of items displayed on a visualization is
carried out with operations that involve the reduction of the dataset size.
The three existing options, filter, aggregate and embed, are double-way operations, and we will consider the same term for the operations that reduce the number of items as well as for the operations that increase their number.
These operations also affect the interaction that takes place with the data.
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Design choices: filter Filtering operations are characterized by defining the inclusion and
exclusion criteria for visualizing items. Some techniques allow to hide data and its subsequent restoration,
while others directly discard the data.
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Design choices: aggregate Aggregation increases the granularity of the visualized elements,
leading to fewer elements to view. The complementary operation is segregate. Aggregation typically involves the creation of new permanent data
derived from those existing. If the operations of aggregation and segregation only generate
temporary data, then they are not usually called derived data.
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Design choices: embed Embedding allows to include both detailed and overview context
information in a single view. Using this design option usually also involves to make use of the other
reduction operations, and even consider also the selection and navigation operations.
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Category 1 Category 2 Category 3 Categora 4
F(x), G(x)
F(x) G(x)
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Category 1 Category2 Category 3 Category 4
H(x)=F(x)-G(x)
H(x)
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Task abstractionsSlide Number 2Task abstraction: needActions: why?Actions: why is being used?Actions: what kind of search?Actions: what kind of query?Targets: why?Targets: what is used?Design choices: how?Design choices: how?Analysis example: SpaceTree vs. TreeJuxtaposerAnalysis example: SpaceTree vs. TreeJuxtaposerAnalysis example: SpaceTree vs. TreeJuxtaposerExample deriving one attribute: Strahler numberExample deriving one attribute: Strahler numberExample deriving one attribute: Strahler numberReferencesActions: presentActions: discoverActions: enjoyActions: produceActions: produceActions: searchActions: queryTargets: all dataTargets: one attributeTargets: many attributesTargets: networksTargets: spatial dataDesign choices: encodeDesign choices: manipulateDesign choices: facetDesign choices: reduceDesign choices: filterDesign choices: aggregateDesign choices: embedSlide Number 38Slide Number 39Slide Number 40
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