Task abstractionsWhy?

Abstract tasks Abstract data and views

MethodsHow?

What?Why?

How?

What?Why?

How?

What?Why?

How?

What?Why?

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).

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

Actions: why is being used?

UseConsume

information

Presentinformation to third parties

Discovernew knowledge

Generate Verify

Enjoya casual intereset

Produceinformation

Use

Buscar

Preguntar

Actions: what kind of search?

Search

Lookup

Locate

Browse

Explore

Target known

Locationknown

Locationunknown

Target unknown

Usar

Search

Preguntar

Actions: what kind of query?

Query

IdentifyOne

CompareSome

SummarizeAll

Usar

Buscar

Query

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?

Targets: what is used?

All data

Trends Outliers Features

Attributes

One

Distribution

Multiple

Dependency Correlation Similarity

Networks

Topology

Path

Spatialdata

Shape

Extremes

Value

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?

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

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)

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.

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

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.

Example deriving one attribute: Strahlernumber

4.600 Nodes 520.000 Nodes

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

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.

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.

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.

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

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

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.

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.

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.

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.

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.

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.

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.

Targets: spatial data The most common target of spatial data is to understand and

compare shapes.

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.

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.

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.

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.

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.

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.

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.

01

2

3

4

5

6

7

8

Category 1 Category 2 Category 3 Categora 4

F(x), G(x)

F(x) G(x)

-5

-4

-3

-2

-1

0

1

2

3

Category 1 Category2 Category 3 Category 4

H(x)=F(x)-G(x)

H(x)

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