Hierarchical visual filtering pragmatic and epistemic actions for database visualization

The ACM Symposium On Applied Computing 2013(ACM-SAC 2013) - Coimbra, Portugal, March 18 - 22, 2013

Hierarchical Visual Filtering, Hierarchical Visual Filtering, pragmatic and epistemicpragmatic and epistemic

actions for database actions for database visualizationvisualization

Jose F Rodrigues Jr,Carlos E Cirilo, Antonio F Prado, Luciana A M Zaina

Computer Science and Mathematics Institute (ICMC)Computer Science DepartmentBrazilhttp://www.icmc.usp.br/~junio/PublishedPapers/RodriguesJr_et_al-ACMSAC2013.pdf

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Increasing volume of data that cannot be well employed to produce useful knowledge

The efficient analysis of multivariate data can provide assistance in decision making

Raw visualization techniques are limited in the task of data analysis, while datasets are unlimited both in size and complexity

There is a need for visualization mechanisms that reduce the

drawback of massive datasets.

Mot

ivat

ion

Mot

ivat

ion

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The

pro

blem

The

pro

blem

Due to overlap of graphical items, some regions of the visualization seam like blots in the display

Massively populated datasets tend to result in a visualization scene with an unacceptable level of clutter

Overlap of graphical items

Visual clutter

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The

pro

blem

The

pro

blem

Multiple concurrent perspectives

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The

pro

blem

The

pro

blem

How to reduce visual clutter problems at the same time that we put together multiple views of the same data?

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Pra

gmat

ic a

nd E

pist

emic

Pra

gmat

ic a

nd E

pist

emic From cognitive science, Kirsh and Maglio

identified two kinds of actions: Pragmatic: actions performed to bring one

closer to a goal; Epistemic: actions performed to describe,

and uncover, information that, otherwise, would be hard to process mentally.

Arithmetic example: Pragmatic corresponds to steps in order to

solve the problem; Epistemic corresponds to annotations of

intermediate results so to guide and describe the pragmatism.

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Pra

gmat

ic a

nd E

pist

emic

Pra

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nd E

pist

emic

Why pragmatic and epistemic actions?

1. reduced memory – space complexity;

2. reduced number of steps – time complexity;

3. reduced probability of error – reliability.

The same holds for Visualization.

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Pra

gmat

ic a

nd E

pist

emic

Pra

gmat

ic a

nd E

pist

emic In Information Visualization:

Pragmatic: interaction operations that lead the analyst to new perspectives of the data concerning: which data to show; which visualization to use; which potential conclusions to draw.

Epistemic: the recording of intermediate visual presentations in order to assist the analyst in a sequence of interactive steps

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Pra

gmat

ic a

nd E

pist

emic

Pra

gmat

ic a

nd E

pist

emic In our system, the VisTree:

Pragmatic: visual filtering in the form of pipelined visualization workspaces with annotation: which data to show; which visualization to use; which potential conclusions.

Epistemic: recording of the workspaces in an evolving tree structure that can be navigated

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Pra

gmat

ic a

nd E

pist

emic

Pra

gmat

ic a

nd E

pist


Pragmatic: visual filtering in the form of pipelined visualization workspaces with annotation which data to show; which visualization to use; which potential conclusions.

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Pra

gmat

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nd E

pist

emic

Pra

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pist



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Pra

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pist



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Pra

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Pra

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Pra

gmat

ic a

nd E

pist

emic

Pra

gmat

ic a

nd E

pist

emic In our system, the VisTree

Pragmatic + Epistemic actions

Hierarchical Visual Filtering

Multiple Linked visualizations with the following features:

•Derivable (pipelined)•Simultaneous•Annotated•Structured•Joinable•Potentially without space limitations

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Der

ivab

le v

isua

liza

tion

sD

eriv

able

vis

uali

zati

ons One visualization can give rise to many others

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Sim

ulta

neou

sS

imul

tane

ous

Different analytical perspectives in the same scenario

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Ann

otat

edA

nnot

ated

Every workspace can be annotated for epistemic purposes

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Use

r ex

peri

men

tsU

ser

expe

rim

ents

In the paper:analytical demonstrations of clutter reduction

+user experimentation

Subjects: 22 Computational Physics undergraduate students One task: identify the two extreme regions in the dataset and create

further visualizations from each based on the a specific attribute Two rounds

– First: using Hierarchical Visual Filtering over VisTree multiple views

– Second: using one single workspace and multiple windows Wall-clock time Results

– 21 students completed the tasks– In average, 42% faster by using Hierarchical Visual Filtering

(4:52 min average x 8:24 min average)– 5 students used paper annotations in round 2, the others used

window alternation

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Con

clus

ions

Con

clus

ions

Hierarchical Visual Filtering Visual exploration following the principle of:

– Pragmatic: filter and pipeline

– Epistemic: record, annotate, and recall persistent visualizations

Gains in:– Memory: recall instead of remember

– Space: reduced visual clutter

– Usability: user tests showed improvements

To do:– Use multiple tables simultaneously

– More extensive HCI experimentation

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The

End

The

End

Thanks for coming

Data & Analytics

Hierarchical visual filtering pragmatic and epistemic actions for database visualization