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Record W2900510420 · doi:10.1109/bdva.2018.8534019

Multiple Workspaces in Visual Analytics

2018· article· en· W2900510420 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsWorkspaceComputer scienceVisual analyticsVisualizationExploratory analysisAnalyticsHuman–computer interactionProcess (computing)Data visualizationExploratory researchWork (physics)Interactive visual analysisPath (computing)Data scienceData miningArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Exploratory visual analysis is an iterative process, where analysts often start from an overview of the data. Subsequently, they often pursue different hypotheses through multiple rounds of interaction and analysis. Commercial visualization packages support mostly a model with a single analysis path, where the system view represents only the final state of the users' current analysis. In this paper, we investigate the benefit of using multiple workspaces to support alternative analyses, enabling users to create different workspaces to pursue multiple analysis paths at the same time. We implemented a prototype for multiple workspaces using a multi-tab design in a visual analytics system. The results of our user studies show that multiple workspaces: enable analysts to work on concurrent tasks, work well for organizing an analysis, and make it easy to revisit previous parts of their work.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.326
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations11
Published2018
Admission routes1
Has abstractyes

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