Meeting Big Data challenges with visual analytics
Bibliographic record
Abstract
Purpose – This paper aims to explore the role of records management in supporting the effective use of information visualisation and visual analytics (VA) to meet the challenges associated with the analysis of Big Data. Design/methodology/approach – This exploratory research entailed conducting and analysing interviews with a convenience sample of visual analysts and VA tool developers, affiliated with a major VA institute, to gain a deeper understanding of data-related issues that constrain or prevent effective visual analysis of large data sets or the use of VA tools, and analysing key emergent themes related to data challenges to map them to records management controls that may be used to address them. Findings – The authors identify key data-related issues that constrain or prevent effective visual analysis of large data sets or the use of VA tools, and identify records management controls that may be used to address these data-related issues. Originality/value – This paper discusses a relatively new field, VA, which has emerged in response to meeting the challenge of analysing big, open data. It contributes a small exploratory research study aimed at helping records professionals understand the data challenges faced by visual analysts and, by extension, data scientists for the analysis of large and heterogeneous data sets. It further aims to help records professionals identify how records management controls may be used to address data issues in the context of VA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.067 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.032 | 0.029 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".