Order of Magnitude Markers : An Empirical Study on Large Magnitude Number Detection
- Submitting institution
-
King's College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 87320656
- Type
- D - Journal article
- DOI
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10.1109/TVCG.2014.2346428
- Title of journal
- IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
- Article number
- 12
- First page
- 2261
- Volume
- 20
- Issue
- 12
- ISSN
- 1077-2626
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
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-
- Citation count
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper introduces Order of Magnitude Markers (OOMMs), a new technique for numerical representation of large-magnitude-range data. Especially effective when traditional numerical representations fail to cope with the required dynamic range, the paper proposes several novel designs, validated and rigorously evaluated through 3 lab-controlled user studies and using 4 different domain expert suggested scenarios (earthquake simulations data, movement ecology sensor data). OOMMs have been successfully employed in research applications in Bioinformatics (Ohio State https://doi.org/10.1186/s12859-015-0585-1), Diffusion Magnetic Resonance Imaging (BioMED Lab., Maryland https://doi.org/10.1109/TVCG.2019.2898438), and Cybersecurity (MIT https://dl.acm.org/doi/10.5555/3071534.3071565). OOMS have inspired the design of the visual encoding SplitVectors (https://dl.acm.org/doi/10.1109/TVCG.2016.2539949).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
