Abstract
Data visualization is an effective mechanism for identifying trends, insights, and anomalies in data. On large datasets, however, generating visualizations can take a long time, delaying the extraction of insights, hampering decision making, and reducing exploration time. One solution is to use online sampling-based schemes to generate visualizations faster while improving the displayed estimates incrementally, eventually converging to the exact visualization computed on the entire data. However, the intermediate visualizations are approximate, and often fluctuate drastically, leading to potentially incorrect decisions. We propose sampling-based incremental visualization algorithms that reveal the "salient" features of the visualization quickly-with a 46× speedup relative to baselines-while minimizing error, thus enabling rapid and errorfree decision making. We demonstrate that these algorithms are optimal in terms of sample complexity, in that given the level of interactivity, they generate approximations that take as few samples as possible. We have developed the algorithms in the context of an incremental visualization tool, titled INCVISAGE, for trendline and heatmap visualizations. We evaluate the usability of INCVISAGE via user studies and demonstrate that users are able to make effective decisions with incrementally improving visualizations, especially compared to vanilla online-sampling based schemes.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1262-1273 |
| Number of pages | 12 |
| Journal | Proceedings of the VLDB Endowment |
| Volume | 10 |
| Issue number | 11 |
| DOIs | |
| State | Published - Aug 1 2017 |
| Event | 43rd International Conference on Very Large Data Bases, VLDB 2017 - Munich, Germany Duration: Aug 28 2017 → Sep 1 2017 |
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ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Computer Science(all)
Cite this
I've seen "enough" : Incrementally improving visualizations to support rapid decision making. / Rahman, Sajjadur; Aliakbarpour, Maryam; Kong, Ha Kyung; Blais, Eric; Karahalios, Kyratso George; Parameswaran, Aditya G; Rubinfield, Ronitt.
In: Proceedings of the VLDB Endowment, Vol. 10, No. 11, 01.08.2017, p. 1262-1273.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - I've seen "enough"
T2 - Incrementally improving visualizations to support rapid decision making
AU - Rahman, Sajjadur
AU - Aliakbarpour, Maryam
AU - Kong, Ha Kyung
AU - Blais, Eric
AU - Karahalios, Kyratso George
AU - Parameswaran, Aditya G
AU - Rubinfield, Ronitt
PY - 2017/8/1
Y1 - 2017/8/1
N2 - Data visualization is an effective mechanism for identifying trends, insights, and anomalies in data. On large datasets, however, generating visualizations can take a long time, delaying the extraction of insights, hampering decision making, and reducing exploration time. One solution is to use online sampling-based schemes to generate visualizations faster while improving the displayed estimates incrementally, eventually converging to the exact visualization computed on the entire data. However, the intermediate visualizations are approximate, and often fluctuate drastically, leading to potentially incorrect decisions. We propose sampling-based incremental visualization algorithms that reveal the "salient" features of the visualization quickly-with a 46× speedup relative to baselines-while minimizing error, thus enabling rapid and errorfree decision making. We demonstrate that these algorithms are optimal in terms of sample complexity, in that given the level of interactivity, they generate approximations that take as few samples as possible. We have developed the algorithms in the context of an incremental visualization tool, titled INCVISAGE, for trendline and heatmap visualizations. We evaluate the usability of INCVISAGE via user studies and demonstrate that users are able to make effective decisions with incrementally improving visualizations, especially compared to vanilla online-sampling based schemes.
AB - Data visualization is an effective mechanism for identifying trends, insights, and anomalies in data. On large datasets, however, generating visualizations can take a long time, delaying the extraction of insights, hampering decision making, and reducing exploration time. One solution is to use online sampling-based schemes to generate visualizations faster while improving the displayed estimates incrementally, eventually converging to the exact visualization computed on the entire data. However, the intermediate visualizations are approximate, and often fluctuate drastically, leading to potentially incorrect decisions. We propose sampling-based incremental visualization algorithms that reveal the "salient" features of the visualization quickly-with a 46× speedup relative to baselines-while minimizing error, thus enabling rapid and errorfree decision making. We demonstrate that these algorithms are optimal in terms of sample complexity, in that given the level of interactivity, they generate approximations that take as few samples as possible. We have developed the algorithms in the context of an incremental visualization tool, titled INCVISAGE, for trendline and heatmap visualizations. We evaluate the usability of INCVISAGE via user studies and demonstrate that users are able to make effective decisions with incrementally improving visualizations, especially compared to vanilla online-sampling based schemes.
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U2 - 10.14778/3137628.3137637
DO - 10.14778/3137628.3137637
M3 - Conference article
AN - SCOPUS:85037042988
VL - 10
SP - 1262
EP - 1273
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
SN - 2150-8097
IS - 11
ER -