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Personal visualizations present a separate class of visualizations where users interact with their own data to draw inferences about themselves. In this paper, we study how a realistic understanding of personal visualizations can be gained from analyzing user interactions. We designed an interface presenting visualizations of the personal data gathered in a prior study and logged interactions from 369 participants as they each explored their own data. We found that the participants spent different amounts of time in exploring their data and used a variety of physical devices which could have affected their engagement with the visualizations. Our findings also suggest that the participants made more comparisons between their data instances than with the provided baselines and certain interface design choices, such as the ordering of options, influenced their exploratory behaviors.
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Small Data
Data is becoming increasingly personal. Individuals regularly interact with a wide variety of structured data, from SQLite databases on phones, to HR spreadsheets, to personal sensors, to open government data appearing in news articles. Although these workloads are important, many of the classical challenges associated with scale and Big Data do not apply. This panel brings together experts in a variety of fields to explore the new opportunities and challenges presented by "Small Data".
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- Award ID(s):
- 1617586
- PAR ID:
- 10175101
- Date Published:
- Journal Name:
- IEEE 33rd International Conference on Data Engineering (ICDE)
- Page Range / eLocation ID:
- 1475 to 1476
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICDE.2017.216
