Name
Abstract | ||
|---|---|---|
Uncertainty plays an important and complex role in data analysis, where the goal is to find pertinent patterns, build robust models, and support decision making. While these endeavours are often associated with professional data scientists, many domain experts engage in such activities with varying skill levels. To understand how these domain experts (or \"data workers\") analyse uncertain data we conducted a qualitative user study with 12 participants from a variety of domains. In this paper, we describe their various coping strategies to understand, minmise, exploit or even ignore this uncertainty. The choice of the coping strategy is influenced by accepted domain practices, but appears to depend on the types and sources of uncertainty and whether participants have access to support tools. Based on these findings, we propose a new process model of how data workers analyse various types of uncertain data and conclude with design considerations for uncertainty-aware data analytics. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3025453.3025738 | CHI |
Keywords | Field | DocType |
uncertainty, data analysis, data science, qualitative study | Data analysis,Computer science,Coping (psychology),Knowledge management,Uncertain data,Exploit,Qualitative research | Conference |
Citations | PageRank | References |
10 | 0.55 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nadia Boukhelifa | 1 | 101 | 9.85 |
| Marc-Emmanuel Perrin | 2 | 10 | 0.55 |
| Samuel Huron | 3 | 150 | 9.80 |
| James Eagan | 4 | 470 | 24.10 |