Guidelines For Pursuing and Revealing Data Abstractions
| Autor: | Alex Bigelow, Katherine E. Isaacs, Katy Williams |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science business.industry media_common.quotation_subject Computer Science - Human-Computer Interaction 020207 software engineering Survey research 02 engineering and technology Creativity Computer Graphics and Computer-Aided Design Transparency (behavior) Data science Grounded theory Human-Computer Interaction (cs.HC) Visualization Data visualization Text mining Signal Processing 0202 electrical engineering electronic engineering information engineering Computer Vision and Pattern Recognition business Set (psychology) Software media_common |
| Popis: | Many data abstraction types, such as networks or set relationships, remain unfamiliar to data workers beyond the visualization research community. We conduct a survey and series of interviews about how people describe their data, either directly or indirectly. We refer to the latter as latent data abstractions. We conduct a Grounded Theory analysis that (1) interprets the extent to which latent data abstractions exist, (2) reveals the far-reaching effects that the interventionist pursuit of such abstractions can have on data workers, (3) describes why and when data workers may resist such explorations, and (4) suggests how to take advantage of opportunities and mitigate risks through transparency about visualization research perspectives and agendas. We then use the themes and codes discovered in the Grounded Theory analysis to develop guidelines for data abstraction in visualization projects. To continue the discussion, we make our dataset open along with a visual interface for further exploration. |
| Databáze: | OpenAIRE |
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