DataSense: Display Agnostic Data Documentation
Summary: DataSense proposes display-agnostic data documentation (DAD): inline, interactive dataset documentation—synthesized from profiling, provenance, and user annotations—embedded into any presentation (tables, graphs, maps, query UIs) to aid discovery and task-relevant lookup. Key novelty: decouple generation of documentation (profiling, provenance summarization, annotation extraction) from modality-specific rendering, enabling reusable IDE-like highlights and mouseover details across heterogeneous data displays. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Poonam Kumari (State University of New York at Buffalo)
- 2. Michael Brachmann (State University of New York at Buffalo)
- 3. Oliver Kennedy (State University of New York at Buffalo)
- 4. Su Feng (Illinois Institute of Technology)
- 5. Boris Glavic (Illinois Institute of Technology)
BibTeX Citation
@inproceedings{kumari_cidr21,
address = {Amsterdam, Netherlands},
series = {{CIDR} '21},
title = {{DataSense: Display Agnostic Data Documentation}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Kumari, Poonam and Brachmann, Michael and Kennedy, Oliver and Feng, Su and Glavic, Boris},
year = {2021}
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