Boomerang: Proactive Insight-Based Recommendations for Guiding Conversational Data Analysis
Summary: Boomerang offers proactive insight-based recommendations for guiding conversational data analysis. Aggregates signals from statistical, collaborative, and content-based sources; ranks insights by relevance and timeliness to fit the conversational context. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Doris Jung-Lin Lee (University of California Berkeley)
- 2. Abdul Quamar (IBM)
- 3. Eser Kandogan (Megagon Labs)
- 4. Fatma Özcan (Google)
BibTeX Citation
@inproceedings{lee_sigmod21,
title = {{Boomerang: Proactive Insight-Based Recommendations for Guiding Conversational Data Analysis}},
author = {Lee, Doris Jung-Lin and Quamar, Abdul and Kandogan, Eser and Özcan, Fatma},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3452748},
url = {https://dl.acm.org/doi/10.1145/3448016.3452748},
year = {2021}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,493 | Foresight: Recommending Visual Insights | 2017 | VLDB | 7.2593029e-05 |
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