MacroBase: Prioritizing Attention in Fast Data
Summary: MacroBase prioritizes end-user attention in high-volume fast data streams and acts as a search engine for fast data. It delivers accurate, scalable classifications with explanations over groups, powered by a reservoir sampler and heavy-hitters sketch that reach up to 2M events/sec per core and see deployment in telematics. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Peter Bailis (Stanford University)
- 2. Edward Gan (Stanford University)
- 3. Samuel Madden (Massachusetts Institute of Technology)
- 4. Deepak Narayanan (Stanford University)
- 5. Kexin Rong (Stanford University)
- 6. Sahaana Suri (Stanford University)
BibTeX Citation
@inproceedings{bailis_sigmod17,
title = {{MacroBase: Prioritizing Attention in Fast Data}},
author = {Bailis, Peter and Gan, Edward and Madden, Samuel and Narayanan, Deepak and Rong, Kexin and Suri, Sahaana},
series = {{SIGMOD} '17},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3035918.3035928},
url = {https://dl.acm.org/doi/10.1145/3035918.3035928},
year = {2017}
}
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