G-OLA: Generalized On-Line Aggregation for Interactive Analysis on Big Data
Summary: G-OLA generalizes online aggregation to support OLAP queries with arbitrarily nested aggregates via delta-maintenance in a mini-batch model. Implemented in FluoDB atop Spark; scales to 100 nodes processing ~10 TB of logs with real-time dashboards. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kai Zeng (University of California Berkeley)
- 2. Sameer Agarwal (Databricks)
- 3. Ankur Dave (University of California Berkeley)
- 4. Michael Armbrust (Databricks)
- 5. Ion Stoica (University of California Berkeley)
BibTeX Citation
@inproceedings{zeng_sigmod15,
title = {{G-OLA: Generalized On-Line Aggregation for Interactive Analysis on Big Data}},
author = {Zeng, Kai and Agarwal, Sameer and Dave, Ankur and Armbrust, Michael and Stoica, Ion},
series = {{SIGMOD} '15},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2723372.2735381},
url = {https://dl.acm.org/doi/10.1145/2723372.2735381},
year = {2015}
}
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