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iOLAP: Managing Uncertainty for Efficient Incremental OLAP
Summary: iOLAP provides an incremental OLAP engine with mini-batches and delta updates to balance accuracy and latency. A delta-update algorithm models uncertainty to minimize recomputation, enabling scalable Spark deployment on 100+ machines with frequent approximate updates.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h536bed72f8b9b9b8
Venue
SIGMOD
Year
2016
Pagerank
5.8452386e-05
Overall Rank
6,227 | 58.14%
DOI
10.1145/2882903.2915240
Incoming Non-self Citations Over Time
BibTeX Citation
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@inproceedings{zeng_sigmod16,
title = {{iOLAP: Managing Uncertainty for Efficient Incremental OLAP}},
author = {Zeng, Kai and Agarwal, Sameer and Stoica, Ion},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2915240},
url = {https://dl.acm.org/doi/10.1145/2882903.2915240},
year = {2016}
}
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1997
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