Alpine: Efficient In situ Data Exploration in the Presence of Updates
Summary: Alpine demonstrates adaptive in situ data exploration with updates via an online partitioning and indexing tuner for fast raw-data queries. Coupled to an in situ executor, the tuner builds and adapts structures over raw files as updates arrive. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Antonios Anagnostou (Aristotle University of Thessaloniki; EPFL)
- 2. Matthaios Olma (EPFL)
- 3. Anastasia Ailamaki (EPFL)
BibTeX Citation
@inproceedings{anagnostou_sigmod17,
title = {{Alpine: Efficient In situ Data Exploration in the Presence of Updates}},
author = {Anagnostou, Antonios and Olma, Matthaios and Ailamaki, Anastasia},
series = {{SIGMOD} '17},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3035918.3058743},
url = {https://dl.acm.org/doi/10.1145/3035918.3058743},
year = {2017}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,413 | Slalom: Coasting Through Raw Data via Adaptive Partitioning and Indexing | 2017 | VLDB | 7.4326381e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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