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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)

Paper ID
5439
Venue
SIGMOD
Year
2017
Pagerank
5.093636e-05
Overall Rank
11,989 | 17.75%
DOI
10.1145/3035918.3058743

Incoming Non-self Citations Over Time

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Authors

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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