Parallel Data Analysis Directly on Scientific File Formats
Summary: Query processing runs directly on HDF5 scientific arrays, eliminating data loading in analysis workflows. It uses bitmap indexing and in-memory parallel execution to exploit supercomputers, outperforming Hive by over 10x and relational DBs. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Spyros Blanas (Ohio State University)
- 2. Kesheng Wu (Lawrence Berkeley National Laboratory)
- 3. Surendra Byna (Lawrence Berkeley National Laboratory)
- 4. Bin Dong (Lawrence Berkeley National Laboratory)
- 5. Arie Shoshani (Lawrence Berkeley National Laboratory)
BibTeX Citation
@inproceedings{blanas_sigmod14,
title = {{Parallel Data Analysis Directly on Scientific File Formats}},
author = {Blanas, Spyros and Wu, Kesheng and Byna, Surendra and Dong, Bin and Shoshani, Arie},
series = {{SIGMOD} '14},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2588555.2612185},
url = {https://dl.acm.org/doi/10.1145/2588555.2612185},
year = {2014}
}
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