Optimizing Data-Intensive Applications Automatically By Leveraging Parallel Data Processing Frameworks
Summary: Casper automatically rewrites sequential data-intensive programs into Spark-friendly DSLs/APIs, lowering adaptation inertia for non-experts. The demonstration compares original Java implementations with optimized Spark versions in real time, via a browser interface and cloud execution. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Maaz Bin Safeer Ahmad (University of Washington)
- 2. Alvin Cheung (University of Washington)
BibTeX Citation
@inproceedings{ahmad_sigmod17,
title = {{Optimizing Data-Intensive Applications Automatically By Leveraging Parallel Data Processing Frameworks}},
author = {Ahmad, Maaz Bin Safeer and Cheung, Alvin},
series = {{SIGMOD} '17},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3035918.3056440},
url = {https://dl.acm.org/doi/10.1145/3035918.3056440},
year = {2017}
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,499 | Towards Auto-Generated Data Systems | 2023 | VLDB | 5.093636e-05 |
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