ODYS: An Approach to Building a Massively-Parallel Search Engine Using a DB-IR Tightly-Integrated Parallel DBMS for Higher-Level Functionality
Summary: ODYS builds a massively-parallel search engine atop a tightly integrated DBMS-IR, delivering SQL-like programming for IR workloads over a DBMS rather than a filesystem. A hybrid analytic-experimental model shows ~2% error and scalable performance (1B queries/day on 30B pages; 194 ms, 148 ms with 2× nodes), proving DBMS-driven search viability. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Kyu-Young Whang (Korea Advanced Institute of Science and Technology)
- 2. Tae-Seob Yun (Korea Advanced Institute of Science and Technology)
- 3. Yeon-Mi Yeo (Korea Advanced Institute of Science and Technology)
- 4. Il-Yeol Song (Drexel University)
- 5. Hyuk-Yoon Kwon (Korea Advanced Institute of Science and Technology)
- 6. In-Joong Kim (Korea Advanced Institute of Science and Technology)
BibTeX Citation
@inproceedings{whang_sigmod13,
title = {{ODYS: An Approach to Building a Massively-Parallel Search Engine Using a DB-IR Tightly-Integrated Parallel DBMS for Higher-Level Functionality}},
author = {Whang, Kyu-Young and Yun, Tae-Seob and Yeo, Yeon-Mi and Song, Il-Yeol and Kwon, Hyuk-Yoon and Kim, In-Joong},
series = {{SIGMOD} '13},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2463676.2465316},
url = {https://dl.acm.org/doi/10.1145/2463676.2465316},
year = {2013}
}
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| 120 | HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads | 2009 | VLDB | 0.00031680027 |
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