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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)

Paper ID
4785
Venue
SIGMOD
Year
2013
Pagerank
5.093636e-05
Overall Rank
12,253 | 15.94%
DOI
10.1145/2463676.2465316

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Authors

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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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
120 HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads 2009 VLDB 0.00031680027
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