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
- 2. Tae-Seob Yun
- 3. Yeon-Mi Yeo
- 4. Il-Yeol Song
- 5. Hyuk-Yoon Kwon
- 6. In-Joong Kim
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| 157 | HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads | 2009 | VLDB | 0.00040397359 |
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