Building a Scalable Geo-Spatial DBMS: Technology, Implementation, and Evaluation
Summary: Novel parallelization techniques for geo-spatial DBMS, implemented in the Paradise object-relational DBMS. Evaluation with complex geo-spatial queries over a 120 GB global dataset demonstrates scalable performance gains. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jignesh Patel (University of Wisconsin)
- 2. JieBing Yu (University of Wisconsin)
- 3. Navin Kabra (University of Wisconsin)
- 4. Kristin Tufte (University of Wisconsin)
- 5. Biswadeep Nag (University of Wisconsin)
- 6. Josef Burger (University of Wisconsin)
- 7. Nancy Hall (University of Wisconsin)
- 8. Karthikeyan Ramasamy (University of Wisconsin)
- 9. Roger Lueder (University of Wisconsin)
- 10. Curt Ellmann (University of Wisconsin)
- 11. Jim Kupsch (University of Wisconsin)
- 12. Shelly Guo (University of Wisconsin)
- 13. Johan Larson (University of Wisconsin)
- 14. David DeWitt (University of Wisconsin)
- 15. Jeffrey Naughton (University of Wisconsin)
BibTeX Citation
@inproceedings{patel_sigmod97,
title = {{Building a Scalable Geo-Spatial DBMS: Technology, Implementation, and Evaluation}},
author = {Patel, Jignesh and Yu, JieBing and Kabra, Navin and Tufte, Kristin and Nag, Biswadeep and Burger, Josef and Hall, Nancy and Ramasamy, Karthikeyan and Lueder, Roger and Ellmann, Curt and Kupsch, Jim and Guo, Shelly and Larson, Johan and DeWitt, David and Naughton, Jeffrey},
series = {{SIGMOD} '97},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/253260.253342},
url = {https://dl.acm.org/doi/10.1145/253260.253342},
year = {1997}
}
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