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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)

Paper ID
3020
Venue
SIGMOD
Year
1997
Pagerank
0.00010520118
Overall Rank
1,517 | 89.60%
DOI
10.1145/253260.253342

Incoming Non-self Citations Over Time

Authors

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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