ZigZag: Supporting Similarity Queries on Vector Space Models
Summary: ZigZag uses inverted-index based methods for similarity search in vector-space models where records are token bags with token weights and per-record degrees. Pruning bounds with formal correctness and iterative tightening enable exact filtering of dissimilar records, scalable to disks. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Wenhai Li (Wuhan University)
- 2. Lingfeng Deng (Wuhan University)
- 3. Yang Li (Wuhan University)
- 4. Chen Li (University of California Irvine)
BibTeX Citation
@inproceedings{li_sigmod18,
title = {{ZigZag: Supporting Similarity Queries on Vector Space Models}},
author = {Li, Wenhai and Deng, Lingfeng and Li, Yang and Li, Chen},
series = {{SIGMOD} '18},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3183713.3196936},
url = {https://dl.acm.org/doi/10.1145/3183713.3196936},
year = {2018}
}
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