Similarity Search and Locality Sensitive Hashing using Ternary Content Addressable Memories

Summary: TLSH, a TCAM-based ternary Locality Sensitive Hashing method for approximate NNS in Euclidean space, achieves near-linear storage and O(1) query time with a single TCAM access. Hashes to {0,1,*} using wildcards; validated on 1M-point data with high accuracy and 1.5M qps on a 1 Gb/s switch. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 4259
Venue: SIGMOD
Year: 2010
Pagerank: 5.0818596e-05
Overall Rank: 6,399 | 55.49%
DOI: -

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
867	SRS: Solving c-Approximate Nearest Neighbor Queries in High Dimensional Euclidean Space with a Tiny Index	2015	VLDB	0.00015792021

Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
34	Similarity Search in High Dimensions via Hashing	1999	VLDB	0.00076637636
79	A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces	1998	VLDB	0.00056242144
400	Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search	2007	VLDB	0.0002427237
682	Quality and Efficiency in High Dimensional Nearest Neighbor Search	2009	SIGMOD	0.00018201541
3,794	Identifying Representative Trends in Massive Time Series Data Sets Using Sketches	2000	VLDB	6.7617267e-05

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682	Quality and Efficiency in High Dimensional Nearest Neighbor Search	2009	SIGMOD	0.00018201541
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8,763	Smooth Tradeoffs between Insert and Query Complexity in Nearest Neighbor Search	2015	PODS	4.456315e-05
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867	SRS: Solving c-Approximate Nearest Neighbor Queries in High Dimensional Euclidean Space with a Tiny Index	2015	VLDB	0.00015792021
34	Similarity Search in High Dimensions via Hashing	1999	VLDB	0.00076637636