I/O-Efficient Similarity Join

Rasmus Pagh, Ninh Dang Pham, Francesco Silvestri, Morten Stöckel

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


We present an I/O-efficient algorithm for computing similarity
joins based on locality-sensitive hashing (LSH). In contrast to the filtering
methods commonly suggested our method has provable sub-quadratic
dependency on the data size. Further, in contrast to straightforward
implementations of known LSH-based algorithms on external memory,
our approach is able to take significant advantage of the available internal
memory: Whereas the time complexity of classical algorithms includes a
factor of N ρ, where ρ is a parameter of the LSH used, the I/O complexity
of our algorithm merely includes a factor (N/M)ρ, where N is the data size
and M is the size of internal memory. Our algorithm is randomized and
outputs the correct result with high probability. It is a simple, recursive,
cache-oblivious procedure, and we believe that it will be useful also in
other computational settings such as parallel computation.
Original languageEnglish
Title of host publicationAlgorithms - ESA 2015 : 23rd Annual European Symposium, Patras, Greece, September 14-16, 2015, Proceedings
Number of pages12
Publication date14 Sept 2015
ISBN (Print)978-3-662-48349-7
ISBN (Electronic)978-3-662-48350-3
Publication statusPublished - 14 Sept 2015
SeriesLecture Notes in Computer Science


  • I/O-efficient algorithms
  • Locality-sensitive hashing
  • Similarity joins
  • Cache-oblivious computing
  • External memory algorithms


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