Landmark-Join: Hash-Join Based String Similarity Joins with Edit Distance Constraints

  • Kazuyo Narita
  • Shinji Nakadai
  • Takuya Araki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7448)


Parallel data processing complicates the completion of string similarity joins because parallel data processing requires the use of a well designed data partitioning scheme. Moreover, efficient verification of string pairs is needed to speed up the entire string similarity join process. We propose a novel framework that addresses these requirements through the use of edit distance constraints. The Landmark-Join framework has two functions that reduce two kinds of search spaces. The first, q-bucket partitioning, reduces the number of verifications of dissimilar string pairs and lowers skewness among buckets. The second, local upper bound calculation, prunes the search space of edit distance to speed up each verification. Experimental results show that Landmark-Join has good parallel scalability and that the two proposed functions speed up the entire string similarity join process.


Edit Distance Input String Edit Graph Position List Zipf Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kazuyo Narita
    • 1
  • Shinji Nakadai
    • 1
  • Takuya Araki
    • 1
  1. 1.Cloud System Research LaboratoriesNEC CorporationKawasakiJapan

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