n-gram/2L: a space and time efficient two-level n-gram inverted index structure

  • Authors:

  • Min-Soo Kim;Kyu-Young Whang;Jae-Gil Lee;Min-Jae Lee

  • Affiliations:

  • Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea;Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea;Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea;Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea

  • Venue:
  • VLDB '05 Proceedings of the 31st international conference on Very large data bases
  • Year:
  • 2005

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Abstract

The n-gram inverted index has two major advantages: language-neutral and error-tolerant. Due to these advantages, it has been widely used in information retrieval or in similar sequence matching for DNA and protein databases. Nevertheless, the n-gram inverted index also has drawbacks: the size tends to be very large, and the performance of queries tends to be bad. In this paper, we propose the two-level n-gram inverted index (simply, the n-gram/2L index) that significantly reduces the size and improves the query performance while preserving the advantages of the n-gram inverted index. The proposed index eliminates the redundancy of the position information that exists in the n-gram inverted index. The proposed index is constructed in two steps: 1) extracting subsequences of length m from documents and 2) extracting n-grams from those subsequences. We formally prove that this two-step construction is identical to the relational normalization process that removes the redundancy caused by a non-trivial multivalued dependency. The n-gram/2L index has excellent properties: 1) it significantly reduces the size and improves the performance compared with the n-gram inverted index with these improvements becoming more marked as the database size gets larger; 2) the query processing time increases only very slightly as the query length gets longer. Experimental results using databases of 1 GBytes show that the size of the n-gram/2L index is reduced by up to 1.9 ~ 2.7 times and, at the same time, the query performance is improved by up to 13.1 times compared with those of the n-gram inverted index.