Structural optimization of a full-text n -gram index using relational normalization

Structural optimization of a full-text n -gram index using relational normalization As the amount of text data grows explosively, an efficient index structure for large text databases becomes ever important. The n -gram inverted index (simply, the n-gram index ) has been widely used in information retrieval or in approximate string matching due to its two major advantages: language-neutral and error-tolerant. Nevertheless, the n -gram 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 by using the relational normalization theory. We first identify that, in the (full-text) n -gram index, there exists redundancy in the position information caused by a non-trivial multivalued dependency. The proposed index eliminates such redundancy by constructing the index in two levels: the front-end index and the back-end index. We formally prove that this two-level construction is identical to the relational normalization process. We call this process structural optimization of the n -gram index. The n -gram/2 L index has excellent properties: (1) it significantly reduces the size and improves the performance compared with the n -gram 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 real databases of 1 GB show that the size of the n -gram/2 L index is reduced by up to 1.9–2.4 times and, at the same time, the query performance is improved by up to 13.1 times compared with those of the n -gram index. We also compare the n -gram/2 L index with Makinen’s compact suffix array (CSA) (Proc. 11th Annual Symposium on Combinatorial Pattern Matching pp. 305–319, 2000) stored in disk. Experimental results show that the n -gram/2 L index outperforms the CSA when the query length is short (i.e., less than 15–20), and the CSA is similar to or better than the n -gram/2 L index when the query length is long (i.e., more than 15–20). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Structural optimization of a full-text n -gram index using relational normalization

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Publisher
Springer-Verlag
Copyright
Copyright © 2008 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-007-0082-x
Publisher site
See Article on Publisher Site

Abstract

As the amount of text data grows explosively, an efficient index structure for large text databases becomes ever important. The n -gram inverted index (simply, the n-gram index ) has been widely used in information retrieval or in approximate string matching due to its two major advantages: language-neutral and error-tolerant. Nevertheless, the n -gram 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 by using the relational normalization theory. We first identify that, in the (full-text) n -gram index, there exists redundancy in the position information caused by a non-trivial multivalued dependency. The proposed index eliminates such redundancy by constructing the index in two levels: the front-end index and the back-end index. We formally prove that this two-level construction is identical to the relational normalization process. We call this process structural optimization of the n -gram index. The n -gram/2 L index has excellent properties: (1) it significantly reduces the size and improves the performance compared with the n -gram 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 real databases of 1 GB show that the size of the n -gram/2 L index is reduced by up to 1.9–2.4 times and, at the same time, the query performance is improved by up to 13.1 times compared with those of the n -gram index. We also compare the n -gram/2 L index with Makinen’s compact suffix array (CSA) (Proc. 11th Annual Symposium on Combinatorial Pattern Matching pp. 305–319, 2000) stored in disk. Experimental results show that the n -gram/2 L index outperforms the CSA when the query length is short (i.e., less than 15–20), and the CSA is similar to or better than the n -gram/2 L index when the query length is long (i.e., more than 15–20).

Journal

The VLDB JournalSpringer Journals

Published: Nov 1, 2008

References

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