scholarly journals Compressed Index for Dictionary Matching

2008 ◽  
Author(s):  
Wing-Kai Hon ◽  
Tak-Wah Lam ◽  
Rahul Shah ◽  
Siu-Lung Tam ◽  
Jeffrey Scott Vitter
Keyword(s):  
Dictionary Matching ◽  
Compressed Index

Erratum to: A Grouping Approach for Succinct Dynamic Dictionary Matching

Algorithmica ◽  
2016 ◽  
Vol 77 (1) ◽  
pp. 151-151
Author(s):  
Guy Feigenblat ◽  
Ely Porat ◽  
Ariel Shiftan
Keyword(s):  
Dictionary Matching

A Compressed Index for Hamming Distances

2014 ◽  
pp. 113-126
Author(s):  
Francisco Santoyo ◽  
Edgar Chávez ◽  
Eric S. Téllez
Keyword(s):  
Compressed Index

Development of a Novel Compressed Index-Query Web Search Engine Model

2011 ◽  
Vol 6 (3) ◽  
pp. 39-56 ◽  
Author(s):  
Hussein Al-Bahadili ◽  
Saif Al-Saab
Keyword(s):  
Data Compression ◽  
Search Engine ◽  
Web Search ◽  
Server Side ◽  
Engine Model ◽  
Web Search Engine ◽  
Index Index ◽  
Compression Layer ◽  
Compressed Index ◽  
Space Requirements

In this paper, the authors present a description of a new Web search engine model, the compressed index-query (CIQ) Web search engine model. This model incorporates two bit-level compression layers implemented at the back-end processor (server) side, one layer resides after the indexer acting as a second compression layer to generate a double compressed index (index compressor), and the second layer resides after the query parser for query compression (query compressor) to enable bit-level compressed index-query search. The data compression algorithm used in this model is the Hamming codes-based data compression (HCDC) algorithm, which is an asymmetric, lossless, bit-level algorithm permits CIQ search. The different components of the new Web model are implemented in a prototype CIQ test tool (CIQTT), which is used as a test bench to validate the accuracy and integrity of the retrieved data and evaluate the performance of the proposed model. The test results demonstrate that the proposed CIQ model reduces disk space requirements and searching time by more than 24%, and attains a 100% agreement when compared with an uncompressed model.

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