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Analysis of Signature Generation Schemes for Multiterm Queries In Linear Hashing with Superimposed Signatures

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Show simple item record Can, Fazli en_US Ertugay, Osman en_US 2008-07-22T19:31:40Z en_US 2013-07-10T15:06:34Z 2008-07-22T19:31:40Z en_US 2013-07-10T15:06:34Z 1995-12-01 en_US 2008-03-17 en_US
dc.identifier.uri en_US
dc.description.abstract Signature files provide efficient retrieval of data by reflecting the essence of the data objects into bit patterns. Our analysis explores the performance of three superimposed signature generation schemes as they are applied to a dynamic signature file organization based on linear hashing: Linear Hashing with Superimposed Signatures (LHSS). The first scheme (SM) allows all terms set the same number of bits whereas the second and third schemes (MMS aid MMM) emphasize the terms with high discriminatory power. In addition, MMM considers the probability distribution of the number of query terms. The main contribution of the study is a detailed analysis of LHSS in multiterm query environments by incorporating the term discrimination values based on document and query frequencies. The approach of the study can also be extended to other signature file access methods based on partitioning. The derivation of the performance evaluation formulas, the simulation results based on these formulas for various experimental settings, and the implementation results based on INSPEC and NPL text databases are provided. Results indicate that MMM and MMS outperform SM in all cases in terms of access savings, especially when terms become more distinctive. MMM slightly outperforms MMS in high weight and low weight query cases. The performance gap among all three schemes decreases as the database size increases, and as the signature size increases the performances of MMM and MMS decrease and converge to that of the SM scheme when the hashing level is fixed. en_US
dc.title Analysis of Signature Generation Schemes for Multiterm Queries In Linear Hashing with Superimposed Signatures en_US
dc.type Text en_US
dc.type.genre Report en_US

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