7,707 research outputs found
Analysis of Signature Generation Schemes for Multiterm Queries In Linear Hashing with Superimposed Signatures
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
Analysis of Signature Generation Schemes for Multiterm Queries In Partitioned Signature File Environments
Our analysis explores the performance of three superimposed signature generation schemes as they are applied to a dynamic sigrtature file organization based on linear hashing: Linear Hashing with Superinzposed Signatures (LHSS). First scheme (SM) allows all terms set the same number of bits whereas the second and third methods (MMS and MMM) emphasize the terms with hlgh discriminatory power. In addition, M Mco nsiders the probaOiZity distribution of the number of query terms. The main contribution of the study is the combination of signature generation and signature file organization concepts together
with the relaxation of the single term query and uniform frequency assumptions. The derivation of the performance evaluation formulas are provided as well as the analysis of various experimental settings. Results indicate that MMM outperforms the others as terms become more distinctive in their discriminatory power. MMM accomplishes the highest savings in retrieval eficiency for the high query weight case. We also discuss the applicability of the derivations to other partitioned signature organizations providing a detailed analysis of Fixed Prefix Partitioning (FPP) as an example. Finally, an appro.ximate perfortnance evaluation formula that works for both FPP and LHSS is modijied to account for the multiterm case
Noise-tolerant approximate blocking for dynamic real-time entity resolution
Entity resolution is the process of identifying records in one or multiple data sources that represent the same real-world entity. This process needs to deal with noisy data that contain for example wrong pronunciation or spelling errors. Many real world applications require rapid responses for entity queries on dynamic datasets. This brings challenges to existing approaches which are mainly aimed at the batch matching of records in static data. Locality sensitive hashing (LSH) is an approximate
blocking approach that hashes objects within a certain distance into the same block with high probability. How to make approximate blocking approaches scalable to large datasets and effective for entity resolution in real-time remains an open question. Targeting this problem, we propose a noise-tolerant approximate blocking approach to index records based on their distance ranges using LSH and sorting trees within large sized hash blocks. Experiments conducted on both synthetic and real-world
datasets show the effectiveness of the proposed approach
Simple, compact and robust approximate string dictionary
This paper is concerned with practical implementations of approximate string
dictionaries that allow edit errors. In this problem, we have as input a
dictionary of strings of total length over an alphabet of size
. Given a bound and a pattern of length , a query has to
return all the strings of the dictionary which are at edit distance at most
from , where the edit distance between two strings and is defined as
the minimum-cost sequence of edit operations that transform into . The
cost of a sequence of operations is defined as the sum of the costs of the
operations involved in the sequence. In this paper, we assume that each of
these operations has unit cost and consider only three operations: deletion of
one character, insertion of one character and substitution of a character by
another. We present a practical implementation of the data structure we
recently proposed and which works only for one error. We extend the scheme to
. Our implementation has many desirable properties: it has a very
fast and space-efficient building algorithm. The dictionary data structure is
compact and has fast and robust query time. Finally our data structure is
simple to implement as it only uses basic techniques from the literature,
mainly hashing (linear probing and hash signatures) and succinct data
structures (bitvectors supporting rank queries).Comment: Accepted to a journal (19 pages, 2 figures
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