A Survey on Removal of Duplicate Records in Database

Karthigha, M.

doi:10.17485/ijst/2013/v6i4.11

IJST

2013

DOI: 10.17485/ijst/2013/v6i4.11

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A Survey on Removal of Duplicate Records in Database

M. Karthigha¹

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2022

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Automating Duplicate Detection for Lexical Heterogeneous Web Databases

Ahlawat¹,

Sagar²

2022

RACSC

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Introduction: The need for efficient search engines has been identified with the ever-increasing technological advancement and huge growing demand of data on the web. Method: Automating duplicate detection over query results in identifying the records from multiple web databases that point to the similar real-world entity and returns non-matching records to the end-users. The proposed algorithm in this paper is based upon an unsupervised approach with classifiers over heterogeneous web databases that return more accurate results with high precision, F-measure, and recall. Different assessments are also executed to analyze the efficacy of the proposed algorithm for identification of the duplicates. Result: Results show that the proposed algorithm has greater precision, F-score measure, and the same recall values as compared to standard UDD. Conclusion: This paper concludes that the proposed algorithm outperforms standard UDD. Discussion: This paper aims to introduce an algorithm that automates the process of duplicate detection for lexical heterogeneous web databases.

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Automating Duplicate Detection for Lexical Heterogeneous Web Databases

Ahlawat¹,

Sagar²

2022

RACSC

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A Survey on Removal of Duplicate Records in Database

Cited by 1 publication

References 0 publications

Automating Duplicate Detection for Lexical Heterogeneous Web Databases

Automating Duplicate Detection for Lexical Heterogeneous Web Databases

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