Optimized Extraction of Records from the Web Using Signal Processing and Machine Learning | Zendy

Roberto Panerai Velloso | Zendy; Carina F. Dorneles | Zendy

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Optimized Extraction of Records from the Web Using Signal Processing and Machine Learning

Author(s) -

Roberto Panerai Velloso,

Carina F. Dorneles

Publication year - 2020

Language(s) - English

Resource type - Conference proceedings

DOI - 10.5753/sbbd.2020.13629

Subject(s) - computer science , information extraction , extraction (chemistry) , signal (programming language) , upper and lower bounds , data mining , machine learning , artificial intelligence , programming language , mathematics , chemistry , chromatography , mathematical analysis

In this paper, we present an optimization of our previous record extraction approach from web pages. The proposed optimization improves the upper bound from O(nlogn) to O(n) while maintaining the same qualitative results as before (i.e., no loss in efficacy). We have achieved the following results: a 47% improvement in runtime efficiency when compared to our previous work and 95% f-score (same as our previous work).

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