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An Efficiently harvesting Deep Web Interfaces based on Two Stage Crawler

Published on June 2018 by Rohini Navnathkhedkar, Madhuri Dalal
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International Conference on Emerging Trends in Computing and Communication
Foundation of Computer Science USA
ICETCC2017 - Number 3
June 2018
Authors: Rohini Navnathkhedkar, Madhuri Dalal
48b0d5f2-8db8-4ccc-bd43-79d3c495e666

Rohini Navnathkhedkar, Madhuri Dalal . An Efficiently harvesting Deep Web Interfaces based on Two Stage Crawler. International Conference on Emerging Trends in Computing and Communication. ICETCC2017, 3 (June 2018), 18-22.

@article{
author = { Rohini Navnathkhedkar, Madhuri Dalal },
title = { An Efficiently harvesting Deep Web Interfaces based on Two Stage Crawler },
journal = { International Conference on Emerging Trends in Computing and Communication },
issue_date = { June 2018 },
volume = { ICETCC2017 },
number = { 3 },
month = { June },
year = { 2018 },
issn = 0975-8887,
pages = { 18-22 },
numpages = 5,
url = { /proceedings/icetcc2017/number3/29474-c129/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Emerging Trends in Computing and Communication
%A Rohini Navnathkhedkar
%A Madhuri Dalal
%T An Efficiently harvesting Deep Web Interfaces based on Two Stage Crawler
%J International Conference on Emerging Trends in Computing and Communication
%@ 0975-8887
%V ICETCC2017
%N 3
%P 18-22
%D 2018
%I International Journal of Computer Applications
Abstract

As deep web grows at a very fast pace, there has been increased interest in techniques that help efficiently locate deep-web interfaces. However, due to the large volume of web resources and the dynamic nature of deep web, achieving wide coverage and high efficiency is a challenging issue. We propose a two-stage framework, for harvesting deep web interfaces. In the first stage of harvesting, performs site-based searching for center pages with the help of search engines, avoiding visiting a large number of pages. To achieve more accurate results for a focused crawl ranks websites to prioritize highly relevant ones for a given topic. In the second stage, it achieves fast in-site searching by excavating most relevant links with an adaptive link-ranking.

References
  1. Feng Zhao, Jingyu Zhou, Chang Nie, Heqing Huang, Hai Jin "SmartCrawler: A Two Stage Crawler for efficiently harvesting Deep-Web interfaces" IEEE Transactions on Services Computing Volume: 99 PP Year: 2015.
  2. L. Barbosa and J. Freire, "An adaptive crawler for locating hidden web entry points," in Proc. 16th Int. Conf. World Wide Web, 2007, pp. 441–450.
  3. . Olston and M. Najork , "Web Crawling", Foundations and Trends in Information Retrieval, vol. 4, No. 3 ,pp. 175–246, 20.
  4. Y. He, D. Xin, V. Ganti, S. Rajaraman, and N. Shah, "Crawling deep web entity pages," in Proc. 6th ACM Int. Conf. Web Search Data Mining, 2013, pp. 355–364.
  5. Barbosa and J. Freire, "Searching for hidden-web databases,"in Proc. 8th Int. Workshop Web Databases, 2005, pp. 1–6.
  6. Rabia and Sami, Lalitha K. , "Understanding the Deep Web" (2010). Library Philosophy and Practice (e-journal). Paper 364. http://digitalcommons. unl. edu/libphilprac.
Index Terms

Computer Science
Information Sciences

Keywords

Deep Web Ranking Adaptive Learning Two-stage Crawler.