scholarly journals Deep Web Crawler: Exploring and Re-ranking of Web Forms

2016 ◽  
Vol 150 (1) ◽  
pp. 32-35
Author(s):  
Rashmi K. ◽  
Vijaya Kumar ◽  
H. S.
Keyword(s):  
Deep Web ◽  
Web Crawler

A Novel Architecture for Deep Web Crawler

2011 ◽  
Vol 6 (1) ◽  
pp. 25-48 ◽  
Author(s):  
Dilip Kumar Sharma ◽  
A. K. Sharma
Keyword(s):  
Cost Effective ◽  
Deep Web ◽  
Web Data ◽  
Web Crawler ◽  
Web Information ◽  
General Search ◽  
Web Crawlers

A traditional crawler picks up a URL, retrieves the corresponding page and extracts various links, adding them to the queue. A deep Web crawler, after adding links to the queue, checks for forms. If forms are present, it processes them and retrieves the required information. Various techniques have been proposed for crawling deep Web information, but much remains undiscovered. In this paper, the authors analyze and compare important deep Web information crawling techniques to find their relative limitations and advantages. To minimize limitations of existing deep Web crawlers, a novel architecture is proposed based on QIIIEP specifications (Sharma & Sharma, 2009). The proposed architecture is cost effective and has features of privatized search and general search for deep Web data hidden behind html forms.

Search Engine

The Dark Web ◽  
2018 ◽  
pp. 359-374
Author(s):  
Dilip Kumar Sharma ◽  
A. K. Sharma
Keyword(s):  
Computer Networks ◽  
Search Engines ◽  
Web Search ◽  
Relevant Information ◽  
Vital Role ◽  
Deep Web ◽  
Telecommunication Networks ◽  
Web Pages ◽  
Web Crawler ◽  
Main Components

ICT plays a vital role in human development through information extraction and includes computer networks and telecommunication networks. One of the important modules of ICT is computer networks, which are the backbone of the World Wide Web (WWW). Search engines are computer programs that browse and extract information from the WWW in a systematic and automatic manner. This paper examines the three main components of search engines: Extractor, a web crawler which starts with a URL; Analyzer, an indexer that processes words on the web page and stores the resulting index in a database; and Interface Generator, a query handler that understands the need and preferences of the user. This paper concentrates on the information available on the surface web through general web pages and the hidden information behind the query interface, called deep web. This paper emphasizes the Extraction of relevant information to generate the preferred content for the user as the first result of his or her search query. This paper discusses the aspect of deep web with analysis of a few existing deep web search engines.

A Novel Architecture for Deep Web Crawler

The Dark Web ◽  
2018 ◽  
pp. 334-358
Author(s):  
Dilip Kumar Sharma ◽  
A. K. Sharma
Keyword(s):  
Cost Effective ◽  
Deep Web ◽  
Web Data ◽  
Web Crawler ◽  
Web Information ◽  
General Search ◽  
Web Crawlers

A traditional crawler picks up a URL, retrieves the corresponding page and extracts various links, adding them to the queue. A deep Web crawler, after adding links to the queue, checks for forms. If forms are present, it processes them and retrieves the required information. Various techniques have been proposed for crawling deep Web information, but much remains undiscovered. In this paper, the authors analyze and compare important deep Web information crawling techniques to find their relative limitations and advantages. To minimize limitations of existing deep Web crawlers, a novel architecture is proposed based on QIIIEP specifications (Sharma & Sharma, 2009). The proposed architecture is cost effective and has features of privatized search and general search for deep Web data hidden behind html forms.

Search Engine

2011 ◽  
Vol 3 (2) ◽  
pp. 38-51 ◽  
Author(s):  
Dilip Kumar Sharma ◽  
A. K. Sharma
Keyword(s):  
Computer Networks ◽  
Search Engines ◽  
Web Search ◽  
Relevant Information ◽  
Vital Role ◽  
Deep Web ◽  
Telecommunication Networks ◽  
Web Pages ◽  
Web Crawler ◽  
Main Components

ICT plays a vital role in human development through information extraction and includes computer networks and telecommunication networks. One of the important modules of ICT is computer networks, which are the backbone of the World Wide Web (WWW). Search engines are computer programs that browse and extract information from the WWW in a systematic and automatic manner. This paper examines the three main components of search engines: Extractor, a web crawler which starts with a URL; Analyzer, an indexer that processes words on the web page and stores the resulting index in a database; and Interface Generator, a query handler that understands the need and preferences of the user. This paper concentrates on the information available on the surface web through general web pages and the hidden information behind the query interface, called deep web. This paper emphasizes the Extraction of relevant information to generate the preferred content for the user as the first result of his or her search query. This paper discusses the aspect of deep web with analysis of a few existing deep web search engines.

Design and implementation of crawling algorithm to collect deep web information for web archiving

2018 ◽  
Vol 52 (2) ◽  
pp. 266-277 ◽  
Author(s):  
Hyo-Jung Oh ◽  
Dong-Hyun Won ◽  
Chonghyuck Kim ◽  
Sung-Hee Park ◽  
Yong Kim
Keyword(s):  
Deep Web ◽  
Web Crawler ◽  
Web Archiving ◽  
Web Browser ◽  
Web Documents ◽  
Content Type ◽  
Web Document ◽  
Web Information ◽  
Web Crawlers ◽  
The Web

Purpose The purpose of this paper is to describe the development of an algorithm for realizing web crawlers that automatically collect dynamically generated webpages from the deep web. Design/methodology/approach This study proposes and develops an algorithm to collect web information as if the web crawler gathers static webpages by managing script commands as links. The proposed web crawler actually experiments with the algorithm by collecting deep webpages. Findings Among the findings of this study is that if the actual crawling process provides search results as script pages, the outcome only collects the first page. However, the proposed algorithm can collect deep webpages in this case. Research limitations/implications To use a script as a link, a human must first analyze the web document. This study uses the web browser object provided by Microsoft Visual Studio as a script launcher, so it cannot collect deep webpages if the web browser object cannot launch the script, or if the web document contains script errors. Practical implications The research results show deep webs are estimated to have 450 to 550 times more information than surface webpages, and it is difficult to collect web documents. However, this algorithm helps to enable deep web collection through script runs. Originality/value This study presents a new method to be utilized with script links instead of adopting previous keywords. The proposed algorithm is available as an ordinary URL. From the conducted experiment, analysis of scripts on individual websites is needed to employ them as links.

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