A Novel Architecture for Deep Web Crawler

Dilip Kumar Sharma; A. K. Sharma

doi:10.4018/jitwe.2011010103

A Novel Architecture for Deep Web Crawler

The Dark Web ◽

10.4018/978-1-5225-3163-0.ch015 ◽

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.

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A Novel Architecture for Deep Web Crawler

Network and Communication Technology Innovations for Web and IT Advancement ◽

10.4018/978-1-4666-2157-2.ch007 ◽

2014 ◽

pp. 106-129

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.

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

Data Technologies and Applications ◽

10.1108/dta-07-2017-0053 ◽

2018 ◽

Vol 52 (2) ◽

pp. 266-277 ◽

Cited By ~ 2

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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Deep Web Information Retrieval Process

The Dark Web ◽

10.4018/978-1-5225-3163-0.ch007 ◽

2018 ◽

pp. 114-137

Author(s):

Dilip Kumar Sharma ◽

A. K. Sharma

Keyword(s):

Information Retrieval ◽

General Framework ◽

Deep Web ◽

Web Searching ◽

Web Content ◽

Web Information Retrieval ◽

Technical Literature ◽

Web Information ◽

Web Crawlers ◽

Surface Web

Web crawlers specialize in downloading web content and analyzing and indexing from surface web, consisting of interlinked HTML pages. Web crawlers have limitations if the data is behind the query interface. Response depends on the querying party's context in order to engage in dialogue and negotiate for the information. In this paper, the authors discuss deep web searching techniques. A survey of technical literature on deep web searching contributes to the development of a general framework. Existing frameworks and mechanisms of present web crawlers are taxonomically classified into four steps and analyzed to find limitations in searching the deep web.

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Efficient Methodology for Deep Web Data Extraction

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i1s.1769 ◽

2021 ◽

Vol 12 (1S) ◽

pp. 286-293

Author(s):

Shilpa Deshmukh, Et. al.

Keyword(s):

Information Extraction ◽

Data Extraction ◽

Deep Web ◽

Web Pages ◽

Web Data ◽

Web Information Extraction ◽

Web Data Extraction ◽

Web Information ◽

Assessment Measure ◽

Enormous Number

Deep Web substance are gotten to by inquiries submitted to Web information bases and the returned information records are enwrapped in progressively created Web pages (they will be called profound Web pages in this paper). Removing organized information from profound Web pages is a difficult issue because of the fundamental mind boggling structures of such pages. As of not long ago, an enormous number of strategies have been proposed to address this issue, however every one of them have characteristic impediments since they are Web-page-programming-language subordinate. As the mainstream two-dimensional media, the substance on Web pages are constantly shown routinely for clients to peruse. This inspires us to look for an alternate path for profound Web information extraction to beat the constraints of past works by using some fascinating normal visual highlights on the profound Web pages. In this paper, a novel vision-based methodology that is Visual Based Deep Web Data Extraction (VBDWDE) Algorithm is proposed. This methodology basically uses the visual highlights on the profound Web pages to execute profound Web information extraction, including information record extraction and information thing extraction. We additionally propose another assessment measure amendment to catch the measure of human exertion expected to create wonderful extraction. Our investigations on a huge arrangement of Web information bases show that the proposed vision-based methodology is exceptionally viable for profound Web information extraction.

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Deep Web Information Retrieval Process

International Journal of Information Technology and Web Engineering ◽

10.4018/jitwe.2010010101 ◽

2010 ◽

Vol 5 (1) ◽

pp. 1-22 ◽

Cited By ~ 11

Author(s):

Dilip Kumar Sharma ◽

A. K. Sharma

Keyword(s):

Deep Web ◽

Web Searching ◽

Retrieval Process ◽

Query Interface ◽

Web Content ◽

Web Information Retrieval ◽

Technical Literature ◽

Web Information ◽

Web Crawlers ◽

Surface Web

Web crawlers specialize in downloading web content and analyzing and indexing from surface web, consisting of interlinked HTML pages. Web crawlers have limitations if the data is behind the query interface. Response depends on the querying party’s context in order to engage in dialogue and negotiate for the information. In this article, the authors discuss deep web searching techniques. A survey of technical literature on deep web searching contributes to the development of a general framework. Existing frameworks and mechanisms of present web crawlers are taxonomically classified into four steps and analyzed to find limitations in searching the deep web.

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Deep Web Information Retrieval Process

Models for Capitalizing on Web Engineering Advancements ◽

10.4018/978-1-4666-0023-2.ch005 ◽

2012 ◽

pp. 75-96

Author(s):

Dilip Kumar Sharma ◽

A. K. Sharma

Keyword(s):

Information Retrieval ◽

General Framework ◽

Deep Web ◽

Web Searching ◽

Web Content ◽

Web Information Retrieval ◽

Technical Literature ◽

Web Information ◽

Web Crawlers ◽

Surface Web

Web crawlers specialize in downloading web content and analyzing and indexing from surface web, consisting of interlinked HTML pages. Web crawlers have limitations if the data is behind the query interface. Response depends on the querying party’s context in order to engage in dialogue and negotiate for the information. In this paper, the authors discuss deep web searching techniques. A survey of technical literature on deep web searching contributes to the development of a general framework. Existing frameworks and mechanisms of present web crawlers are taxonomically classified into four steps and analyzed to find limitations in searching the deep web.

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Cross-Fertilizing Deep Web Analysis and Ontology Enrichment

10.31219/osf.io/b3fvz ◽

2017 ◽

Author(s):

Marilena Oita ◽

Antoine Amarilli ◽

Pierre Senellart

Keyword(s):

Domain Knowledge ◽

Deep Web ◽

Web Pages ◽

Complete Understanding ◽

Specific Knowledge ◽

Domain Specific ◽

Domain Specific Knowledge ◽

Web Crawlers ◽

New Perspective ◽

The Impact

Deep Web databases, whose content is presented as dynamically-generated Web pages hidden behind forms, have mostly been left unindexed by search engine crawlers. In order to automatically explore this mass of information, many current techniques assume the existence of domain knowledge, which is costly to create and maintain. In this article, we present a new perspective on form understanding and deep Web data acquisition that does not require any domain-specific knowledge. Unlike previous approaches, we do not perform the various steps in the process (e.g., form understanding, record identification, attribute labeling) independently but integrate them to achieve a more complete understanding of deep Web sources. Through information extraction techniques and using the form itself for validation, we reconcile input and output schemas in a labeled graph which is further aligned with a generic ontology. The impact of this alignment is threefold: first, the resulting semantic infrastructure associated with the form can assist Web crawlers when probing the form for content indexing; second, attributes of response pages are labeled by matching known ontology instances, and relations between attributes are uncovered; and third, we enrich the generic ontology with facts from the deep Web.

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