Abstract: This paper mainly introduces the web crawler system structure. Through the analysis of the architecture of web crawler we obtainedfive functional components. They respectively are: the URL scheduler, DNS resolver, web crawling modules, web page analyzer, and the URL judgment device. To build an efficient Web crawler the key technology is to design an efficient Web crawler, so as to solve the challenges that the huge scale of Web brings
SmartCrawler is a two-stage crawler for efficiently harvesting deep-web interfaces. In the first stage, SmartCrawler performs site-based searching to identify relevant websites using search engines and site ranking, avoiding visiting many irrelevant pages. In the second stage, SmartCrawler prioritizes links within websites using adaptive link ranking to efficiently find searchable forms. Experimental results showed SmartCrawler achieved higher harvest rates of deep-web interfaces than other crawlers by using its two-stage approach and adaptive learning techniques.
This document proposes that web servers export metadata archives describing their content to make crawling more efficient. It suggests web servers partition metadata into compressed files by modification date, MIME type, and size to reduce bandwidth. Exporting metadata in this way could help crawlers identify updated pages, discover pages without downloading HTML, and know bandwidth needs upfront. The proposed partitioning scheme balances file size, number of files, and server resources.
This document discusses web crawling and summarizes key aspects. It begins by outlining the basic process of web crawling, which involves maintaining a frontier of unvisited URLs, fetching pages from the frontier, extracting links, and adding new links to the frontier. The document notes that while some crawlers exhaustively crawl the web, others use preferential or topical crawling to focus on specific topics or applications. It discusses challenges in evaluating crawlers and comparing their performance.
This document summarizes a research paper on implementing a web crawler on a client machine rather than a server. It describes the basic workings of web crawlers, including downloading pages, extracting links, and recursively visiting pages. It then presents the design of a crawler that uses multiple HTTP connections and asynchronous downloading via multiple threads to optimize performance on a client system. The software architecture includes modules for URL scheduling, multi-threaded downloading, parsing pages to extract URLs/content, and storing downloaded data in a database.
Smart Crawler: A Two Stage Crawler for Concept Based Semantic Search Engine.iosrjce
The internet is a vast collection of billions of web pages containing terabytes of information
arranged in thousands of servers using HTML. The size of this collection itself is a formidable obstacle in
retrieving necessary and relevant information. This made search engines an important part of our lives. Search
engines strive to retrieve information as relevant as possible. One of the building blocks of search engines is the
Web Crawler. We tend to propose a two - stage framework, specifically two smart Crawler, for efficient
gathering deep net interfaces. Within the first stage, smart Crawler, performs site-based sorting out centre
pages with the assistance of search engines, avoiding visiting an oversized variety of pages. To realize
additional correct results for a targeted crawl, smart Crawler, ranks websites to order extremely relevant ones
for a given topic. Within the second stage, smart Crawler, achieves quick in – site looking by excavating most
relevant links with associate degree accommodative link -ranking
IRJET - Review on Search Engine OptimizationIRJET Journal
This document discusses search engine optimization (SEO) and how search engines work. It covers the key processes of crawling, indexing, and ranking that search engines use to find and organize web content. Crawling involves search engine bots finding and downloading web pages. Indexing processes and stores the crawled content in a searchable database. Ranking determines the order search results are displayed, with more relevant pages ranking higher. The document provides technical details on Google's architecture and algorithms to perform these core functions at scale across the vastness of the internet.
This document provides an overview of a web crawler project implemented in Java. It includes sections on the theoretical background of web crawlers, using a DOM parser to parse XML files, software analysis including requirements and design, and software testing. The project involves building a web crawler that takes a fully built XML website and recursively visits all pages, saving links in a hash table and then printing them. It parses XML files into a DOM representation and uses classes like Main and WebCrawler to implement the crawling functionality.
Smart Crawler Base Paper A two stage crawler for efficiently harvesting deep-...Rana Jayant
The document describes a two-stage crawling framework called SmartCrawler for efficiently harvesting deep-web interfaces. In the first stage, SmartCrawler performs site-based searching to identify relevant websites using reverse searching and site ranking. It prioritizes highly relevant websites for focused crawling. In the second stage, SmartCrawler explores within selected websites by ranking links adaptively to excavate searchable forms efficiently while achieving wider coverage. Experimental results on representative domains show SmartCrawler retrieves more deep-web interfaces at higher rates than other crawlers.
SmartCrawler is a two-stage crawler for efficiently harvesting deep-web interfaces. In the first stage, SmartCrawler performs site-based searching to identify relevant websites using search engines and site ranking, avoiding visiting many irrelevant pages. In the second stage, SmartCrawler prioritizes links within websites using adaptive link ranking to efficiently find searchable forms. Experimental results showed SmartCrawler achieved higher harvest rates of deep-web interfaces than other crawlers by using its two-stage approach and adaptive learning techniques.
This document proposes that web servers export metadata archives describing their content to make crawling more efficient. It suggests web servers partition metadata into compressed files by modification date, MIME type, and size to reduce bandwidth. Exporting metadata in this way could help crawlers identify updated pages, discover pages without downloading HTML, and know bandwidth needs upfront. The proposed partitioning scheme balances file size, number of files, and server resources.
This document discusses web crawling and summarizes key aspects. It begins by outlining the basic process of web crawling, which involves maintaining a frontier of unvisited URLs, fetching pages from the frontier, extracting links, and adding new links to the frontier. The document notes that while some crawlers exhaustively crawl the web, others use preferential or topical crawling to focus on specific topics or applications. It discusses challenges in evaluating crawlers and comparing their performance.
This document summarizes a research paper on implementing a web crawler on a client machine rather than a server. It describes the basic workings of web crawlers, including downloading pages, extracting links, and recursively visiting pages. It then presents the design of a crawler that uses multiple HTTP connections and asynchronous downloading via multiple threads to optimize performance on a client system. The software architecture includes modules for URL scheduling, multi-threaded downloading, parsing pages to extract URLs/content, and storing downloaded data in a database.
Smart Crawler: A Two Stage Crawler for Concept Based Semantic Search Engine.iosrjce
The internet is a vast collection of billions of web pages containing terabytes of information
arranged in thousands of servers using HTML. The size of this collection itself is a formidable obstacle in
retrieving necessary and relevant information. This made search engines an important part of our lives. Search
engines strive to retrieve information as relevant as possible. One of the building blocks of search engines is the
Web Crawler. We tend to propose a two - stage framework, specifically two smart Crawler, for efficient
gathering deep net interfaces. Within the first stage, smart Crawler, performs site-based sorting out centre
pages with the assistance of search engines, avoiding visiting an oversized variety of pages. To realize
additional correct results for a targeted crawl, smart Crawler, ranks websites to order extremely relevant ones
for a given topic. Within the second stage, smart Crawler, achieves quick in – site looking by excavating most
relevant links with associate degree accommodative link -ranking
IRJET - Review on Search Engine OptimizationIRJET Journal
This document discusses search engine optimization (SEO) and how search engines work. It covers the key processes of crawling, indexing, and ranking that search engines use to find and organize web content. Crawling involves search engine bots finding and downloading web pages. Indexing processes and stores the crawled content in a searchable database. Ranking determines the order search results are displayed, with more relevant pages ranking higher. The document provides technical details on Google's architecture and algorithms to perform these core functions at scale across the vastness of the internet.
This document provides an overview of a web crawler project implemented in Java. It includes sections on the theoretical background of web crawlers, using a DOM parser to parse XML files, software analysis including requirements and design, and software testing. The project involves building a web crawler that takes a fully built XML website and recursively visits all pages, saving links in a hash table and then printing them. It parses XML files into a DOM representation and uses classes like Main and WebCrawler to implement the crawling functionality.
Smart Crawler Base Paper A two stage crawler for efficiently harvesting deep-...Rana Jayant
The document describes a two-stage crawling framework called SmartCrawler for efficiently harvesting deep-web interfaces. In the first stage, SmartCrawler performs site-based searching to identify relevant websites using reverse searching and site ranking. It prioritizes highly relevant websites for focused crawling. In the second stage, SmartCrawler explores within selected websites by ranking links adaptively to excavate searchable forms efficiently while achieving wider coverage. Experimental results on representative domains show SmartCrawler retrieves more deep-web interfaces at higher rates than other crawlers.
The document proposes a two-stage crawler called SmartCrawler to efficiently harvest deep-web interfaces. In the first stage, SmartCrawler performs site-based searching to identify relevant websites while avoiding visiting many pages. In the second stage, SmartCrawler achieves fast in-site searching by prioritizing relevant links using an adaptive link-ranking approach. Experimental results show SmartCrawler retrieves deep-web interfaces more efficiently than other crawlers.
Smart Crawler -A Two Stage Crawler For Efficiently Harvesting Deep WebS Sai Karthik
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, namely Smart Crawler, for efficient harvesting deep web interfaces. In the first stage, Smart Crawler 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, Smart Crawler ranks websites to prioritize highly relevant ones for a given topic. In the second stage, Smart Crawler achieves fast in-site searching by excavating most relevant links with an adaptive learning.
Web Crawling Using Location Aware Techniqueijsrd.com
Most of the modern search engines are based in the well-known web crawling model. A centralized crawler or a farm of parallel crawlers is dedicated to the process of downloading the web pages and updating the database. However, the model fails to keep pace with the size of the web, and the frequency of changes in the web documents. For this reason, there have been some recent proposals for distributed web crawling, trying to alleviate the bottlenecks in the search engines and scale with the web . In this work, facilitating the flexibility and extensibility of Mobile Agent , we consider adding location awareness to distributed web crawling, so that each web page is crawled from the web crawler logically most near to it, the crawler that could download it the faster. We evaluate the location aware approach and show that it can reduce the time demanded for downloading to one order of magnitude.
Web crawling involves automated programs called crawlers or spiders that browse the web methodically to index web pages for search engines. Crawlers start from seed URLs and extract links from visited pages to discover new pages, repeating the process until a desired size or time limit is reached. Crawlers are used by search engines to build indexes of web content and ensure freshness through revisiting URLs. Challenges include the web's large size, fast changes, and dynamic content generation. APIs allow programmatic access to web services and information through REST, HTTP POST, and SOAP.
The Research on Related Technologies of Web CrawlerIRJESJOURNAL
ABSTRACT: Web crawler is a computer program which can automatically download page or automation scripts, and it is an important part of the search engine. With the rapid growth of Internet, more and more network resources, search engines have been unable to meet people's need for useful information. As an important part of the search engine, web crawler is becoming more and more important role. This article mainly discusses about the working principle, classification of web crawler, etc were related in this paper. And then discusses the research and the subject of the search engine important topic web crawler.
The webinar will present the SemaGrow demonstrator “Web Crawler + AgroTagger”, in order to collect feedback, ideas and comments about the status of the development and how the demonstrator helps to overcome data problems.
SemaGrow is a project funded by the Seventh Framework Programme (FP7) of the European Commission, aiming at developing algorithms, infrastructures and methodologies to cope with large data volumes and real time performance.
In this context, FAO is providing a component than can be used to crawl the Web, giving a meaning to discovered resources by using the AgroTagger, which can assign some AGROVOC URIs to resources gathered by a Web crawler.
The demonstrator is publicly available at https://github.com/agrisfao/agrotagger.
Web crawlers, also known as robots or bots, are programs that systematically browse the internet and index websites for search engines. Crawlers follow links from seed URLs and download pages to extract new URLs to crawl. They use techniques like breadth-first crawling to efficiently discover as much of the web as possible. Crawlers must have policies to select pages, revisit sites, be polite to not overload websites, and coordinate distributed crawling. Their high-performance architecture is crucial for search engines to comprehensively index the large and constantly changing web.
The document discusses web crawlers, which are programs that download web pages to help search engines index websites. It explains that crawlers use strategies like breadth-first search and depth-first search to systematically crawl the web. The architecture of crawlers includes components like the URL frontier, DNS lookup, and parsing pages to extract links. Crawling policies determine which pages to download and when to revisit pages. Distributed crawling improves efficiency by using multiple coordinated crawlers.
A web crawler works by starting with a specified URL and recursively retrieving links within pages to build a crawl frontier of URLs to visit. It checks each URL to see if it exists and parses the page to extract new links, adding them to the frontier. This process continues recursively to a depth of around 5 levels typically to gather most on-site information before stopping to avoid getting trapped on pages with infinite loops of links.
The document discusses web crawling and provides an overview of the process. It defines web crawling as the process of gathering web pages to index them and support search. The objective is to quickly gather useful pages and link structures. The presentation covers the basic operation of crawlers including using a seed set of URLs and frontier of URLs to crawl. It describes common modules in crawler architecture like URL filtering tests. It also discusses topics like politeness, distributed crawling, DNS resolution, and types of crawlers.
Data scraping, Web crawlers are programs that extract information out of world wide web. This presentation aims to cover all relevant topics that one should know before building a crawler.
Smart crawlet A two stage crawler for efficiently harvesting deep web interf...Rana Jayant
The document proposes a two-stage "Smart Crawler" framework to efficiently harvest information from the deep web. In the first stage, the crawler performs site-based searching to avoid visiting many pages. In the second stage, it achieves fast in-site searching by excavating the most relevant links with an adaptive link-ranking. This approach allows the crawler to achieve both wide coverage and high efficiency when searching for information on a specific topic within the deep web.
Web crawlers, also known as spiders, are programs that systematically browse the World Wide Web to download pages for search engines and indexes. Crawlers start with a list of URLs and identify links on pages to add to the list to visit recursively. Effective crawlers require flexibility, high performance, fault tolerance, and maintainability. Crawling strategies include breadth-first, repetitive, targeted, and random walks. Selection, revisit, politeness, and parallelization policies help crawlers efficiently gather relevant information from the dynamic web. Distributed crawling employs multiple computers to index large portions of the internet in parallel.
Colloquim Report on Crawler - 1 Dec 2014Sunny Gupta
This document summarizes a web crawling project completed by Sunny Kumar for his Bachelor's degree. It describes a web crawler called Rotto Link Crawler that was developed to extract broken or dead links within a website. The crawler takes a seed URL, crawls every page of that site to find hyperlinks, and checks if any links are broken. If broken links or pages containing keywords are found, they are stored in a database. The project utilized various Python libraries and was built with a Flask backend and AngularJS frontend.
The document discusses options used in a web crawler code to control its behavior. The -r option enables recursive retrieval, allowing the crawler to follow links. The -spider option makes the crawler behave like a spider to check web pages are accessible without downloading them. The -domains option limits crawling to the specified domain only. The -l 5 option specifies a depth of 5 pages to avoid spider traps, and --tries=5 sets the number of retries if a connection fails.
A Novel Interface to a Web Crawler using VB.NET TechnologyIOSR Journals
This document describes the design of a web crawler interface created using VB.NET technology. It discusses the components and architecture of web crawlers, including the seed URLs, frontier, parser, and performance metrics used to evaluate crawlers. The high-level design of the crawler simulator is presented as an algorithm, and screenshots of the VB.NET user interface for the crawler are shown. The crawler was tested on the website www.cdlu.edu.in using different crawling algorithms like breadth-first and best-first, and the results were stored in an MS Access database.
The document describes a smart crawler system that uses remote method invocation (RMI) for automation. It crawls websites to find active links and relevant data for users. The crawler uses a breadth-first search approach to efficiently find links near the seed URL. It checks links to determine if they are active and distributes documents across multiple machines (M1 and M2) which continue crawling in parallel. Natural language processing techniques like bag-of-words and term frequency-inverse document frequency are used to analyze content and prioritize the most useful results for the user. The use of RMI allows different processes to be run on separate machines, improving the speed and scalability of the crawling system.
This document describes a two-stage crawler called Smart Crawler for concept-based semantic search engines. The first stage involves locating relevant websites for a given topic through site collecting, ranking, and classification. The second stage performs in-site crawling to uncover searchable content from the highest ranked sites using reverse searching and incremental site prioritization algorithms. The system aims to maximize retrieval of deep web content across many websites rather than just a few individual sites.
A web crawler is a program that browses the World Wide Web in an automated manner to download pages and identify links within pages to add to a queue for future downloading. It uses strategies like breadth-first or depth-first searching to systematically visit pages and index them to build a database for search engines. Crawling policies determine which pages to download, when to revisit pages, how to avoid overloading websites, and how to coordinate distributed crawlers.
The document proposes a two-stage crawler called SmartCrawler to efficiently harvest deep-web interfaces. In the first stage, SmartCrawler performs site-based searching to identify relevant websites while avoiding visiting many pages. In the second stage, SmartCrawler achieves fast in-site searching by prioritizing relevant links using an adaptive link-ranking approach. Experimental results show SmartCrawler retrieves deep-web interfaces more efficiently than other crawlers.
Smart Crawler -A Two Stage Crawler For Efficiently Harvesting Deep WebS Sai Karthik
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, namely Smart Crawler, for efficient harvesting deep web interfaces. In the first stage, Smart Crawler 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, Smart Crawler ranks websites to prioritize highly relevant ones for a given topic. In the second stage, Smart Crawler achieves fast in-site searching by excavating most relevant links with an adaptive learning.
Web Crawling Using Location Aware Techniqueijsrd.com
Most of the modern search engines are based in the well-known web crawling model. A centralized crawler or a farm of parallel crawlers is dedicated to the process of downloading the web pages and updating the database. However, the model fails to keep pace with the size of the web, and the frequency of changes in the web documents. For this reason, there have been some recent proposals for distributed web crawling, trying to alleviate the bottlenecks in the search engines and scale with the web . In this work, facilitating the flexibility and extensibility of Mobile Agent , we consider adding location awareness to distributed web crawling, so that each web page is crawled from the web crawler logically most near to it, the crawler that could download it the faster. We evaluate the location aware approach and show that it can reduce the time demanded for downloading to one order of magnitude.
Web crawling involves automated programs called crawlers or spiders that browse the web methodically to index web pages for search engines. Crawlers start from seed URLs and extract links from visited pages to discover new pages, repeating the process until a desired size or time limit is reached. Crawlers are used by search engines to build indexes of web content and ensure freshness through revisiting URLs. Challenges include the web's large size, fast changes, and dynamic content generation. APIs allow programmatic access to web services and information through REST, HTTP POST, and SOAP.
The Research on Related Technologies of Web CrawlerIRJESJOURNAL
ABSTRACT: Web crawler is a computer program which can automatically download page or automation scripts, and it is an important part of the search engine. With the rapid growth of Internet, more and more network resources, search engines have been unable to meet people's need for useful information. As an important part of the search engine, web crawler is becoming more and more important role. This article mainly discusses about the working principle, classification of web crawler, etc were related in this paper. And then discusses the research and the subject of the search engine important topic web crawler.
The webinar will present the SemaGrow demonstrator “Web Crawler + AgroTagger”, in order to collect feedback, ideas and comments about the status of the development and how the demonstrator helps to overcome data problems.
SemaGrow is a project funded by the Seventh Framework Programme (FP7) of the European Commission, aiming at developing algorithms, infrastructures and methodologies to cope with large data volumes and real time performance.
In this context, FAO is providing a component than can be used to crawl the Web, giving a meaning to discovered resources by using the AgroTagger, which can assign some AGROVOC URIs to resources gathered by a Web crawler.
The demonstrator is publicly available at https://github.com/agrisfao/agrotagger.
Web crawlers, also known as robots or bots, are programs that systematically browse the internet and index websites for search engines. Crawlers follow links from seed URLs and download pages to extract new URLs to crawl. They use techniques like breadth-first crawling to efficiently discover as much of the web as possible. Crawlers must have policies to select pages, revisit sites, be polite to not overload websites, and coordinate distributed crawling. Their high-performance architecture is crucial for search engines to comprehensively index the large and constantly changing web.
The document discusses web crawlers, which are programs that download web pages to help search engines index websites. It explains that crawlers use strategies like breadth-first search and depth-first search to systematically crawl the web. The architecture of crawlers includes components like the URL frontier, DNS lookup, and parsing pages to extract links. Crawling policies determine which pages to download and when to revisit pages. Distributed crawling improves efficiency by using multiple coordinated crawlers.
A web crawler works by starting with a specified URL and recursively retrieving links within pages to build a crawl frontier of URLs to visit. It checks each URL to see if it exists and parses the page to extract new links, adding them to the frontier. This process continues recursively to a depth of around 5 levels typically to gather most on-site information before stopping to avoid getting trapped on pages with infinite loops of links.
The document discusses web crawling and provides an overview of the process. It defines web crawling as the process of gathering web pages to index them and support search. The objective is to quickly gather useful pages and link structures. The presentation covers the basic operation of crawlers including using a seed set of URLs and frontier of URLs to crawl. It describes common modules in crawler architecture like URL filtering tests. It also discusses topics like politeness, distributed crawling, DNS resolution, and types of crawlers.
Data scraping, Web crawlers are programs that extract information out of world wide web. This presentation aims to cover all relevant topics that one should know before building a crawler.
Smart crawlet A two stage crawler for efficiently harvesting deep web interf...Rana Jayant
The document proposes a two-stage "Smart Crawler" framework to efficiently harvest information from the deep web. In the first stage, the crawler performs site-based searching to avoid visiting many pages. In the second stage, it achieves fast in-site searching by excavating the most relevant links with an adaptive link-ranking. This approach allows the crawler to achieve both wide coverage and high efficiency when searching for information on a specific topic within the deep web.
Web crawlers, also known as spiders, are programs that systematically browse the World Wide Web to download pages for search engines and indexes. Crawlers start with a list of URLs and identify links on pages to add to the list to visit recursively. Effective crawlers require flexibility, high performance, fault tolerance, and maintainability. Crawling strategies include breadth-first, repetitive, targeted, and random walks. Selection, revisit, politeness, and parallelization policies help crawlers efficiently gather relevant information from the dynamic web. Distributed crawling employs multiple computers to index large portions of the internet in parallel.
Colloquim Report on Crawler - 1 Dec 2014Sunny Gupta
This document summarizes a web crawling project completed by Sunny Kumar for his Bachelor's degree. It describes a web crawler called Rotto Link Crawler that was developed to extract broken or dead links within a website. The crawler takes a seed URL, crawls every page of that site to find hyperlinks, and checks if any links are broken. If broken links or pages containing keywords are found, they are stored in a database. The project utilized various Python libraries and was built with a Flask backend and AngularJS frontend.
The document discusses options used in a web crawler code to control its behavior. The -r option enables recursive retrieval, allowing the crawler to follow links. The -spider option makes the crawler behave like a spider to check web pages are accessible without downloading them. The -domains option limits crawling to the specified domain only. The -l 5 option specifies a depth of 5 pages to avoid spider traps, and --tries=5 sets the number of retries if a connection fails.
A Novel Interface to a Web Crawler using VB.NET TechnologyIOSR Journals
This document describes the design of a web crawler interface created using VB.NET technology. It discusses the components and architecture of web crawlers, including the seed URLs, frontier, parser, and performance metrics used to evaluate crawlers. The high-level design of the crawler simulator is presented as an algorithm, and screenshots of the VB.NET user interface for the crawler are shown. The crawler was tested on the website www.cdlu.edu.in using different crawling algorithms like breadth-first and best-first, and the results were stored in an MS Access database.
The document describes a smart crawler system that uses remote method invocation (RMI) for automation. It crawls websites to find active links and relevant data for users. The crawler uses a breadth-first search approach to efficiently find links near the seed URL. It checks links to determine if they are active and distributes documents across multiple machines (M1 and M2) which continue crawling in parallel. Natural language processing techniques like bag-of-words and term frequency-inverse document frequency are used to analyze content and prioritize the most useful results for the user. The use of RMI allows different processes to be run on separate machines, improving the speed and scalability of the crawling system.
This document describes a two-stage crawler called Smart Crawler for concept-based semantic search engines. The first stage involves locating relevant websites for a given topic through site collecting, ranking, and classification. The second stage performs in-site crawling to uncover searchable content from the highest ranked sites using reverse searching and incremental site prioritization algorithms. The system aims to maximize retrieval of deep web content across many websites rather than just a few individual sites.
A web crawler is a program that browses the World Wide Web in an automated manner to download pages and identify links within pages to add to a queue for future downloading. It uses strategies like breadth-first or depth-first searching to systematically visit pages and index them to build a database for search engines. Crawling policies determine which pages to download, when to revisit pages, how to avoid overloading websites, and how to coordinate distributed crawlers.
The document discusses web crawlers, which are computer programs that systematically browse the World Wide Web and download web pages and content. It provides an overview of the history and development of web crawlers, how they work by following links from page to page to index content for search engines, and the policies that govern how they select, revisit, and prioritize pages in a polite and parallelized manner.
DESIGN AND IMPLEMENTATION OF CARPOOL DATA ACQUISITION PROGRAM BASED ON WEB CR...ijmech
Now the public traffics make the life more and more convenient. The amount of vehicles in large or medium
sized cities is also in the rapid growth. In order to take full advantage of social resources and protect the
environment, regional end-to-end public transport services are established by analyzing online travel data.
The usage of computer programs for processing of the web page is necessary for accessing to a large
number of the carpool data. In the paper, web crawlers are designed to capture the travel data from
several large service sites. In order to maximize the access to traffic data, a breadth-first algorithm is used.
The carpool data will be saved in a structured form. Additionally, the paper has provided a convenient
method of data collecting to the program.
Design and Implementation of Carpool Data Acquisition Program Based on Web Cr...ijmech
Now the public traffics make the life more and more convenient. The amount of vehicles in large or medium sized cities is also in the rapid growth. In order to take full advantage of social resources and protect the environment, regional end-to-end public transport services are established by analyzing online travel data. The usage of computer programs for processing of the web page is necessary for accessing to a large number of the carpool data. In the paper, web crawlers are designed to capture the travel data from several large service sites. In order to maximize the access to traffic data, a breadth-first algorithm is used. The carpool data will be saved in a structured form. Additionally, the paper has provided a convenient method of data collecting to the program.
DESIGN AND IMPLEMENTATION OF CARPOOL DATA ACQUISITION PROGRAM BASED ON WEB CR...ijmech
The document describes a web crawler designed to collect carpool data from websites. It begins with an introduction to the need for efficient carpool data collection and issues with existing methods. It then details the design and implementation of the web crawler program. Key aspects summarized are:
1) The web crawler uses a breadth-first search algorithm to crawl links across multiple pages and maximize data collection. It filters URLs to remove duplicates and irrelevant links.
2) It analyzes pages using the BeautifulSoup library to extract relevant text data and links. It stores cleaned data in a structured format.
3) The program architecture involves crawling URLs, cleaning the URL list, and then crawling pages to extract carpool data fields using BeautifulSoup functions
http://lab.lvduit.com:7850/~lvduit/crawl-the-web/
Special thanks to Hatforrent, csail.mit.edu, Amitavroy, ...
Contact us at dafculi.xc@gmail.com or duyet2000@gmail.com
This document proposes a novel architecture for a parallel domain focused crawler that uses mobile crawlers. The architecture aims to reduce bandwidth usage by only downloading pages that have changed since the last crawl. The key components are a frequency change estimator, crawler manager, domain name director, and crawler hands. The frequency change estimator identifies modified pages. Crawler hands fetch pages from remote sites and only download modified pages in compressed form. This is intended to more efficiently update search engine repositories while reducing network resource usage.
This document discusses techniques for improving the speed of web crawling through parallelization using multi-core processors. It provides background on how web crawlers work as part of search engines to index web pages. Traditional single-core crawlers can be improved by developing parallel crawlers that distribute the work of downloading, parsing, and indexing pages across multiple processor cores. This allows different parts of the crawling process to be performed simultaneously, improving overall speed. The document reviews several existing approaches for distributed and parallel web crawling and proposes using a multi-core approach to enhance crawling speed and CPU utilization.
1) The document proposes a system for a semantic web information retrieval service using domain ontology, WCF services, and .NET technologies to improve search in a distributed environment.
2) It involves developing three web services - one to extract search engine result pages based on a user query, one to pre-process the query and results using domain ontology and semantic annotation, and one to determine and compare the link and page content against a repository to calculate relevancy.
3) The most relevant results are then re-ranked based on the total relevancy calculated from the link and page content matching to provide more precise search results compared to traditional keyword-based approaches.
Design and Implementation of SOA Enhanced Semantic Information Retrieval web ...iosrjce
The document describes a proposed system for a semantic web information retrieval service using domain ontology, WCF services, and .NET technologies. It discusses implementing concept relevancy ranking of link and page content as web services. The system architecture includes an admin module to create domain ontology and semantic annotations, a search interface for users, and a testing module. Experimental results show the proposed approach provides more relevant results than traditional search engines for the sample query "company cts chennai taramani".
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document describes a novel parallel domain focused crawler (PDFC) that aims to reduce the load on networks by crawling only web pages relevant to specific domains and skipping pages that have not been modified. The proposed system uses mobile crawlers that stay at remote sites to monitor page changes and only send compressed, modified pages back to the search engine. It is estimated that this approach can reduce network load by up to 40% by avoiding downloading of unchanged pages. The key components of PDFC include modules for URL allocation, comparing page changes over time, and estimating frequency of changes to prioritize recrawling. Experimental results suggest the system is effective at preserving network resources.
Google is the most popular search engine in the world. It was founded in 1998 by Larry Page and Sergey Brin while they were PhD students at Stanford University. Google's main aim is to organize the world's information and make it universally accessible and useful. The company gets its name from the term "googol", which refers to the large quantities of information Google aims to provide. Google uses web crawlers to retrieve web pages from across the internet and stores them in a repository. It then indexes the pages to build search indexes like word occurrence lists. These indexes are used by the searcher to return relevant results in response to user queries. Google's PageRank algorithm assigns importance values to web pages based on the page's
This document provides an overview of search engine optimization (SEO) and analytics techniques. It discusses SEO fundamentals like search engine basics, URL structure best practices, and the importance of metadata tags. It also covers analytics methods and tools. Tips are provided for optimizing content, images, and links. Considerations for technologies like JavaScript, cloaking, and session IDs are explained. The document aims to help optimize websites for search engines and understand user behavior through analytics.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
The document describes a proposed two-stage smart web spider framework for efficiently harvesting data from the deep web. In the first stage, the smart web spider performs site-based searching for central pages using search engines to locate relevant sites while avoiding visiting a large number of pages. In the second stage, the spider performs fast in-site searching by prioritizing relevant links within sites. The goal is to improve the efficiency and coverage of deep web crawling compared to existing approaches. The proposed approach aims to balance wide coverage and efficient crawling to index more of the deep web, which contains a vast amount of valuable information not accessible by typical search engines.
The document describes an improved focused crawler that uses inverted WAH bitmap indexing to more efficiently retrieve topic-specific web pages. It calculates URL relevance scores based on the relevancy of content blocks within a web page rather than the entire page. This allows it to identify relevant links within pages that discuss multiple topics. The approach involves setting up the focused crawler, learning a baseline classifier from user feedback, and monitoring the crawl using an inverted WAH bitmap index to speed up logical operations for relevance scoring.
Intelligent Web Crawling (WI-IAT 2013 Tutorial)Denis Shestakov
<<< Slides can be found at http://www.slideshare.net/denshe/intelligent-crawling-shestakovwiiat13 >>>
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Web crawling, a process of collecting web pages in
an automated manner, is the primary and ubiquitous operation used by a large number of web systems and agents starting from a simple program for website backup to a major web search engine. Due to an astronomical amount of data already published on the Web and ongoing exponential growth of web content, any party that want to take advantage of massive-scale web data faces a high barrier to entry. We start with background on web crawling and the structure of the Web. We then discuss different crawling strategies and describe adaptive web crawling techniques leading to better overall crawl performance. We finally overview some of the challenges in web crawling by presenting such topics as collaborative web crawling, crawling the deep Web and crawling multimedia content. Our goals are to introduce the intelligent systems community to the challenges in web crawling research, present intelligent web crawling approaches, and engage researchers and practitioners for open issues and research problems. Our presentation could be of interest to web intelligence and intelligent agent technology communities as it particularly focuses on the usage of intelligent/adaptive techniques in the web crawling domain.
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IRJET-Deep Web Crawling Efficiently using Dynamic Focused Web CrawlerIRJET Journal
This document proposes a focused semantic web crawler to efficiently access valuable and relevant deep web content in two stages. The first stage fetches relevant websites, while the second performs a deep search within sites using cosine similarity to rank pages. Deep web content, estimated at over 500 times the size of the surface web, is difficult for search engines to index as it is dynamic. The proposed crawler aims to address this using adaptive learning and storing patterns to become more efficient at locating deep web information.
Similar to Research on Key Technology of Web Reptile (20)
Drying of agricultural products using forced convection indirect solar dryerIRJESJOURNAL
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Flight Safety Case Study: Adi Sucipto Airport Jogjakarta - IndonesiaIRJESJOURNAL
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interesting results based on our requirement. Moreover, different post processing techniques will also help to
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Annealing Response of Aluminum Alloy AA6014 Processed By Severe Plastic Defor...IRJESJOURNAL
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Evaluation of Thresholding Based Noncontact Respiration Rate Monitoring using...IRJESJOURNAL
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Correlation of True Boiling Point of Crude OilIRJESJOURNAL
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Combined Geophysical And Geotechnical Techniques For Assessment Of Foundation...IRJESJOURNAL
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Abstract:- research stands out because it is provided by the model of Al-Mobaideen (2009) critics to analyze for the governance of information and communications technology (ICT) at the National University of Chimborazo factors which raises the factors such as: strategies and policies, infrastructure and networks, financing and sustainability, and institutional culture that should be taken into account if desired govern the successful integration of ICT in the school. The study is exploratory, the almost total lack of previous studies on Governance of ICT integration at the University. It is concluded that there is a set of organizations with addresses IT markedly different roles in their duties with regard to its orientation to administrative, academic and research. The University has failed to define the strategic role of ICT in their academic, because there is no objective referred to IT academia in 2013-2016 pedi, but also because there is not a pedi-oriented IT the formation. The limited effectiveness of IT organizations in academic activities is provided by the low rate of use of educationalplatformsb_learning.
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The Analysis and Perspective on Development of Chinese Automotive Heavy-duty ...IRJESJOURNAL
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Research on The Bottom Software of Electronic Control System In Automobile El...IRJESJOURNAL
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Evaluation of Specialized Virtual Health Libraries in Scholar Education Evalu...IRJESJOURNAL
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Abstract. Ensuring of permanent and continuous working process of oil-gas and field equipment alongside with the other factors, depends also on reliability of sealing units. A problem of deterioration modeling of a sealing element of a packer including into an oil field equipment complex is considered in this paper.
Determining Loss of Liquid from Different Types of Mud by Various Addictives ...IRJESJOURNAL
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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
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solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
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dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
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Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
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Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
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Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
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Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
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IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
1. International Refereed Journal of Engineering and Science (IRJES)
ISSN (Online) 2319-183X, (Print) 2319-1821
Volume 6, Issue 1 (January 2017), PP.62-67
www.irjes.com 62 | Page
Research on Key Technology of Web Reptile
Li Yuntao, Yu Su
(College Of Mechanical Engineering, Shanghai University of Engineer Science, China)
Abstract: This paper mainly introduces the web crawler system structure. Through the analysis of the
architecture of web crawler we obtainedfive functional components. They respectively are: the URL scheduler,
DNS resolver, web crawling modules, web page analyzer, and the URL judgment device. To build an efficient
Web crawler the key technology is to design an efficient Web crawler, so as to solve the challenges that the huge
scale of Web brings.
Keywords: DNS, URL, web crawler, web pages
II. NTRODUCTION
Web crawler is application which will retrieve the Web pages on the Web, usually as a search engine
page collection subsystem. In the network initial stage, the network reptiles existed, and there are plenty of
research achievements on web crawlernowadays. The first crawler is Google crawler, the crawler features
mainly aimed at the different crawler components can respectively complete their own processes. In the process
ofthe maintenance of separate URLserver, download URL set is necessary. Web access canalso be achieved by
the crawlers. In the process of the index, the crawlers can also extract hyperlinks and keywords on the Web
pages. In the process of solving processes, the URL should be able to realize the relative path to the absolute
path of transformation.
At present, the commonly used on the market of engines such as Google and baidu, these engines
crawlers technology are confidential. And the crawler realization strategies we are familiar with mainly are:
breadth-first, Repetitive, definitions,deep crawling methods.
The basic work of theprocess of any Web crawler is similar, setting the initial seeds of the URL set S as
input information of Web crawler, repeat the following steps: first of all, from the set S in some strategy to
obtain a URL to crawl, parse out the URL in the host name of the IP address, using the HTTP protocol to
connect the IP address of the Web server and download the URL corresponding page, download page after
extracting all URLlinks of the page. For each of the URLlink, if it is a relative URL, convert it into absolute
URL, and then determine whether the URL link has already downloaded or whether exists in the set S. For the
URL link which has not been downloaded and doesn’t exist in set S, add it to the set S[1]
.
Building a web crawler generally requires the following five features[2]
: a collection of storage to grab
the URL which is called URL scheduler.This URL scheduler can in some strategies select a URL from the URL
set and provide it to the web crawler as a fetching goal;a DNS resolver, which is used toparse out the
correspondingIP address according to the host name in the URL; a web page fetching module, using the HTTP
protocol to download a URL of the page; a web page analyzer, responsible for extracting all theURL links or
some information of interestfrom a web page;a URL judgement device,which can judge whether a given URL
appeared before according to the URLaccess history.
According to the features of web crawler we can get its system structure, as shown in figure 1:
Figure 1 Web crawler architecture
We can see how each feature to cooperate to each other to complete the task of Web information
2. Research on Key Technology of Web Reptile
www.irjes.com 63 | Page
gatheringfrom the architecture of Web crawler. To design an efficient Web crawler it is essential to make close
cooperation between each feature, and the ability to handle huge amounts of data on the Web is also needed.
The following will be based on the web crawler features to study the key technologies which a highly efficient
web crawler needs.
II. URL SCHEDULING
URL scheduler is mainly responsible for savingthe URL set which is to be grabbed. The URL
scheduler provides two main operations: the one operation is to get a URL from the URL set to be fetched and
provide it to the web crawler module to download. The other one is to add the URL link from the web page
analysis module to the URL set to be fetched. The order of the URL to jointhe scheduler is determined by the
external, but URL pop-up sequence is determined by the scheduler itself, that is to say the URL scheduler
determines the order of the web crawler scraping of the page.
The simplest method toimplement the URLscheduleris to use a FIFO queue preserving all the URL sets
to be grabbed. The URL order out of the queue is consistent with the order of their team. However, due tomost
of the links a Web page contains are relative URLslocated on the same host, sothese relative URL links which
are extracted by the web analysis module will enter the URL in order. Then they will leave the queue at the
same time, this will make fetching thread continuous to grab the multiple URL links in the same host, causing a
frequent access to the same Web server by the fetching thread at the same time, which can result in a denial of
service attack.Such behavior is considered to be impolite, so the URL scheduler design requirements must meet
the requirements of courtesy[3]
.
The order of the pop-up in a URL from the scheduler is URL scheduling strategy. It also determines the
order of the web crawler scraping of the page. Therefore, the key of the URL scheduler is how to use a
reasonable strategy to decide the order of the URL pop-up.Research on what strategies to grab web pages can be
an important access to the Web page collection.
The usual scheduling strategy of the URL scheduler is to use a priority queue to store all the URL
collection to be grabbed. The definition of priority is for web important degree evaluation. Define different
priority rules will make different URLscheduling order, so the scraping of the page order is different, then the
network eventually acquire different set of web pages. Judge strengths and weaknesses of a URL scheduling
strategy, namely is to evaluate the quality of web page fetching strategy in web crawler. The standard of the
evaluation is base on the sum of weight of the importance degree of the web page that the URLscheduling
strategy obtains, and theURL scheduling policy which obtains the largest value is considered to be the optimal
scheduling strategy.
III. DNSANALYSIS
DNS is the abbreviation of the domain name system. The system is used for naming hierarchy of the
organization to domain of computer and network services. The host on the Internet is the 32-bit IP address. IP
address is not easy to remember, however, the domain name is to facilitate the people to the IP address in
memory. They conversion between them is called domain name resolution. Domain name resolution needs to be
done by special DNS server. DNS is the server for domain name resolution.
DNS is a worldwide distributed service. When needs the DNS server to resolve a domain name, if the
current DNS server cannot resolve this domain, it willrequest the upper layer DNS server to do. This process
will recursively go on until the requested domain is resolved properly. Therefore, a DNS request may take
several seconds or several dozen of seconds to complete. Through a browser to access the Web, DNS request
will not cause too big effect.For Web crawler search engines, however, because of the need to frequently access
the Web server, if DNS resolution time is too long,it will become the main bottleneck of Web crawler. So, for
efficient Web crawler, we must solve the DNS bottleneck problem.
First of all, analyze the corresponding relationship between domain name and IP. In the Internet,
domain name and IP address of the corresponding exists complex relationships, so a number of different URLs
might point to the same physical pages[4]
. In this case, if the web crawler to URL as grab goals, will lead to the
same page repeated grab, causing the waste of resources. In order to solve this problem, we need to make clear
of the relationship betweenthe domain name and its corresponding IP address.
For the relationship between Domain name and its corresponding IP address, there are four kinds of
circumstances: one-to-one, one-to-many, many-to-one and many-to-many. One-to-one won't cause repeated
grab, and the other three kinds of circumstances are likely to cause repeated grab.
That a domain name corresponds to multiple IP may due to the condition of the DNS rotation, and the
situation is more common in commercial sites.Because the visit of commercial siteper unit time is big, it need
to replicate server content, through DNS rotation to achieve load balancing, meeting the needs of a large
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number of users to access at the same time.
The situation that Multiple domain names corresponds to one IP addressmay be due to the virtual host
IP technology. The virtual host technology is the Internet server technology used to save server hardware costs.
Virtual host technology is mainly used in HTTP service. This technology makes one or all of the contents of a
server logically divide into multiple service units. It seems that there are several servers for visitors, so as to
make full use of server resources. The contents of a plurality of virtual hosts, which are present in the physical
server with the same IP address identification, are mutually exclusive, and each virtual host has its own
independent domain name, but corresponds to the same IP address.
The situation that multiple domain names corresponds to multiple IPis due to thatmultiple domain
names corresponds to a site, and each domain name corresponds to multiple IP address at the same time. This
situation is equivalent to the above two kinds of domain name and IP address of the corresponding relationship.
Web crawler must solve the repeated grab caused by the relationship between the last three kinds of
domain name and its corresponding IP address, which reduces fetching time, and savethe storage space of the
page. In order to solve the problem, we must find the multiple domain names and its corresponding IP address
which point to the same URL. This is a gradual process of accumulation. First accumulates to a certain number
of domain name and IP address, and then grab the home page which the domain names and IP corresponds.
Then grab the first few pages which links to the home page.Compare the results, if they are the same, it should
be classified as a group. After the crawl we can only choose one of them to grab henceforth.
The second is the custom DNS.
Operating system usually provides a DNS interface. For example, UNIX operating system use the
gethostbyname function to achieve this function. However the interface provided by the operating system
usually are synchronized. When fetching threadusing this interface to analyze DNS, the thread can be blocked
until the analysis of DNS is completed. This is the time to DNS spending accounted for 70% of the single grab
work and become the main bottleneck of web crawler. So in order to improve web scraping efficiency, it is
essential to customize a DNS resolver instead of the operating system to provide DNS interface.
Custom DNS resolver can use three ways to improve the efficiency of DNS.
The first is to customize a DNS client, parallel processing of multiple DNS parsing requests in multi
thread mode, and this way can help the load balancingin more than one name server, avoid frequently sending
the request to the same name serverwhich will lead to a similar denial of service attacks.
The second method is to use the cache server to save the NDS analytic results, this approach can
greatly decrease the times of the DNS analysis.Only when encountering for the first time a new domain namedo
the process ofDNS request, but after it all, obtain DNS results from the cache server. In actual use, to design a
proper cache server refresh strategy, at the same time try to put the results of DNS cache in memory, so that we
can consider using a dedicated machine as a caching server.
The third method is to use prefetching strategy. When the analysis moduleextract the URL links in
theweb page, send them to DNS client to do the process of analysis, rather than waiting for the URL links to be
scheduled to grab. This strategy makes it possible that when the URL is scheduled, the DNS parsing results may
be stored in the cache server to avoid the actual DNS parsing and reduce the time of web crawling.
IV. WEB SCRAPING
Web crawler grabbing a Web page takesroughlyabout a few seconds. The time of this process include
getting the target URL from the URL scheduler, DNS parsing, connecting to the Web server to download page,
extracting all the URL links of the web page, and finally saving the page content and linkinformation to local
disk.If the web crawlerdownloads each URL corresponding web pagesone by one, it is unable to grab a large
number of web pages in a short time.This method is obviously not suitable for large-scale grid clustering task.
There are two ways to solve this problem. One is with the method of multi-thread parallel fetching, another is to
use single-threaded manner to grabasynchronously.
Web crawler in the process of scraping of the page, also must follow the Internet rules. If the
administrator of this website make a statement thatsome content on the site is prohibited from the web crawler
accessing, the web crawler is need to abide by the rules, otherwise will be considered to be unfriendly.This rule
is known as the crawler prohibition agreement[5]
.
The multi-threaded parallel crawling mode is that the Web crawler starts multiple grab thread at the
same time, each grab thread connecting to different Web server for Web scraping. This approach has the
advantage that the program structure is simple, each thread fetching independently. Mercator web crawleradopts
this way[6]
.
However, multi-threaded parallel crawling mode also can produce some performance cost[7]
. First, the
number of grab threads is limited by a web crawler memory address space, and the number of grab threads
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directly determines the efficiency of web scraping. Second, each crawling thread needs access to some shared
resources, which will bring the overhead of thread synchronization. Due to the performance bottleneck of the
web crawler mainly lies in the DNS resolution,the network I/O when downloading the web pages and the disk
I/O when storing the web information, so when the grab thread respectivelycomplete a crawl task and a web
information storage, will lead to a large number of cross-random disk I/O. The external performance of the disk
is constantly seeking, so the speed is very slow, and it has a great influence on scraping performance. This kind
of defects can be improved through a single thread asynchronous grab way.
Google's web crawler and the Internet Archive web crawler adopt single thread asynchronous way
tocrawl the web pages. Asynchronous mode is set non-blocking Socket. Because the calls of function connect,
send, recvreturnsimmediately, not waiting for completion of the network operation[8]
, thus can create multiple
continuously Socket to connect to different Web server, then check the status of these Socket by polling, once a
Socket is ready, call the corresponding processing process to complete the preservation of Web information.
This single thread asynchronous grab way reflects more efficient storage management strategy. The
disk I/O which is caused by web information storage works in order, in the Shared pool of tasks do not need
synchronization method, avoiding the cross disk I/O caused by the disk constantly searching.This method also
has the advantage of saving time and improving the performance of the web crawler. Moreover single threaded
asynchronous crawl is easy to expand on multiple machines, so is the preferred way of distributed network.
Crawler ban protocol is a plain-text file which is created by the web site administratorunder the root
directory of the web site.In this file, youdeclare the parts of the site that you do not want to be accessed by the
web crawler. Web crawler need to consciously comply with the agreement, so web crawler and the web site can
get along well, otherwisethe web crawler may be blacklisted by the webmaster as unfriendly.
When a Web crawler visitsa newweb site, it should be the first to check the Web server root directory
of the robots.txt file.For each path name prefixof the file, the specified crawler should not be accessed.For
example, the content of a robots.txt is:
# AltaVista Search
The user-agent: AltaVista Intranet V2.0 W3C Webreq
Disallow: / Out - Of - Date
#exclude some access - controlled areas
User-agent: *
Disallow:/Team
Disallow:/Project
Disallow:/Systems
The robots.txtstatement prohibits the AltaVista web crawler to access the content of the /Out - Of -
Date directory.
V. WEB ANALYTICS
When the crawler crawls to a corresponding resource of URL, the web page analysis module will first
determine whether the resource is the target type of the crawler.For the purpose of this article research is the
focus of the HTML page. This can be determined according to the content-type field in the HTTP response
header of the Web crawler by the web server.If the field value is text/HTML, it means that the resource is a
HTML page, and goes on the next step, otherwise dispose of it.
For each HTML page, the page analysis module will judge whether the content of the page has
occurred, because there may be different URL corresponding to the same web page information.That is the
so-called mirror web page or reproduced[9]
. The same content of web pages you just need to store once in a disk.
This can not only reduce the cost of storage space, but also when these pages as a search engine search results
will not return a lot of duplication. This process is called web pagede-replication.
After dealing with the web page de-replication, web page analysis moduleextracts URL links and some
of the pages of interest information. This process is usually handled by regular expressions. Regular expression
is a very powerful text analysis tools. It is widely used in JavaScript, PHP scripting language.The regular
expression was introduced from Java SDK 1.4. Regular expressions with some special symbols (called
meta-characters) represent certain features of a set of characters, and specify the number of matches. The text
that contains a meta-character no longer represents a specific content, but rather forms a textual pattern that
matches all the strings that match the pattern. By using regular expressions to extract the URLlinks, put these
URL links into the UR scheduler, so that you can guarantee the web crawler run forever. At the same time, the
web page analysis module can use regular expressions to extract the web page information of interest, such as
the page title, the anchor text for the URL links, and so on. This information can be for the use of the search
engine subsystems of indexing and retrieval.
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After the Web page analysis module extracts all the URL links in the web page.Make sure that only
when these URL links pass through the URL referee can they be added to the URL scheduler. Because these
extracted URL links of the web page might have been downloaded. The operation of the URL referee can avoid
the same web page be downloaded many times and waste of fetching time and storage space.
As the crawling process of the web spider goes on, the grabbed web pages are more and more and so is
the number of the URL links.Every time when extracting a web URL from a grabbed web page, how to quickly
perform a referee operation in a huge URL setis a key issue in the design of URL referees. Therefore, the URL
referee must solve the mass data storage and efficient look-up operation in huge amounts of data.
In order to improve the efficiency of URL referee, it is necessary to put as many as grabbed URL links
into the web crawler’s memory. This can be achieved by computing a piece of fingerprint information for each
URL instead of a URL string itself. Web crawler memory, however, no matter how large it is, finally cannot
satisfy the increase of the number of the URL set. Fingerprints that can’t be put into memory can be stored on
disk. Only keepthe most frequently usedsubset of URL links in memory, through a least recently used update
mechanism to maintain a subset of URL links in the memory.
Finding the URL fingerprint information is faster than searching for the URL character itself, because
the URL fingerprint is of fixed length and the information type is of value. You can apply binary search on it
to improve search efficiency. However, the scale of the URL set makes the search efficiency very difficult to
guarantee. Solving the problem of finding information inhuge amounts of informationusually adopts the
following two efficient lookup tables. One is the application of B tree data structure, the other one is the
application of Bloom Filter data structure.
The B-tree[10]
is a balanced search tree for external memory lookups. Each node in the tree is the same
size as a disk block, so that each node can contain a lot of keywords. Each node in the B-tree can also have a lot
of child nodes, which will lead to the B-tree height is very low. Only need a small amount of disk access, when
performing a lookup, so it has a high efficiency. As the process of judging repeating URLneed to combine the
use of internal and external memory,B-tree can efficiently complete the judge of URL repetition.
Bloom Filter is a kind of random data structure with high spatial efficiency. It uses an array to represent
a collection succinctly. But when judging an element whether belongs to a set, it is possible to mistakenly
identify elements that do not belong to this set as belonging to this set. This feature is also known as false
positive. Therefore, Bloom Filter is not suitable for those"zero error"applications. In applications that can
tolerate low error rates, Bloom Filter uses minimal error rate in exchange for a great saving storage space. In the
process of judging URL repetition, in order to improve the search efficiency we can tolerate low error, because
the errorcaused by a small amount of repetition can be got rid of in the process of creating the index page.
Bloom Filter is perfect for the realization of the URL repetition judge device. Internet Archive adopts this way
for the judging of URL repetition.
VI.CONCLUSION
This paper mainly introduces the five functional components in the web crawler technology. Through the
five components can realize preliminary web crawler. But the information on the Web has the characteristics of
heterogeneity and dynamic, due to the limitation of time and storage space, even the largest search engine
cannot crawl back all the pages on the Web. So in the process of actual crawling pages we should have a theme
to grab, only crawl some specificweb content, rather than all of the content are crawled over. So for web crawl
technology, based on this paper, should further study on the theme web crawler.
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