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Breadth-first BASED WEB Crawling Application May Phyu Htun Computer University (Mandalay) [email protected] com. Abstract The large size and the dynamic nature of the Web highlight the need for continuous support and updating of Web-based information retrieval systems. Crawlers facilitate the process by following the hyperlinks in Web pages to automatically download a partial snapshot of the Web. Traversing the web graph in breadth-first search order is a good crawling. This system is intended to study a crawling infrastructure and basic concepts in Web crawling.

Then, web crawler application is implemented by using breadth-first search technique. Breadth-First Crawling checks each link on a page before proceeding to the next page. Thus, it crawls each link on the first page and then crawls each link on the first page’s first’ link, and so on, until each level of link has been exhausted. While Crawling the links of a URL address, the local HTML web pages are saved in a folder as MHTML format: (Single File Web Page). Introduction The Web is a very large collection of pages and search engines serve as the primary discovery mechanism to the content.

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To be able to provide the search functionality, search engines use crawlers that automatically follow links to web pages and extract. Web crawlers are programs that exploit the graph structure of the Web to move from page to page. In their infancy such programs were also called wanderers, robots, spiders, fish, and worms, words that are quite evocative of Web imagery. Crawler can be viewed as a graph search problem. The Web is seen as a large graph with pages at its nodes and hyperlinks as its edges. Web Crawler moves from node to node by means of the hyperlinks that each node contains and that define the edges of the web graph.

Therefore, many algorithms used in graph searching can be frequently observed in web crawling of transformed versions. Traversing the web graph in breadth-first search order is a good crawling strategy, as it tends to discover high-quality pages early on in the crawl. In its simplest form, a crawler starts from a seed page and then uses the external links within it to attend to other pages. The process repeats with the new pages offering more external links to follow, until a sufficient number of pages are identified or some higher level objective is reached.

There is a continual need for crawlers to help applications stay current as new pages are added and old ones are deleted or moved. When a web crawler is given a set of starting URL the web crawler downloads the corresponding documents. If save as a Web page  need to creates a folder that contains an . htm file and all supporting files, such as images, sound files, cascading style sheets, scripts, and more. Save your presentation as a Web page when you want to edit it with FrontPage or another HTML editor, and then post it to an existing Web site.

An HTML document saved in MHTML format, which integrates inline graphics, applets, linked documents, and other supporting items referenced in the documents. The HTML combines all the “support” files in the folder into one big file. Convenient but it may not be supported by all browsers. The rest of this paper is organized as follows. Section (2) describes the correspondingly releated works of the system. Section (3) explains the general architecture of the system. Following is the Section (4) which explains the design of the system. Implementation of the system is discussed in section (5).

The result of this system is evaluated in section (6). Finally, conclusion will follow in section (7). Releated works of the system Gautam Pant,Padmini Srinivasan and Filippo Menczer presented that the crawler maintains a list of unvisited URLs called the frontier. Each crawling loop involves picking the next URL to crawl from the frontier, fetching the page corresponding to the URL through HTTP, parsing the retrieved page to extract the URLs and application specific information, and finally adding the unvisited URLs to the frontier.

The crawling process may be terminated when a certain number of pages have been crawled. If the crawler is ready to crawl another page and the frontier is empty, the situation signals a dead-end for the crawler. The crawler has no new page to fetch and hence it stops [1]. Crawlers exploit the Web’s hyperlinked structure to retrieve new pages by traversing links from previously retrieved ones. As pages are fetched, their outward links may be added to a list of unvisited pages, which is referred to as the crawl frontier. The algorithm to select the next link for traversal is necessarily tied to the goals of the crawler.

Filippo Menczer, Gautampant and Padmini Stinivasan described and evaluated five different crawling algorithms that: Breadth-First, Best-First, PageRank, Shark-Search, and InfoSpiders [2]. Breadth-First crawler is the simplest strategy for crawling. This algorithm was explored as early as 1994 in the WebCrawler [Pinkerton 1994] as well as in more recent research [Cho et al. 1998; Najork and Wiener 2001]. It uses the frontier as a FIFO queue. Breadth-First is used here as a baseline crawler; since it does not use any knowledge about the topic,Its performance to provide a lower bound for any of the more sophisticated algorithms [2].

Overview of the System This system is intended to demonstrate of a typical Web Crawler as well as to learn the basic concept of Web Crawler. This system is able to crawl single URL address (web site address) within a few minutes. While crawling the URL, Crawler will download pages using MHTML format; that format saved pages as web archive single file can be seen like online page. 3. 1. Frontier The frontier is the to-do list of a crawler that contains the URLs of unvisited pages. In graph search terminology, the frontier is an open list of unexpanded (unvisited) nodes as an in-memory data structure for simplicity.

The frontier may be implemented as a FIFO queue in which case have a breadth-first crawler that can be used to blindly crawl the Web. The URL to crawl next comes from the head of the queue and the new URLs are added to the tail of the queue. A linear search to find out if a newly extracted URL is already in the frontier is costly. If the crawler finds the frontier empty when it needs the next URL to crawl, the crawling process comes to a halt [1]. 3. 2. History and Page Repository While history may be stored occasionally to the disk, it is also maintained as an in-memory data structure.

This provides for a fast lookup to check whether a page has been crawled or not. This check is important to avoid not only revisiting pages but also adding the URLs of crawled pages to the frontier [1]. 3. 3. Fetching In order to fetch a Web page, we need an HTTP client which sends an HTTP request for a page and reads the response [1]. 3. 4. Parsing Once a page has been fetched, need to parse its content to extract information that will feed and possibly guide the future path of the crawler [1]. 4. Design of the system Figure 2. Breadth-First Crawler Design Figure 2 illustrates the flow of Breadth-first crawler.

Web Crawler starts with a seed set of source node in the frontier. Crawler then proceeds in the following way: 1. Crawler takes a source node from the frontier. 2. Crawler fetches a link included in frontier as a web document. 3. Crawler extracts all the nodes from the document. 4. If the link contains child links, then these links will be put at the bottom of the frontier. 5. Crawled link will be taken out from the frontier and put into the remove queue. 6. Crawler repeats the procedure from step 1, until there are no unvisited source nodes left in the frontier. 7. While crawling the page, crawler will download the page.

The flow of the system is depicted in Figure 3. Start with single URL address . To see the links, frontier is needed to be built. The Fetch module receives a list of URLs (from URL Frontier) and it fetches the respective pages of these URLs. The parser module is responsible for retrieving pages from Fetch module and parsing them to extract URLs. Crawled link removes from the frontier. Figure 3. System flow Diagram While crawling the page, crawler will save that page as a single file Web page (Single File Web Page (MHTML): everything need to run the presentation is saved with the file.

This means that user doesn’t need a support folder. A presentation saved as a single file Web page takes on the . mht or . mhtml file extension and encapsulation of aggregate HTML Documents (MHTML) file format. 5. Implementation of the system This system is implemented on the fundamental functions of Web Crawler and demonstrated by using Breadth-First theory on developing search engine. This system contains manual crawl function and auto crawl function. In manual Crawl portion, crawl a link of the first page by right click button from Frontier.

In auto Crawl portion, a user must choose the auto Crawl check box and then the system will perform crawling as in the following steps. User can type the URL address into the system. So, the system will be displayed all the links that are connected with that URL page. Moreover, it can also present those links in page level by creating a Frontier (Crawling URLs Queue). The link that has been crawled is removed from the Frontier (Crawling URLs Queue). Then, it will be moved to Remove URLs Queue. If there are child links from the first page, those links will be put at the button of the Frontier (Crawling URLs Queue).

This system will be displayed the web page of the link by crawling it. Moreover, it can also save the page. Web browsers cannot save the whole web page without separating the text and images. This system can save the whole web page into web archive single file (*. mht). To be able to do like this, the system changes the html web page into Mhtml web page (Mhtml is a web page archive format used to bind resources which are typically represented by external links) together with HTML code into a single file. When a user watches the saved web page, user can see the whole page as if it were an online page.

But, the page was saved for offline viewing; therefore, the link in that page would not work. 6. evaluation of the system Many graph algorithms require one to systematically examine the nodes and edges of a graph. There are two standard ways . One way is called a breadth-first search, and the other is called a depth-first search. The breadth-first search will use queue, and depth-first search will use stack. Breadth-first order and depth-first order are the two basic graph-traversing orders that can also be used on the Web, as long as the Web is treated as a graph with pages being nodes and links between pages being edges.

Breadth-first search is chosen if there may be long paths, even infinitely long paths, that neither reach dead ends nor become complete paths. In the Web environment, Breadth-first is an attractive crawling policy because it is computationally simple to implement and compared with depth-first, is more likely to avoid overloading individual servers. Not only does breadth-first search download the hot (high PageRank score) pages first, but also that the average quality of the pages decreased over the duration of the crawler. This System is constructed with the Breadth-First based web crawling.

Breadth-First Search Algorithm is mostly used at Mathematical. This system outlines the Breadth-First search using connected links in web pages. In this system, only URL addresses are used in the search box. This system can avoid overloading a single server by attempting to retrieve all the links that are connected with that URL page within a short period of time. This system can view the links and retrieve web page. By building remove queue user can be seen clearly whether a page has been crawled or not. Futher more this check to avoid revisiting pages and also to avoid adding the URLs of crawled pages to the frontier.

This system describes total page in which include number of crawled pages and number of removed pages. Moreover, the sub links of saved web pages folder can be copied in a flash drive and reopened at anywhere. This system can be used across the Internet. 7. Conclusion Web Crawlers have been made and are widely used; many companies are vigorously trying to improve them. As web crawlers advance, more information is easily accessible from anywhere by typing in what you want. Most search industries seem to be trying to improve on personalization. In a way, they want to make an “intelligent” search.

This system can retrieve the entire link of the page. Links are stored in the frontier as a queue. User can see all links of the page that have been crawled in the frontier. After crawling that page, the link is removed from the frontier. So, duplicate URLs are not possible to exist in the frontier. And then the crawled pages will be downloaded to the storage device. While crawling, user can see the total pages, number of crawled pages and number of removed pages. Moreover, the pages that have been saved as web archive single file can be seen as if it were online page. References 1]1,Gautam Pan, 1,2Padrmini Srinivasan, and 3,Filippo Menezer, “Crawling The Web” 1. Department of Management Sciences, 2. School of Library and Information Science. The University of Iowa, Iowa City IA 52242, USA, 3. School of Informatics Indiana University, Bloomington, IN 47408, USA . [2] Filipomenczer, Gautampant, Padmini Srinvasan, “Topic Web Crawers”: ACM Transactions on Internet Technology, Vol. V, No. N, pp 10-11, February 2003. [3]Pinkerton, B. 1994. “Finding what people want: Experiences with the WebCrawler”. In Proc. 1stInternational World Wide Web Conference (Geneva). 4]Najork, M. and Wiener, J. L. 2001. “Breadth-First search crawling yields high-quality pages”. In Proc. 10th International World Wide Web Conference. ———————– Child URL Unvisited Page Crawled URL Remove queue FIFO(URLs) queue Web page Crawler Display MHTML web pages yes No URL Visited Page Remove From Frontier to Remove URLs Queue Check unvisted or visited Start End Check Next URL in URL Queue Parsing Web Pages Build Frontier as URLs Queue (Breadth-First Crawling) Fetching Link Collection Download pages Convert HTML to MHTML Stop URLs Queue

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