IMPLEMENTETION OF WEB CRAWLER

Summary – HTML format through the World Wide Web is a document that generated billions of collection of

links, then such a large-scale data has become an obstacle to information retrieval, the user in order to find the

information you want may have to flip a few pages. Web crawler is the core of search engines, web crawlers

continuous crawl the Internet to find any new pages added to the network and has been removed from the web page.

The continued growth of the web pages and dynamic, traverse the network and handles all the site has become a

challenge. A concentration is a crawling reptile specific topics, access and collect relevant pages agent. Will be

mentioned in this paper I have made some of the design of web crawler to retrieve the work of copyright

infringement. We will use a seed URL as input and search for a keyword search under the keyword, and found that

the keyword for the page. Focus is to find keywords that contain the user to retrieve web crawling approach, we use

breadth-first search manner. Now, when we search the web, you use the text on pattern recognition. We select a file

as input and retrieval by pattern recognition algorithms. Here, the match only to check the website for the text and

the number of text available. I use the matching search algorithm Knutt-Morri-Pratt, Boyer-Moore, finite automata.

Keywords: search engines, focused crawlers, pattern recognition, copyright infringement

1 Introduction

World Wide Web provides a huge source of information, almost all types. However, these are usually divisions in

many Web servers and hosts, and uses a lot of different formats. We all hope that we should use less time to retrieve

the desired results. In this article, we introduced the work focused Web crawler, to find copyright infringement

through the merger process 

For any Web crawler must take into account two things, first of all, reptiles need to have ability to plan, such as the

pull can decide which page the next time; Secondly, it needs to have a highly optimized and robust system

architecture to achieve the second to pull a large number of pages, and prevent system crashes, and effective

management to take a page Lala, appropriate resources and Web server. Some recent scholarly attention to the

first question many topics, including the important pages to determine which should first crawl. In contrast, the

second question concerns less and less. Obviously, all the big search engines have highly optimized retrieval system,

although the details of the system documentation, where their owners are usually in general is confidential.

Currently known systems documented in detail in the Heydon and Najork DECCompaq

development Mercator system, which used to be Alta Vista. Pull a few short pages the crawler is very simple to run

slowly. However, to construct a high-performance system in the system design, I / O and network performance,

robustness and manageability aspect is a big challenge.

Each search engine is divided into many different modules that the module is the search engine spiders the most

important because it helps to provide the best possible search engine results. Is the search engine spiders 'browse'

page is a small program, similar to the user by clicking on different pages to access the program from the Internet to

retrieve some of the seeds used to start the URL. Reptile extract retrieved Web page URL, and the information

available to the crawler control module. After the visit of the module to determine which pages to do, then these

pages provide links to the reptiles. Reptiles will also pull the retrieved pages and add to the page library. This

ongoing web crawling, until exhaustion of local resources, such as storage devices.

The rest of this article is structured as follows. The next section surveys related work reptiles, section 3, we use the

focused crawler described the principles, section 4 describes pattern recognition algorithm, section 5 describes the

realization of reptiles, and Section 6 summarizes the work of the future that needs to be done.

2 Related Work

Crawler, also known as robots, spiders, worms, web chaser, and Rangers, almost as old as the network itself. The

first developer of Web crawler is Matthew Gray's Wandered, written in the spring of 1993, about exactly the NCSA

Mosaic browser released the first version of the time.

In this paper, we focus on the focused crawler, it will be given in accordance with our keyword index relevant pages.

Reptiles in the specified page to find specific keywords we entered, first find the URL in the seed, and then turned to

the Web site's page and the page contains other links that we specified in the keyword search, has been crawling the

page until such We reached the limits we set, but it may not find the number of pages set before us, that there is no

other link to the page containing the specific keyword. Extraction of the page when the reptile should ensure that

only the pull-specific links, to ensure that over and over again does not always access the same page. After we pull

the link, will be a txt text as input and run KMP (Knutt-Morris-Pratt), BMM (Boyer-Moore), and

finite automata these three patternrecognition algorithms.

3 the principle of focused crawler

Figure 1 Principle of focused crawler

The course of the operation of reptiles as shown above, DNS process is responsible for removing the seed URL in a

URL and attempts to connect to the URL host IP protocol.

First, DNS process, view the DNS database, check whether the host is resolved, if you have parsed, the direct access

to the IP, if not resolve, DNS through the DNS server process to get the host IP; after the read process is responsible

for parsing the IP address, and tried to open an HTTP socket connection to request the page.

Download page, the crawler checks to prevent duplication of page content pull, and then extracted and standardized

pull pages contain URl, to verify whether the network robots crawling these pages, check whether the reptiles before

the pull of these URL

Obviously we can not let the server has been in a busy state to check the information, so we must set some time

stamp, time stamp information, etc. Check not exhausted even checked the reptile will continue to crawl the page, if

you run out of time stamp is also can not find the link will be prompted to find a string, if found the reptile will go to

pull the page and record in some table which is stored in the file inside, here, we only pull html page.

4 Pattern Recognition

Here is recognition of the object is only for text, pattern recognition used for parsing.

When we go to compare the expression of pattern recognition and the general pattern matching match will find more

powerful, but the identification process will slow down

Is a string matches a pattern, all the keywords can be written in uppercase or lowercase, a pattern matching

expression element by a binary operator and the composition of spaces and tabs can be used to separate words Text

in the discovery of knowledge plays an important role in the process can be used has never been structured or semi-

structured data extracted hidden information, in part, after the foundation work, since most of the HTML code

embedded in web pages, these pages information is semi-structured, there are many pages are being linked, there are

a lot of redundant pages, web page text to help us obtain and synthesize useful data, information and knowledge

This paper, the pattern recognition process such applications in the reptiles, when we start the crawler, it will

provide me with relevant links and keywords, and then reads these links pages, and only read the contents of these

pages. This content is only a page of text information can be obtained, does not include pictures, labels, and buttons.

Pull the content will be stored in some file, but does not contain any HTML tags

We extract the text of the algorithm:

l KNUTT-MORRIS-PRATT (KMP)

l finite automata

l BOYER MOORE (BMM)

4.1 KNUTT-MORRIS-PRATT algorithm pseudo code

Knutt-Morris-Pratt algorithm works very much like finite automata algorithms, matching, and text strings are

compared from left to right, if the match is successful, the algorithm looks for matches starting position to the

current location of the largest subscript matches to determine location can match up to move far to the right and to

avoid loss of possible matches.

We need to move the location of the next data is stored in an auxiliary of the "next" table inside the table is matched

by their own pre-obtained, which contains the string match fails nowadays in a position to match the information ,

the "next" table is a senior assistant.

This is a calculation of our "next" table in short description: We use a cursor to find the largest prefix string P, the

subscript P [1 ... j], through the string that they can be calculated for each position moving distance, when the

characters match, P and the next pointer will be increased, when a match occurs more than once, we will next [j] is

set to j-1, if the match had been in the starting position of the next match [j] is set to 0, i increase, by matching their

own to check the location of the next move.

Input: m-character string that contains P (matching documents) and the target page file

Output: the number of matches and find the matching algorithm in the time-consuming process

The main method to achieve approximately like this:

while (I <n) {

if (pattern.charAt (j) == text.charAt (i)) {

if (j == m-1)

return I-m +1; / / match

j + +;

}

else if (j> 0) {

j = fail [j-1];

}

else {

i + +;

}

return -1; / / no match

}

As long as we do not reach the end of the text, and text string match comparison will be continued, when the match

string and text match, i and j are from Canada, the time when all the matches, the algorithm will return a valid offset

position, there is no match for the situation, there is one difference: if the match occurred in the initial position,

matching the string to the right one to match, if not the initial position, the program calls the helper function to

determine the next time you want to move position, if not the end of the text string match is found, the program will

return -1.

4.2 Finite Automata Algorithm pseudo code

This method uses finite automata to scan for text pattern matching, a finite automaton is a quintuple (S, s0, A, Σ, δ),

where:

– S is a finite set of state

– S0 is the initial state

– A ⊆ S is a set of accepting states

– Σ * is a finite input alphabet

– Δ is the one from S � Σ * to the S function, called the transfer function of automatic machines.

In order to use finite automata to solve the string matching problem, P must be a finite automaton model, the

chances of establishing the state of m +1 different states, and the final state is the only one to receive state, the steps

we construct the state transition automata "framework," they will be executed under the matching, then the

mismatch we add directed edges, in order to calculate the transfer function, we use this formula, which defines "the

wrong start," the longest suffix This is the pattern prefix P, (I, a) = max {k <= I ⏐ P [1 ... k] is suffix of P [1 ... I] a},

(I, a) = 0 that the suffix was not found.

4.3 BOYER-MOORE ALGORITHM algorithm pseudo code

In BOYER-MOORE] algorithm, the model is scanned from right to left text, the algorithm using two different

pretreatment strategies to determine the movement as small as possible, each match fails, the two algorithms to

calculate then select the maximum possible move, which will be used for each particular case the most effective

strategy.

The first strategy is to "bad character" heuristic. This strategy focused on "bad character" above, which will lead do

not match. If it does not contain all the P, this model can move through it, if it is somewhere in the model, then

search for the right of the "bad character" and the matching text.

"Bad character" heuristic of auxiliary functions:

public static int [] buildLastFunction (String pattern) {

int [] last = new int [128]; / / assume ASCII character set

for (int i = 0; i <128; i + +) {

last [i] = -1; / / initialize array

}

for (int i = 0; i <pattrn.length; i + +) {

last [pattern.charAt] = i; / / implicit cast to integer

ASCII code

}

return last;

}

Each alphabetic character, we identified the right model in place and the results of its most write to an array. Then,

each match fails, we look for the "last" as the value of the location of bad character, to find out how far to the right

mode.

Only "bad character" A simple heuristic algorithm:

int [] last = buildLastFunction (pattern);

int n = text.length ();

int m = pattern.length ();

int i = m-1;

if (i> n-1)

return -; / / no match if pattern is longer than text

int j = m-1;

do {

if (pattern.charAt (j) == text.charAt (i))

if (j == 0) {

return; / / match

} Else {/ / left-to-right scan

i -;

j -;

}

}

else

i = i + m-Math.min (j, 1 + last [text.charAt (i)]);

j = m-1;

}

while (i <= n-1)

return -; / / no match

}

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First check if the length of pattern length than the text, set the mode and the text pointer to the starting position, the

model is the most right to compare the character and then, when j is equal to m-1, indicating that all the matches,

and then we return a valid partial shift position, if unequal, j and i decline to continue the comparison.

Case does not match the pattern string and text, the auxiliary function is called we identified the right place in the

pattern of the most bad character and the corresponding changes to J and i, if we have checked all the valid mobile

and have found that do not match, we know model does not appear in the text will return – 1. The second strategy is

"good suffix" heuristics. We try to find the "wrong starting point" of the maximum suffix is prefix pattern.

5 to achieve

This paper, we present the design and implementation of web crawler, front, kmp, finite automata and Boyer Moore

algorithms have been shown here, we run the crawler will give you a seed URL, keywords and as inputs text file,

and when we click the Search button will search the Internet pages match the specified keywords, and if we click the

Stop button, the program will terminate the search.

Figure 2, the appearance of the program

As we see, he will return a matching key words extracted from a list of pages, when we click the Find button, he

discusses the window will pop up

Figure 3 output

Here we will see a pull to generate a list of web pages, the text input box in the model we will set our text file as

input, and finally we will click the Run button and 3, the algorithm starts, and then will be output , the output of the

content matching algorithm matches the number and time spent, where time is in nanoseconds.

Later we see it as a Web page to create a txt file, and all the computational results are stored in txt file.

Model file

KMP output

The output of finite automata

The output of BMM

6 Summary and future work needs to be done

Crawler is a program to download and store Web pages, Web search engines generally provide data to the rapid

growth of the Internet link to find the most appropriate to bring greater challenges. Focused crawler extracted only

from the Internet and web pages relevant to the subject of interest. Up to now, Allan Heydon and Marc Najork in the

"Mercator: a scalable web crawler" to describe them, Mercators main support scalable custom ant, Mercator also

introduced the use of special components. In this paper, we use the definition of Mercator in the reptile area features

some of the components, the design of the Web crawler program to process the input text file comparison with the

network connectivity features, the reptile with a pattern recognition algorithm and obtain input text appears in the

number of connections.

The reptiles were the three algorithms used pattern recognition to the text and outputs the results of each algorithm,

through this information we can see the impact of pattern matching algorithm, the reptiles only use a text search

method, where pattern recognition, web crawlers can also be used other text processing technology, so you can

better develop a more intelligent use of web crawlers to find copyright infringement.

References:

[1] Allen Heydon and Mark Najork, "Mercator: A Scalable, Extensible Web Crawler", Compaq Systems Research

Center, 130 Lytton Ave, Palo Alto, CA 94301, 2001.  

[2] Francis Crimmins, "Web Crawler Review", Journal of Information Science, Sep.2001.

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crawlers", in Proc. Of the Seventh International World WideWeb Conference (WWW7), Brisbane, Australia, April

1998.Printed in Computer Network and ISDN Systems v.30, pp.119-130, 1998. Brisbane, Australia, April 1998,

[4] Berners-Lee and Daniel Connolly, "Hypertext Markup Language.Internetworking draft", Published on the WW

W athttp: / / www.w3.org/hypertext, l, 13 Jul 1993.  

[5] Sergey Brin and Lawrence Page, "The anatomy of largescale hyper textual web search

engine", Proc. Of 7 th International World Wide Web Conference, volume 30, Computer Networks and ISDN

Systems, pg. 107-117, April 1998.  

[6] Alexandros Ntoulas, Junghoo Cho, Christopher Olston "What's New on the Web? The Evolution of the Web from

a Search Engine Perspective." In Proc. Of the World-wide-Web Conference (WWW), May 2004.

[7] Arvind Arasu, Junghoo Cho, Hector Garcia-Molina, Andreas Paepcke. Sriram Raghavan. Computer Science

Department, Stanford University. "Searching The Web",.

[8] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, "INTODUCTION TO ALGORITHM", seventh

edition, published by Prentice-Hall of India Private Limited.

[9] Ute Abe, Prof. Brandenburg. "String Matching", Sommersemester 2001, pg 1 -9.

[10] Shi Zhou, Ingemar Cox, Vaclav Petricek, "Characterising Web Site Link Structure", Dept. Of Computer

Science, University College London, UK, IEEE 2007.

[11] M. Najork, J. Wiener, "Breadth-first crawling yields high quality pages", Compaq Systems Research Center,

130 Lytton Avenue, Palo Alto, CA 94301, USA, WWW 2001, pg. 114 – 118.

PS: The first translation of papers, several local translation is not fluent, inappropriate, hope corrected; another

translation of papers before the choice is not a good translation done very general sense of the article.