How do you scrape AJAX pages?

Overview:

All screen scraping first requires manual review of the page you want to extract

resources from. When dealing with AJAX you usually just need to analyze a bit more

than just simply the HTML.

When dealing with AJAX this just means that the value you want is not in the initial

HTML document that you requested, but that javascript will be exectued which asks the

server for the extra information you want.

You can therefore usually simply analyze the javascript and see which request the

javascript makes and just call this URL instead from the start.

Example:

Take this as an example, assume the page you want to scrape from has the following

script:

<script type="text/javascript">
function ajaxFunction()
{
var xmlHttp;
try
  {
  // Firefox, Opera 8.0+, Safari
  xmlHttp=new XMLHttpRequest();
  }
catch (e)
  {
  // Internet Explorer
  try
    {
    xmlHttp=new ActiveXObject("Msxml2.XMLHTTP");
    }
  catch (e)
    {
    try
      {
      xmlHttp=new ActiveXObject("Microsoft.XMLHTTP");
      }
    catch (e)
      {
      alert("Your browser does not support AJAX!");
      return false;
      }
    }
  }
  xmlHttp.onreadystatechange=function()
    {
    if(xmlHttp.readyState==4)
      {
      document.myForm.time.value=xmlHttp.responseText;
      }
    }
  xmlHttp.open("GET","time.asp",true);
  xmlHttp.send(null);
  }
</script>

Then all you need to do is instead do an HTTP request to time.asp of the same server

instead. Example from w3schools.


Sporce: http://stackoverflow.com/questions/260540/how-do-you-scrape-ajax-pages

Using Perl to scrape a website

I am interested in writing a perl script that goes to the following link and extracts

the number 1975: https://familysearch.org/search/collection/results#count=20&query=

%2Bevent_place_level_1%3ACalifornia%20%2Bevent_place_level_2%3A%22San%20Diego

%22%20%2Bbirth_year%3A1923-1923~%20%2Bgender%3AM%20%2Brace

%3AWhite&collection_id=2000219

That website is the amount of white men born in the year 1923 who live in San Diego

County, California in 1940. I am trying to do this in a loop structure to generalize

over multiple counties and birth years.

In the file, locations.txt, I put the list of counties, such as San Diego County.

The current code runs, but instead of the # 1975, it displays unknown. The number 1975

should be in $val\n.

I would very much appreciate any help!

#!/usr/bin/perl

use strict;

use LWP::Simple;

open(L, "locations26.txt");

my $url = 'https://familysearch.org/search/collection/results#count=20&query=

%2Bevent_place_level_1%3A%22California%22%20%2Bevent_place_level_2%3A%22%LOCATION%

%22%20%2Bbirth_year%3A%YEAR%-%YEAR%~%20%2Bgender%3AM%20%2Brace

%3AWhite&collection_id=2000219';

open(O, ">out26.txt");
 my $oldh = select(O);
 $| = 1;
 select($oldh);
 while (my $location = <L>) {
     chomp($location);
     $location =~ s/ /+/g;
      foreach my $year (1923..1923) {
                 my $u = $url;
                 $u =~ s/%LOCATION%/$location/;
                 $u =~ s/%YEAR%/$year/;
                 #print "$u\n";
                 my $content = get($u);
                 my $val = 'unknown';
                 if ($content =~ / of .strong.([0-9,]+)..strong. /) {
                         $val = $1;
                 }
                 $val =~ s/,//g;
                 $location =~ s/\+/ /g;
                 print "'$location',$year,$val\n";
                 print O "'$location',$year,$val\n";
         }
     }

Update: API is not a viable solution. I have been in contact with the site developer.

The API does not apply to that part of the webpage. Hence, any solution pertaining to

JSON will not be applicbale.



Source: http://stackoverflow.com/questions/14654288/using-perl-to-scrape-a-website

Data Scraping using php

Here is my code

    $ip=$_SERVER['REMOTE_ADDR'];

    $url=file_get_contents("http://whatismyipaddress.com/ip/$ip");

    preg_match_all('/<th>(.*?)<\/th><td>(.*?)<\/td>/s',$url,$output,PREG_SET_ORDER);

    $isp=$output[1][2];

    $city=$output[9][2];

    $state=$output[8][2];

    $zipcode=$output[12][2];

    $country=$output[7][2];

    ?>
    <body>
    <table align="center">
    <tr><td>ISP :</td><td><?php echo $isp;?></td></tr>
    <tr><td>City :</td><td><?php echo $city;?></td></tr>
    <tr><td>State :</td><td><?php echo $state;?></td></tr>
    <tr><td>Zipcode :</td><td><?php echo $zipcode;?></td></tr>
    <tr><td>Country :</td><td><?php echo $country;?></td></tr>
    </table>
    </body>

How do I find out the ISP provider of a person viewing a PHP page?

Is it possible to use PHP to track or reveal it?

Error: http://i.imgur.com/LGWI8.png

Curl Scrapping

<?php
$curl_handle=curl_init();
curl_setopt( $curl_handle, CURLOPT_FOLLOWLOCATION, true );
$url='http://www.whatismyipaddress.com/ip/132.123.23.23';
curl_setopt($curl_handle, CURLOPT_URL,$url);
curl_setopt($curl_handle, CURLOPT_HTTPHEADER, Array("User-Agent: Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.8.1.15) Gecko/20080623 Firefox/2.0.0.15") );
curl_setopt($curl_handle, CURLOPT_CONNECTTIMEOUT, 2);
curl_setopt($curl_handle, CURLOPT_RETURNTRANSFER, 1);
curl_setopt($curl_handle, CURLOPT_USERAGENT, 'Your application name');
$query = curl_exec($curl_handle);

curl_close($curl_handle);
preg_match_all('/<th>(.*?)<\/th><td>(.*?)<\/td>/s',$url,$output,PREG_SET_ORDER);
echo $query;
$isp=$output[1][2];

$city=$output[9][2];

$state=$output[8][2];

$zipcode=$output[12][2];

$country=$output[7][2];
?>
<body>
<table align="center">
<tr><td>ISP :</td><td><?php echo $isp;?></td></tr>
<tr><td>City :</td><td><?php echo $city;?></td></tr>
<tr><td>State :</td><td><?php echo $state;?></td></tr>
<tr><td>Zipcode :</td><td><?php echo $zipcode;?></td></tr>
<tr><td>Country :</td><td><?php echo $country;?></td></tr>
</table>
</body>

Error: http://i.imgur.com/FJIq6.png

What's is wrong with my code here? Any alternative code , that i can use here.

I am not able to scrape that data as described here. http://i.imgur.com/FJIq6.png

P.S. Please post full code. It would be easier for me to understand.



Source: http://stackoverflow.com/questions/10461088/data-scraping-using-php

PDF scraping using R

I have been using the XML package successfully for extracting HTML tables but want to extend to PDF's. From previous questions it does not appear that there is a simple R solution but wondered if there had been any recent developments

Failing that, is there some way in Python (in which I am a complete Novice) to obtain and manipulate pdfs so that I could finish the job off with the R XML package

Extracting text from PDFs is hard, and nearly always requires lots of care.

I'd start with the command line tools such as pdftotext and see what they spit out. The problem is that PDFs can store the text in any order, can use awkward font encodings, and can do things like use ligature characters (the joined up 'ff' and 'ij' that you see in proper typesetting) to throw you.

pdftotext is installable on any Linux system



Source: http://stackoverflow.com/questions/7918718/pdf-scraping-using-r

Php Scraping data from a website

I am very new to programming and need a little help with getting data from a website and passing it into my PHP script.

The website is http://www.birthdatabase.com/.

I would like to plug in a name (First and Last) and retrieve the result. I know you can query the site by passing the name in the URL, but I am having problems scraping the results.

http://www.birthdatabase.com/cgi-bin/query.pl?textfield=FIRST&textfield2=LAST&age=&affid=

I am using the file_get_contents($URL) function to get the page but need help after that. Specifically, I would like to scrape only the results from a certain state if there are multiple results for that name.



You need the awesome simple_html_dom class.

With this class you can query the webpage's DOM in a similar way to jQuery.

First include the class in your page, then get the page content with this snippet:

$html = file_get_html('http://www.birthdatabase.com/cgi-bin/query.pl?textfield=' . $first . '&textfield2=' . $last . '&age=&affid=');

Then you can use CSS selections to scrape your data (something like this):

$n = 0;
foreach($html->find('table tbody tr td div font b table tbody') as $element) {
    @$row[$n]['tr']  = $element->find('tr')->text;
    $n++;
}

// output your data
print_r($row);



Source: http://stackoverflow.com/questions/15601584/php-scraping-data-from-a-website

Obtaining reddit data

I am interested in obtaining data from different reddit subreddits. Does anyone know if there is a reddit/other api similar like twitter does to crawl all the pages?


Yes, reddit has an API that can be used for a variety of purposes such as data collection, automatic commenting bots, or even to assist in subreddit moderation.

There are a few places to discover information on reddit's API:

    github reddit wiki -- provides the overview and rules for using reddit's API (follow the rules)
    automatically generated API docs -- provides information on the requests needed to access most of the API endpoints
    /r/redditdev -- the reddit community dedicated to answering questions both about reddit's source code and about reddit's API

If there is a particular programming language you are already familiar with, you should check out the existing set of API wrappers for various languages. Despite my bias (I am the package maintainer) I am quite certain PRAW, for python, has support for the largest number of reddit API features.



Source: http://stackoverflow.com/questions/14322834/obtaining-reddit-data

Scraping data in dynamic sites

I'm trying to scrape data from our local government. What I want is address from kids adoption offices. Here, in Brazil, all adoptions go through the government. So I have the URL of one office, there are 2 or 3 thousands more. But if I can manage to get one, the others will be easy. I made many attempts, bellow I show three.

The problem could be related to a Javascript (Ajax maybe) that refresh the page.

Note: I am not a PHP developer.

First attempt

echo '<html><head></head><body>';
echo '<h1>Scraper PHP GET 1</h1>';

echo ini_get("allow_url_fopen");
echo ini_get("allow_url_fopen");

// I used this url for test
//$url = 'http://www.portaldaadocao.com.br';

//This is the URL that I really want
$url = 'http://www.cnj.jus.br/cna/Controle/ConsultaPublicaBuscaControle.php?transacao=CONSULTA&vara=2673';

$html = file_get_contents($url);
var_dump($html);

echo '</body></html>';

// Output
// 11
// Warning:
file_get_contents(http://www.cnj.jus.br/cna/Controle/ConsultaPublicaBuscaControle.php?
transacao=CONSULTA&vara=2673) [function.file-get-contents]: failed to open stream: HTTP
request failed! HTTP/1.1 404 Not Found in /home/rsl/www/sc01_get.php on line 14
// bool(false)

Second attempt

echo '<html><head></head><body>';
echo '<h1>Scraper PHP CURL 3</h1>';

// I used this url for test
//$url = 'http://www.portaldaadocao.com.br';

//This is the URL that I really want
$url = 'http://www.cnj.jus.br/cna/Controle/ConsultaPublicaBuscaControle.php?transacao=CONSULTA&vara=2673';

$curl = curl_init($url);
@curl_setopt($curl, CURLOPT_POSTFIELDS, "foo");
@curl_setopt($curl, CURLOPT_FOLLOWLOCATION, true);
@curl_setopt($curl, CURLOPT_CUSTOMREQUEST, "POST");;

$html=@curl_exec($curl);

if (!$html) {
    echo "<br />cURL error number:" .curl_errno($curl);
    echo "<br />cURL error:" . curl_error($curl);
    exit;
}
else{
   echo '<br>begin HTML[';
    echo  $html;
   echo '<br>]end html ';
}
echo '</body></html>';

// Output
// 1

third attempt

function curl($url){
    $ch = curl_init();
    curl_setopt($ch, CURLOPT_URL, $url);
    curl_setopt($ch, CURLOPT_RETURNTRANSFER,1);
    curl_setopt($ch, CURLOPT_USERAGENT, 'Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.6 (KHTML, like Gecko) Chrome/16.0.897.0 Safari/535.6');
    curl_setopt($ch, CURLOPT_HEADER, true);
    curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
    curl_setopt($ch, CURLOPT_COOKIEFILE, "cookie.txt");
    curl_setopt($ch, CURLOPT_COOKIEJAR, "cookie.txt");
    curl_setopt($ch, CURLOPT_CONNECTTIMEOUT, 30);
    curl_setopt($ch, CURLOPT_REFERER, "http://www.windowsphone.com");

    $data = curl_exec($ch);
    curl_close($ch);
    return $data;
}

echo '<html><head></head><body>';
echo '<h1>Scraper PHP CURL 5</h1>';

// I used this url for test
//$url = 'http://www.portaldaadocao.com.br';

//This is the URL that I really want
$url = 'http://www.cnj.jus.br/cna/Controle/ConsultaPublicaBuscaControle.php?transacao=CONSULTA&vara=2673';

$curl = curl_init($url);
@curl_setopt($curl, CURLOPT_POSTFIELDS, "foo");
@curl_setopt($curl, CURLOPT_FOLLOWLOCATION, true);
@curl_setopt($curl, CURLOPT_CUSTOMREQUEST, "POST");;

$html=@curl($curl);


if (!$html) {
    echo "<br />cURL error number:" .curl_errno($curl);
    echo "<br />cURL error:" . curl_error($curl);
    exit;
}
else{
    echo '<br>begin HTML[';
    echo  $html;
    echo '<br>]end html ';
}
echo '</body></html>';

// Output
// cURL error number:0
// cURL error:

If the pages are really ajax based meaning the information that you need to scrape is loaded or shown through javascript execution, you will need another approach. You would need to automate with a real browser. You can go the Selenium route which can be written in a number of languages or use CasperJS with Javascript as the programming language.



Source: http://stackoverflow.com/questions/24611046/scraping-data-in-dynamic-sites

What is the right way of storing screen-scraping data?

i'm working on a web site. it is scraping product details(names, features, prices etc.) from various web sites, processing and displaying them. i'am considering to run update script on each day and keep data fresh.

    scrape data
    process them
    store on database
    read(from db) and display them

i'am already storing all the data in a sql schema but i'm not sure. After each update, all the old records are vanishing. if the scraped new data comes corrupted somehow, there is nothing to show.

so, is there any common way to archive the old data? which one is more convenient: seperate sql schemas or xml files? or something else?

Source: http://stackoverflow.com/questions/13686474/what-is-the-right-way-of-storing-screen-scraping-data

Scraping dynamic data

I am scraping profiles on ask.fm for a research question. The problem is that only the top most recent questions are viewable and I have to click "view more" to see the next 15.

The source code for clicking view more looks like this:

<input class="submit-button-more submit-button-more-active" name="commit" onclick="return Forms.More.allowSubmit(this)" type="submit" value="View more" />

What is an easy way of calling this 4 times before scraping it. I want the most recent 60 posts on the site. Python is preferable.

You could probably use selenium to browse to the website and click on the button/link a few times. You can get that here:

    https://pypi.python.org/pypi/selenium

Or you might be able to do it with mechanize:

    http://wwwsearch.sourceforge.net/mechanize/

I have also heard good things about twill, but never used it myself:

    http://twill.idyll.org/



Source: http://stackoverflow.com/questions/19437782/scraping-dynamic-data

Web Scraping data from different sites

I am looking for a few ideas on how can I solve a design problem I'm going to be faced with building a web scraper to scrape multiple sites. Writing the scraper(s) is not the problem, matching the data from different sites (which may have small differences) is.

For the sake of being generic assume that I am scraping something like this from two or more different sites:

    public class Data {
        public int id;
        public String firstname;
        public String surname;
        ....
    }

If i scrape this from two different sites, I will encounter the situation where I could have the following:

Site A: id=100, firstname=William, surname=Doe

Site B: id=1974, firstname=Bill, surname=Doe

Essentially, I would like to consider these two sets of data the same (they are the same person but with their name slightly different on each site). I am looking for possible design solutions that can handle this.

The only idea I've come up with is scraping the data from a third location and using it as a reference list. Then when I scrape site A or B I can, over time, build up a list of failures and store them in a list for each scraper so that it can know (if i find id=100 then i know that the firstname will be William etc). I can't help but feel this is a rubbish idea!

If you need any more info, or if you think my description is a bit naff, let me know!

Thanks,

DMcB


Source: http://stackoverflow.com/questions/23970057/web-scraping-data-from-different-sites

Scrape Data Point Using Python

I am looking to scrape a data point using Python off of the url http://www.cavirtex.com/orderbook .

The data point I am looking to scrape is the lowest bid offer, which at the current moment looks like this:

<tr>
 <td><b>Jan. 19, 2014, 2:37 a.m.</b></td>
 <td><b>0.0775/0.1146</b></td>
 <td><b>860.00000</b></td>
 <td><b>66.65 CAD</b></td>
</tr>

The relevant point being the 860.00 . I am looking to build this into a script which can send me an email to alert me of certain price differentials compared to other exchanges.

I'm quite noobie so if in your explanations you could offer your thought process on why you've done certain things it would be very much appreciated.

Thank you in advance!

Edit: This is what I have so far which will return me the name of the title correctly, I'm having trouble grabbing the table data though.

import urllib2, sys
from bs4 import BeautifulSoup

site= "http://cavirtex.com/orderbook"
hdr = {'User-Agent': 'Mozilla/5.0'}
req = urllib2.Request(site,headers=hdr)
page = urllib2.urlopen(req)
soup = BeautifulSoup(page)
print soup.title



Here is the code for scraping the lowest bid from the 'Buying BTC' table:

from selenium import webdriver

fp = webdriver.FirefoxProfile()
browser = webdriver.Firefox(firefox_profile=fp)
browser.get('http://www.cavirtex.com/orderbook')

lowest_bid = float('inf')
elements = browser.find_elements_by_xpath('//div[@id="orderbook_buy"]/table/tbody/tr/td')

for element in elements:
    text = element.get_attribute('innerHTML').strip('<b>|</b>')
    try:
        bid = float(text)
        if lowest_bid > bid:
            lowest_bid = bid
    except:
        pass

browser.quit()
print lowest_bid

In order to install Selenium for Python on your Windows-PC, run from a command line:

pip install selenium (or pip install selenium --upgrade if you already have it).

If you want the 'Selling BTC' table instead, then change "orderbook_buy" to "orderbook_sell".

If you want the 'Last Trades' table instead, then change "orderbook_buy" to "orderbook_trades".

Note:

If you consider performance critical, then you can implement the data-scraping via URL-Connection instead of Selenium, and have your program running much faster. However, your code will probably end up being a lot "messier", due to the tedious XML parsing that you'll be obliged to apply...

Here is the code for sending the previous output in an email from yourself to yourself:

import smtplib,ssl

def SendMail(username,password,contents):
    server = Connect(username)
    try:
        server.login(username,password)
        server.sendmail(username,username,contents)
    except smtplib.SMTPException,error:
        Print(error)
    Disconnect(server)

def Connect(username):
    serverName = username[username.index("@")+1:username.index(".")]
    while True:
        try:
            server = smtplib.SMTP(serverDict[serverName])
        except smtplib.SMTPException,error:
            Print(error)
            continue
        try:
            server.ehlo()
            if server.has_extn("starttls"):
                server.starttls()
                server.ehlo()
        except (smtplib.SMTPException,ssl.SSLError),error:
            Print(error)
            Disconnect(server)
            continue
        break
    return server

def Disconnect(server):
    try:
        server.quit()
    except smtplib.SMTPException,error:
        Print(error)

serverDict = {
    "gmail"  :"smtp.gmail.com",
    "hotmail":"smtp.live.com",
    "yahoo"  :"smtp.mail.yahoo.com"
}

SendMail("your_username@your_provider.com","your_password",str(lowest_bid))

The above code should work if your email provider is either gmail or hotmail or yahoo.

Please note that depending on your firewall configuration, it may ask your permission upon the first time you try it...



Source: http://stackoverflow.com/questions/21217034/scrape-data-point-using-python

Digging Up Dollars With Data Mining - An Executive's Guide

Introduction

Traditionally, organizations use data tactically - to manage operations. For a competitive edge, strong organizations use data strategically - to expand the business, to improve profitability, to reduce costs, and to market more effectively. Data mining (DM) creates information assets that an organization can leverage to achieve these strategic objectives.

In this article, we address some of the key questions executives have about data mining. These include:

•     What is data mining?

•     What can it do for my organization?

•     How can my organization get started?

Business Definition of Data Mining

Data mining is a new component in an enterprise's decision support system (DSS) architecture. It complements and interlocks with other DSS capabilities such as query and reporting, on-line analytical processing (OLAP), data visualization, and traditional statistical analysis. These other DSS technologies are generally retrospective. They provide reports, tables, and graphs of what happened in the past. A user who knows what she's looking for can answer specific questions like: "How many new accounts were opened in the Midwest region last quarter," "Which stores had the largest change in revenues compared to the same month last year," or "Did we meet our goal of a ten-percent increase in holiday sales?"

We define data mining as "the data-driven discovery and modeling of hidden patterns in large volumes of data." Data mining differs from the retrospective technologies above because it produces models - models that capture and represent the hidden patterns in the data. With it, a user can discover patterns and build models automatically, without knowing exactly what she's looking for. The models are both descriptive and prospective. They address why things happened and what is likely to happen next. A user can pose "what-if" questions to a data-mining model that can not be queried directly from the database or warehouse. Examples include: "What is the expected lifetime value of every customer account," "Which customers are likely to open a money market account," or "Will this customer cancel our service if we introduce fees?"

The information technologies associated with DM are neural networks, genetic algorithms, fuzzy logic, and rule induction. It is outside the scope of this article to elaborate on all of these technologies. Instead, we will focus on business needs and how data mining solutions for these needs can translate into dollars.

Mapping Business Needs to Solutions and Profits

What can data mining do for your organization? In the introduction, we described several strategic opportunities for an organization to use data for advantage: business expansion, profitability, cost reduction, and sales and marketing. Let's consider these opportunities very concretely through several examples where companies successfully applied DM.

Expanding your business: Keystone Financial of Williamsport, PA, wanted to expand their customer base and attract new accounts through a LoanCheck offer. To initiate a loan, a recipient just had to go to a Keystone branch and cash the LoanCheck. Keystone introduced the $5000 LoanCheck by mailing a promotion to existing customers.

The Keystone database tracks about 300 characteristics for each customer. These characteristics include whether the person had already opened loans in the past two years, the number of active credit cards, the balance levels on those cards, and finally whether or not they responded to the $5000 LoanCheck offer. Keystone used data mining to sift through the 300 customer characteristics, find the most significant ones, and build a model of response to the LoanCheck offer. Then, they applied the model to a list of 400,000 prospects obtained from a credit bureau.

By selectively mailing to the best-rated prospects determined by the DM model, Keystone generated $1.6M in additional net income from 12,000 new customers.

Reducing costs: Empire Blue Cross/Blue Shield is New York State's largest health insurer. To compete with other healthcare companies, Empire must provide quality service and minimize costs. Attacking costs in the form of fraud and abuse is a cornerstone of Empire's strategy, and it requires considerable investigative skill as well as sophisticated information technology.

The latter includes a data mining application that profiles each physician in the Empire network based on patient claim records in their database. From the profile, the application detects subtle deviations in physician behavior relative to her/his peer group. These deviations are reported to fraud investigators as a "suspicion index." A physician who performs a high number of procedures per visit, charges 40% more per patient, or sees many patients on the weekend would be flagged immediately from the suspicion index score.

What has this DM effort returned to Empire? In the first three years, they realized fraud-and-abuse savings of $29M, $36M, and $39M respectively.

Improving sales effectiveness and profitability: Pharmaceutical sales representatives have a broad assortment of tools for promoting products to physicians. These tools include clinical literature, product samples, dinner meetings, teleconferences, golf outings, and more. Knowing which promotions will be most effective with which doctors is extremely valuable since wrong decisions can cost the company hundreds of dollars for the sales call and even more in lost revenue.

The reps for a large pharmaceutical company collectively make tens of thousands of sales calls. One drug maker linked six months of promotional activity with corresponding sales figures in a database, which they then used to build a predictive model for each doctor. The data-mining models revealed, for instance, that among six different promotional alternatives, only two had a significant impact on the prescribing behavior of physicians. Using all the knowledge embedded in the data-mining models, the promotional mix for each doctor was customized to maximize ROI.

Although this new program was rolled out just recently, early responses indicate that the drug maker will exceed the $1.4M sales increase originally projected. Given that this increase is generated with no new promotional spending, profits are expected to increase by a similar amount.

Looking back at this set of examples, we must ask, "Why was data mining necessary?" For Keystone, response to the loan offer did not exist in the new credit bureau database of 400,000 potential customers. The model predicted the response given the other available customer characteristics. For Empire, the suspicion index quantified the differences between physician practices and peer (model) behavior. Appropriate physician behavior was a multi-variable aggregate produced by data mining - once again, not available in the database. For the drug maker, the promotion and sales databases contained the historical record of activity. An automated data mining method was necessary to model each doctor and determine the best combination of promotions to increase future sales.

Getting Started

In each case presented above, data mining yielded significant benefits to the business. Some were top-line results that increased revenues or expanded the customer base. Others were bottom-line improvements resulting from cost-savings and enhanced productivity. The natural next question is, "How can my organization get started and begin to realize the competitive advantages of DM?"

In our experience, pilot projects are the most successful vehicles for introducing data mining. A pilot project is a short, well-planned effort to bring DM into an organization. Good pilot projects focus on one very specific business need, and they involve business users up front and throughout the project. The duration of a typical pilot project is one to three months, and it generally requires 4 to 10 people part-time.

The role of the executive in such pilot projects is two-pronged. At the outset, the executive participates in setting the strategic goals and objectives for the project. During the project and prior to roll out, the executive takes part by supervising the measurement and evaluation of results. Lack of executive sponsorship and failure to involve business users are two primary reasons DM initiatives stall or fall short.

In reading this article, perhaps you've developed a vision and want to proceed - to address a pressing business problem by sponsoring a data mining pilot project. Twisting the old adage, we say "just because you should doesn't mean you can." Be aware that a capability assessment needs to be an integral component of a DM pilot project. The assessment takes a critical look at data and data access, personnel and their skills, equipment, and software. Organizations typically underestimate the impact of data mining (and information technology in general) on their people, their processes, and their corporate culture. The pilot project provides a relatively high-reward, low-cost, and low-risk opportunity to quantify the potential impact of DM.

Another stumbling block for an organization is deciding to defer any data mining activity until a data warehouse is built. Our experience indicates that, oftentimes, DM could and should come first. The purpose of the data warehouse is to provide users the opportunity to study customer and market behavior both retrospectively and prospectively. A data mining pilot project can provide important insight into the fields and aggregates that need to be designed into the warehouse to make it really valuable. Further, the cost savings or revenue generation provided by DM can provide bootstrap funding for a data warehouse or related initiatives.

Source:http://ezinearticles.com/?Digging-Up-Dollars-With-Data-Mining---An-Executives-Guide&id=6052872

Three Common Methods For Web Data Extraction

Probably the most common technique used traditionally to extract data from web pages this is to cook up some regular expressions that match the pieces you want (e.g., URL's and link titles). Our screen-scraper software actually started out as an application written in Perl for this very reason. In addition to regular expressions, you might also use some code written in something like Java or Active Server Pages to parse out larger chunks of text. Using raw regular expressions to pull out the data can be a little intimidating to the uninitiated, and can get a bit messy when a script contains a lot of them. At the same time, if you're already familiar with regular expressions, and your scraping project is relatively small, they can be a great solution.

Other techniques for getting the data out can get very sophisticated as algorithms that make use of artificial intelligence and such are applied to the page. Some programs will actually analyze the semantic content of an HTML page, then intelligently pull out the pieces that are of interest. Still other approaches deal with developing "ontologies", or hierarchical vocabularies intended to represent the content domain.

There are a number of companies (including our own) that offer commercial applications specifically intended to do screen-scraping. The applications vary quite a bit, but for medium to large-sized projects they're often a good solution. Each one will have its own learning curve, so you should plan on taking time to learn the ins and outs of a new application. Especially if you plan on doing a fair amount of screen-scraping it's probably a good idea to at least shop around for a screen-scraping application, as it will likely save you time and money in the long run.

So what's the best approach to data extraction? It really depends on what your needs are, and what resources you have at your disposal. Here are some of the pros and cons of the various approaches, as well as suggestions on when you might use each one:

Raw regular expressions and code

Advantages:

- If you're already familiar with regular expressions and at least one programming language, this can be a quick solution.

- Regular expressions allow for a fair amount of "fuzziness" in the matching such that minor changes to the content won't break them.

- You likely don't need to learn any new languages or tools (again, assuming you're already familiar with regular expressions and a programming language).

- Regular expressions are supported in almost all modern programming languages. Heck, even VBScript has a regular expression engine. It's also nice because the various regular expression implementations don't vary too significantly in their syntax.

Disadvantages:

- They can be complex for those that don't have a lot of experience with them. Learning regular expressions isn't like going from Perl to Java. It's more like going from Perl to XSLT, where you have to wrap your mind around a completely different way of viewing the problem.

- They're often confusing to analyze. Take a look through some of the regular expressions people have created to match something as simple as an email address and you'll see what I mean.

- If the content you're trying to match changes (e.g., they change the web page by adding a new "font" tag) you'll likely need to update your regular expressions to account for the change.

- The data discovery portion of the process (traversing various web pages to get to the page containing the data you want) will still need to be handled, and can get fairly complex if you need to deal with cookies and such.

When to use this approach: You'll most likely use straight regular expressions in screen-scraping when you have a small job you want to get done quickly. Especially if you already know regular expressions, there's no sense in getting into other tools if all you need to do is pull some news headlines off of a site.

Ontologies and artificial intelligence

Advantages:

- You create it once and it can more or less extract the data from any page within the content domain you're targeting.

- The data model is generally built in. For example, if you're extracting data about cars from web sites the extraction engine already knows what the make, model, and price are, so it can easily map them to existing data structures (e.g., insert the data into the correct locations in your database).

- There is relatively little long-term maintenance required. As web sites change you likely will need to do very little to your extraction engine in order to account for the changes.

Disadvantages:

- It's relatively complex to create and work with such an engine. The level of expertise required to even understand an extraction engine that uses artificial intelligence and ontologies is much higher than what is required to deal with regular expressions.

- These types of engines are expensive to build. There are commercial offerings that will give you the basis for doing this type of data extraction, but you still need to configure them to work with the specific content domain you're targeting.

- You still have to deal with the data discovery portion of the process, which may not fit as well with this approach (meaning you may have to create an entirely separate engine to handle data discovery). Data discovery is the process of crawling web sites such that you arrive at the pages where you want to extract data.

When to use this approach: Typically you'll only get into ontologies and artificial intelligence when you're planning on extracting information from a very large number of sources. It also makes sense to do this when the data you're trying to extract is in a very unstructured format (e.g., newspaper classified ads). In cases where the data is very structured (meaning there are clear labels identifying the various data fields), it may make more sense to go with regular expressions or a screen-scraping application.

Screen-scraping software

Advantages:

- Abstracts most of the complicated stuff away. You can do some pretty sophisticated things in most screen-scraping applications without knowing anything about regular expressions, HTTP, or cookies.

- Dramatically reduces the amount of time required to set up a site to be scraped. Once you learn a particular screen-scraping application the amount of time it requires to scrape sites vs. other methods is significantly lowered.

- Support from a commercial company. If you run into trouble while using a commercial screen-scraping application, chances are there are support forums and help lines where you can get assistance.

Disadvantages:

- The learning curve. Each screen-scraping application has its own way of going about things. This may imply learning a new scripting language in addition to familiarizing yourself with how the core application works.

- A potential cost. Most ready-to-go screen-scraping applications are commercial, so you'll likely be paying in dollars as well as time for this solution.

- A proprietary approach. Any time you use a proprietary application to solve a computing problem (and proprietary is obviously a matter of degree) you're locking yourself into using that approach. This may or may not be a big deal, but you should at least consider how well the application you're using will integrate with other software applications you currently have. For example, once the screen-scraping application has extracted the data how easy is it for you to get to that data from your own code?

When to use this approach: Screen-scraping applications vary widely in their ease-of-use, price, and suitability to tackle a broad range of scenarios. Chances are, though, that if you don't mind paying a bit, you can save yourself a significant amount of time by using one. If you're doing a quick scrape of a single page you can use just about any language with regular expressions. If you want to extract data from hundreds of web sites that are all formatted differently you're probably better off investing in a complex system that uses ontologies and/or artificial intelligence. For just about everything else, though, you may want to consider investing in an application specifically designed for screen-scraping.

As an aside, I thought I should also mention a recent project we've been involved with that has actually required a hybrid approach of two of the aforementioned methods. We're currently working on a project that deals with extracting newspaper classified ads. The data in classifieds is about as unstructured as you can get. For example, in a real estate ad the term "number of bedrooms" can be written about 25 different ways. The data extraction portion of the process is one that lends itself well to an ontologies-based approach, which is what we've done. However, we still had to handle the data discovery portion. We decided to use screen-scraper for that, and it's handling it just great. The basic process is that screen-scraper traverses the various pages of the site, pulling out raw chunks of data that constitute the classified ads. These ads then get passed to code we've written that uses ontologies in order to extract out the individual pieces we're after. Once the data has been extracted we then insert it into a database.

Source:http://ezinearticles.com/?Three-Common-Methods-For-Web-Data-Extraction&id=165416