Tuesday, 30 December 2014

How to scrape address from Google Maps

If you want to build a new online directory based website and want it to be popular with latest web contents, then you need the help of web scraping services from iWeb scraping. If you want to scrape address from maps.google.com, there is a specialized web scraping tool developed by iWeb scraping which can do the job for you. There are plenty of benefits with web scraping which includes market research, gathering customer information, managing product catalogs, compare prices, gather real estate data, gather job posting information etc. Web scraping technology is very popular nowadays and it saves lot of time and effort involved in manual extraction of data from websites.

The web scraping tools developed iWeb Scraping is very user-friendly and can extract specific information from targeted websites. It converts data from HTML web pages to useful formats like Excel spread sheets or Access database. Whatever web scraping requirements you have, you can contact iWeb Scraping as they have more than 3.5 years of web data extraction experience and offer the best prices in the industry. Also their services are available in 24x7 basis and free pilot projects will be done based on request.

Companies which require specific web data and look for an application which can automate the process and export the HTML data in structured format could benefit greatly from web scraping applications of iWeb scraping. You can easily extract data from multiple target websites, parse and re-assemble the information in HTML format to database or spread sheets as you wish. The application has simple point-and-click user-interface and any beginner can use it scrape address from Google Maps. If you want to gather address of people in particular region from Google maps, you can do it with help of web scraping application developed by iWebscraping.

Web Scraping is a technology that able to digest target website databases that are visible only as HTML web pages, and create a local, identical replica of those databases as a information or result. With our web scraping & web data extraction service we can capture web pages, then pin-point specific pieces of data/information you'd like to extract from web pages. What is needed in this process is much more than a Website crawler and set of Website wrappers. The time required to do web data extraction goes down in comparison to manually data copying and pasting job.

Source:http://www.articlesbase.com/information-technology-articles/how-to-scrape-address-from-google-maps-4683906.html

Sunday, 28 December 2014

So What Exactly Is A Private Data Scraping Services To Use You?

If your computer connects to the Internet or resources on the request for this information, and queries to different servers. If you have a website to introduce to the site server recognizes your computer's IP address and displays the data and much more. Many e - commerce sites use to log your IP address, and the browsing patterns for marketing purposes.

Related Articles

Follow Some Tips For Data Scraping Services

Web Data Scraping Assuring Scraping Success Proxy Data Services

Data Scraping Services with Proxy Data Scraping

Web Data Extraction Services for Data Collection - Screen Scrapping Services, Data Mining Services

The  Scraping server you connect to your destination or to process your information and make a filter. For example, IP address or protocol filtering traffic through a  Scraping service. As you might guess, there are many types of  Scraping services. including the ability to a high demand for the software. Email messages are quickly sent to businesses and companies to help you search for contacts.

Although there are Sanding free  Scraping IP addresses in this way can work, the use of payment services, and automatic user interface (plug and play) are easy to give.  Scraping web information services, thus offering a variety of relevant sources of data.  Scraping information service organizations are generally used where large amounts of data every day. It is possible for you to receive efficient, high precision is also affordable.

Information on the various strategies that companies,  Scraping excellent information services, and use the structure planned out and has led to the introduction of more rapid relief of the Earth.

In addition, the application software that has flexibility as a priority. In addition, there is a software that can be tailored to the needs of customers, and satisfy various customer requirements play a major role. Particular software, allows businesses to sell, a customer provides the features necessary to provide the best experience.

If you do not use a private Data Scraping Services suggest that you immediately start your Internet marketing. It is an inexpensive but vital to your marketing company. To choose how to set up a private  Scraping service, visit my blog for more information. Data Scraping Services software as the activity data and provides a large amount of information, Sorting. In this way, the company reduced the cost and time savings and greater return on investment will be a concept.

Without the steady stream of data from these sites to get stopped? Scraping HTML page requests sent by argument on the web server, depending on changes in production, it is very likely to break their staff. 

Data Scraping Services is common in the respective outsourcing company. Many companies outsource  Data Scraping Services service companies are increasingly outsourcing these services, and generally dealing with the Internet business-related activities, in particular a lot of money, can earn.

Web  Data Scraping Services, pull information from a structured plan format. Informal or semi-structured data source from the source.They are there to just work on your own server to extract data to execute. IP blocking is not a problem for them when they switch servers in minutes and back on track, scraping exercise. Try this service and you'll see what I mean.

It is an inexpensive but vital to your marketing company. To choose how to set up a private  Scraping service, visit my blog for more information. Data Scraping Services software as the activity data and provides a large amount of information, Sorting. In this way, the company reduced the cost and time savings and greater return on investment will be a concept.

Source:http://www.articlesbase.com/outsourcing-articles/so-what-exactly-is-a-private-data-scraping-services-to-use-you-5587140.html

Thursday, 25 December 2014

Limitations and Challenges in Effective Web Data Mining

Web data mining and data collection is critical process for many business and market research firms today. Conventional Web data mining techniques involve search engines like Google, Yahoo, AOL, etc and keyword, directory and topic-based searches. Since the Web's existing structure cannot provide high-quality, definite and intelligent information, systematic web data mining may help you get desired business intelligence and relevant data.

Factors that affect the effectiveness of keyword-based searches include:

• Use of general or broad keywords on search engines result in millions of web pages, many of which are totally irrelevant.

• Similar or multi-variant keyword semantics my return ambiguous results. For an instant word panther could be an animal, sports accessory or movie name.

• It is quite possible that you may miss many highly relevant web pages that do not directly include the searched keyword.

The most important factor that prohibits deep web access is the effectiveness of search engine crawlers. Modern search engine crawlers or bot can not access the entire web due to bandwidth limitations. There are thousands of internet databases that can offer high-quality, editor scanned and well-maintained information, but are not accessed by the crawlers.

Almost all search engines have limited options for keyword query combination. For example Google and Yahoo provide option like phrase match or exact match to limit search results. It demands for more efforts and time to get most relevant information. Since human behavior and choices change over time, a web page needs to be updated more frequently to reflect these trends. Also, there is limited space for multi-dimensional web data mining since existing information search rely heavily on keyword-based indices, not the real data.

Above mentioned limitations and challenges have resulted in a quest for efficiently and effectively discover and use Web resources. Send us any of your queries regarding Web Data mining processes to explore the topic in more detail.

Source: http://ezinearticles.com/?Limitations-and-Challenges-in-Effective-Web-Data-Mining&id=5012994

Monday, 22 December 2014

GScholarXScraper: Hacking the GScholarScraper function with XPath

Kay Cichini recently wrote a word-cloud R function called GScholarScraper on his blog which when given a search string will scrape the associated search results returned by Google Scholar, across pages, and then produce a word-cloud visualisation.

This was of interest to me because around the same time I posted an independent Google Scholar scraper function  get_google_scholar_df() which does a similar job of the scraping part of Kay’s function using XPath (whereas he had used Regular Expressions). My function worked as follows: when given a Google Scholar URL it will extract as much information as it can from each search result on the URL webpage  into different columns of a dataframe structure.

In the comments of his blog post I figured it’d be fun to hack his function to provide an XPath alternative, GScholarXScraper. Essensially it’s still the same function he wrote and therefore full credit should go to Kay on this one as he fully deserves it – I certainly had no previous idea how to make a word cloud, plus I hadn’t used the tm package in ages (to the point where I’d forgotten most of it!). The main changes I made were as follows:

    Restructure internal code of GScholarScraper into a series of local functions which each do a seperate job (this made it easier for me to hack because I understood what was doing what and why).

    As far as possible, strip out Regular Expressions and replace with XPath alternatives (made possible via the XML package). Hence the change of name to GScholarXScraper. Basically, apart from a little messing about with the generation of the URLs I just copied over my get_google_scholar_df() function and removed the Regular Expression alternatives. I’m not saying one is better than the other but f0r me personally, I find XPath shorter and quicker to code but either is a good approach for web scraping like this (note to self: I really need to lean more about regular expressions!) :)

•    Vectorise a few of the loops I saw (it surprises me how second nature this has become to me – I used to find the *apply family of functions rather confusing but thankfully not so much any more!).
•    Make use of getURL from the RCurl package (I was getting some mutibyte string problems originally when using readLines but this approach automatically fixed it for me).
•    Add option to make a word-cloud from either the “title” or the “description” fields of the Google Scholar search results
•    Added steaming via the Rstem package because I couldn’t get the Snowball package to install with my version of java. This was important to me because I was getting word clouds with variations of the same word on it e.g. “game”, “games”, “gaming”.
•    Forced use of URLencode() on generation of URLs to automatically avoid problems with search terms like “Baldur’s Gate” which would otherwise fail.

I think that’s pretty much everything I added. Anyway, here’s how it works (link to full code at end of post):

</pre>
<div id="LC198"># #EXAMPLE 1: Display word cloud based on the title field of each Google Scholar search result returned</div>
<div id="LC199"># GScholarXScraper(search.str = "Baldur's Gate", field = "title", write.table = FALSE, stem = TRUE)</div>
<div id="LC200">#</div>
<div id="LC201"># # word freq</div>
<div id="LC202"># # game game 71</div>
<div id="LC203"># # comput comput 22</div>
<div id="LC204"># # video video 13</div>
<div id="LC205"># # learn learn 11</div>
<div id="LC206"># # [TRUNC...]</div>
<div id="LC207"># #</div>
<div id="LC208"># #</div>
<div id="LC209"># # Number of titles submitted = 210</div>
<div id="LC210"># #</div>
<div id="LC211"># # Number of results as retrieved from first webpage = 267</div>
<div id="LC212"># #</div>
<div id="LC213"># # Be aware that sometimes titles in Google Scholar outputs are truncated - that is why, i.e., some mandatory intitle-search strings may not be contained in all titles</div>

<pre>

// image

I think that’s kind of cool and corresponds to what I would expect for a search about the legendary Baldur’s Gate computer role playing game :)  The following is produced if we look at the ‘description’ filed instead of the ‘title’ field:

</pre>

<div id="LC215"># # EXAMPLE 2: Display word cloud based on the description field of each Google Scholar search result returned</div>
<div id="LC216">GScholarXScraper(search.str = "Baldur's Gate", field = "description", write.table = FALSE, stem = TRUE)</div>
<div id="LC217">#</div>
<div id="LC218"># # word freq</div>
<div id="LC219"># # page page 147</div>
<div id="LC220"># # gate gate 132</div>
<div id="LC221"># # game game 130</div>
<div id="LC222"># # baldur baldur 129</div>
<div id="LC223"># # roleplay roleplay 21</div>
<div id="LC224"># # [TRUNC...]</div>
<div id="LC225"># #</div>
<div id="LC226"># # Number of titles submitted = 210</div>
<div id="LC227"># #</div>
<div id="LC228"># # Number of results as retrieved from first webpage = 267</div>
<div id="LC229"># #</div>
<div id="LC230"># # Be aware that sometimes titles in Google Scholar outputs are truncated - that is why, i.e., some mandatory intitle-search strings may not be contained in all titles</div>
<pre>

//image

Not bad. I could see myself using the text mining and word cloud functionality with other projects I’ve been playing with such as Facebook, Google+, Yahoo search pages, Google search pages, Bing search pages… could be fun!

Many thanks again to Kay for making his code publicly available so that I could play with it and improve my programming skill set.

Code:

Full code for GScholarXScraper can be found here: https://github.com/tonybreyal/Blog-Reference-Functions/blob/master/R/GScholarXScraper/GScholarXScraper

Original GSchloarScraper code is here: https://docs.google.com/document/d/1w_7niLqTUT0hmLxMfPEB7pGiA6MXoZBy6qPsKsEe_O0/edit?hl=en_US

Full code for just the XPath scraping function is here: https://github.com/tonybreyal/Blog-Reference-Functions/blob/master/R/googleScholarXScraper/googleScholarXScraper.R

Source:http://www.r-bloggers.com/gscholarxscraper-hacking-the-gscholarscraper-function-with-xpath/

Friday, 19 December 2014

Extractions and Skin Care

As an esthetician or skin care professional, you may have heard some controversy over the matter of performing extractions during a routine facial service. What may seem like a relatively simple procedure can actually raise great controversy in the world of esthetics. Some estheticians regard extractions as a matter of providing a complete service while others see this as inflicting trauma to the skin. Learning more about both sides of the issue can help you as a professional in making an informed decision and explaining the issue to your clients.

What is an extraction?

As a basic review, an extraction is removing impurity (plug of dead skin or oil) from a pore or pimple. It is the removal of both blackheads and whiteheads from the skin. Extractions occur after the skin has been thoroughly cleansed, exfoliated and sometimes steamed to soften the area prior to extraction.

Why Do It?

Extractions are considered a "must" by many estheticians when performing a routine facial because they want to leave their clients skin looking and feeling it's best. When done correctly, a simple extraction should be quick and relatively painless. As a trained esthetician it is important to know if your client has sensitive skin which would make them more prone to the damage that can be caused by extractions.

Why Not?

Extractions should only be performed by a trained esthetician and should not be done in excess. Extractions can cause broken capillaries or sin irritations that can lead to more (not less) breakouts. Extractions can also cause discomfort for your client when done incorrectly so you should seek their permission before performing any type of extraction during their facial. Remember your client has the right to know any product or procedure being performed on their skin and make an informed choice.

Who Decides?

As an esthetician it may be entirely up to you or it may be a procedure within your salon to do or not do extractions. It is important to check the guidelines of your employer and know their policies before performing any procedure. Remember to explain extractions and their benefits and possible complications to your client. Trust is an important part of any relationship and your client needs to know you are being open and honest with them. The last thing you want as a professional is a reputation for inflicting unnecessary and unwanted procedures or damage to your client's skin.

Bellanina Institute's owner and director, Nina Howard, is a multi-talented, forward-thinking entrepreneur who has built the Bellanina brand form the ground up to a successful million-dollar spa, spa training business, and skin care product line. Nina is a Licensed Esthetician with Para-Medical studies, Massage Therapist, Polarity Therapist, Skin Care Educator, Artist, and Professional Interior Designer.

Source:http://ezinearticles.com/?Extractions-and-Skin-Care&id=5271715

Wednesday, 17 December 2014

Benefits of Predictive Analytics and Data Mining Services

Predictive Analytics is the process of dealing with variety of data and apply various mathematical formulas to discover the best decision for a given situation. Predictive analytics gives your company a competitive edge and can be used to improve ROI substantially. It is the decision science that removes guesswork out of the decision-making process and applies proven scientific guidelines to find right solution in the shortest time possible.

Predictive analytics can be helpful in answering questions like:

•    Who are most likely to respond to your offer?
•    Who are most likely to ignore?
•    Who are most likely to discontinue your service?
•    How much a consumer will spend on your product?
•    Which transaction is a fraud?
•    Which insurance claim is a fraudulent?
•    What resource should I dedicate at a given time?

Benefits of Data mining include:

•    Better understanding of customer behavior propels better decision
•    Profitable customers can be spotted fast and served accordingly
•    Generate more business by reaching hidden markets
•    Target your Marketing message more effectively
•    Helps in minimizing risk and improves ROI.
•    Improve profitability by detecting abnormal patterns in sales, claims, transactions etc
•    Improved customer service and confidence
•    Significant reduction in Direct Marketing expenses

Basic steps of Predictive Analytics are as follows:

•    Spot the business problem or goal
•    Explore various data sources such as transaction history, user demography, catalog details, etc)
•    Extract different data patterns from the above data
•    Build a sample model based on data & problem
•    Classify data, find valuable factors, generate new variables
•    Construct a Predictive model using sample
•    Validate and Deploy this Model

Standard techniques used for it are:

•    Decision Tree
•    Multi-purpose Scaling
•    Linear Regressions
•    Logistic Regressions
•    Factor Analytics
•    Genetic Algorithms
•    Cluster Analytics
•    Product Association

Should you have any queries regarding Data Mining or Predictive Analytics applications, please feel free to contact us. We would be pleased to answer each of your queries in detail.

Source:http://ezinearticles.com/?Benefits-of-Predictive-Analytics-and-Data-Mining-Services&id=4766989

Monday, 15 December 2014

RAM Scraping a New Old Favorite For Hackers

Some of the best stories involve a conflict with an old enemy: a friend-turned-foe, long thought dead, returning from the grave for violent retribution; an ancient order of dark siders from the distant reaches of the galaxy, hiding in plain sight and waiting to seize power for themselves; a dark lord thought destroyed millennia ago, only to rise again and seek his favorite piece of jewelry.  The list goes on.

Granted, 2011 isn’t quite “millennia,” and this story isn’t meant for entertainment, but the old foe in this instance is nonetheless dangerous in its own right.  That is the year when RAM scraping malware first made major headlines: originating as an advanced version of the Trackr malware, controlled through a botnet, it was discovered in the compromised Point of Sale (POS) systems of a university and several hotels.  And while it seemed recently that this method had dwindled in popularity, the Target and other retail breaches saw it return with a vengeance.  With 110 million Target customers having their information compromised, it was easily one the largest incidents involving memory scrapers.

How does it work?  First, the malware has to be introduced into the POS network, which can happen via any machine that is connected to the network, or unsecured wireless networks.  Even with firewalls, an infected laptop could serve as a vector.  Once installed, the malware can hide in the shadows, employing encryption or antivirus-avoiding tools to prevent its identification until it’s ready to strike.  Then, when a customer’s card gets used at a POS machine, the data contained within—name, card number, security code, etc.—gets sent to the system memory.  “There is that opportunity to steal the credit card information when it is in memory, perhaps even before your payment has even been authorized, and the data hasn't even been written to the hard drive yet,” says security researcher Graham Cluley.

So, why not encrypt the system’s memory, when it’s at its most vulnerable?  Not that simple, sadly: “No matter how strong your encryption is, if the system needs to process data or process the code, everything needs to be decrypted in memory,” Chris Elisan, principal malware scientist at security firm RSA, explained to Dark Reading.

There are certain steps a company can take, of course, and should take, to reduce the risk.  Strong passwords to access the POS machines, firewalls to isolate the POS network from the Internet, disabling remote access to POS systems, to name a few.  All the same, while these measures are vital and should be used, I don’t think, in light of recent breaches, they are sufficient.  Now, I wrote a short time ago about the impending October 2014 deadline imposed by the credit card industry, regarding the systematic switch to chipped credit card technology; adopting this standard will definitely assist in eradicating this problem.  But, until such a time when a widespread implementation of new systems comes about, always be vigilant to protect your data from attack, because what’s old is new again, and a colossal data breach is a story consumers are liable to seek financial restitution for.

Source:http://www.netlib.com/blog/application-security/RAM-Scraping-a-New-Old-Favorite-For-Hackers.asp

RAM Scraping a New Old Favorite For Hackers

Some of the best stories involve a conflict with an old enemy: a friend-turned-foe, long thought dead, returning from the grave for violent retribution; an ancient order of dark siders from the distant reaches of the galaxy, hiding in plain sight and waiting to seize power for themselves; a dark lord thought destroyed millennia ago, only to rise again and seek his favorite piece of jewelry.  The list goes on.

Granted, 2011 isn’t quite “millennia,” and this story isn’t meant for entertainment, but the old foe in this instance is nonetheless dangerous in its own right.  That is the year when RAM scraping malware first made major headlines: originating as an advanced version of the Trackr malware, controlled through a botnet, it was discovered in the compromised Point of Sale (POS) systems of a university and several hotels.  And while it seemed recently that this method had dwindled in popularity, the Target and other retail breaches saw it return with a vengeance.  With 110 million Target customers having their information compromised, it was easily one the largest incidents involving memory scrapers.

How does it work?  First, the malware has to be introduced into the POS network, which can happen via any machine that is connected to the network, or unsecured wireless networks.  Even with firewalls, an infected laptop could serve as a vector.  Once installed, the malware can hide in the shadows, employing encryption or antivirus-avoiding tools to prevent its identification until it’s ready to strike.  Then, when a customer’s card gets used at a POS machine, the data contained within—name, card number, security code, etc.—gets sent to the system memory.  “There is that opportunity to steal the credit card information when it is in memory, perhaps even before your payment has even been authorized, and the data hasn't even been written to the hard drive yet,” says security researcher Graham Cluley.

So, why not encrypt the system’s memory, when it’s at its most vulnerable?  Not that simple, sadly: “No matter how strong your encryption is, if the system needs to process data or process the code, everything needs to be decrypted in memory,” Chris Elisan, principal malware scientist at security firm RSA, explained to Dark Reading.

There are certain steps a company can take, of course, and should take, to reduce the risk.  Strong passwords to access the POS machines, firewalls to isolate the POS network from the Internet, disabling remote access to POS systems, to name a few.  All the same, while these measures are vital and should be used, I don’t think, in light of recent breaches, they are sufficient.  Now, I wrote a short time ago about the impending October 2014 deadline imposed by the credit card industry, regarding the systematic switch to chipped credit card technology; adopting this standard will definitely assist in eradicating this problem.  But, until such a time when a widespread implementation of new systems comes about, always be vigilant to protect your data from attack, because what’s old is new again, and a colossal data breach is a story consumers are liable to seek financial restitution for.

Source:http://www.netlib.com/blog/application-security/RAM-Scraping-a-New-Old-Favorite-For-Hackers.asp

Saturday, 13 December 2014

Handling exceptions in scrapers

When requesting and parsing data from a source with unknown properties and random behavior (in other words, scraping), I expect all kinds of bizarrities to occur. Managing exceptions is particularly helpful in such cases.

Here is some ways that an exception might be raised.
[][0] #The list has no zeroth element, so this raises an IndexError
{}['foo'] #The dictionary has no foo element, so this raises a KeyError

Catching the exception is sometimes cleaner than preventing it from happening in the first place. Here are some examples handling bizarre exceptions in scrapers.

Example 1: Inconsistant date formats

Let’s say we’re parsing dates.
import datetime
This doesn’t raise an error.
datetime.datetime.strptime('2012-04-19', '%Y-%m-%d')
But this does.
datetime.datetime.strptime('April 19, 2012', '%Y-%m-%d')

It raises a ValueError because the date formats don’t match. So what do we do if we’re scraping a data source with multiple date formats?

Ignoring unexpected date formats

A simple thing is to ignore the date formats that we didn’t expect.

import lxml.html
import datetime
def parse_date1(source):
    rawdate = lxml.html.fromstring(source).get_element_by_id('date').text
    try:
         cleandate = datetime.datetime.strptime(rawdate, '%Y-%m-%d')
    except ValueError:
         cleandate = None
    return cleandate

print parse_date1('<div id="date">2012-04-19</div>')

If we make a clean date column in a database and put this in there, we’ll have some rows with dates and some rows with nulls. If there are only a few nulls, we might just parse those by hand.

Trying multiple date formats

Maybe we have determined that this particular data source uses three different date formats. We can try all three.

import lxml.html
import datetime

def parse_date2(source):

    rawdate = lxml.html.fromstring(source).get_element_by_id('date').text

    for date_format in ['%Y-%m-%d', '%B %d, %Y', '%d %B, %Y']:

        try:
             cleandate = datetime.datetime.strptime(rawdate, date_format)
             return cleandate
        except ValueError:
             pass
    return None

print parse_date2('<div id="date">19 April, 2012</div>')

This loops through three different date formats and returns the first one that doesn’t raise the error.

Example 2: Unreliable HTTP connection

If you’re scraping an unreliable website or you are behind an unreliable internet connection, you may sometimes get HTTPErrors or URLErrors for valid URLs. Trying again later might help.

import urllib2
def load(url):
    retries = 3
    for i in range(retries):
        try:
            handle = urllib2.urlopen(url)
            return handle.read()
        except urllib2.URLError:
            if i + 1 == retries:
                raise
            else:
                time.sleep(42)
    # never get here

print load('http://thomaslevine.com')

This function tries to download the page thee times. On the first two fails, it waits 42 seconds and tries again. On the third failure, it raises the error. On a success, it returs the content of the page.

Example 3: Logging errors rather than raising them

For more complicated parses, you might find loads of errors popping up in weird places, so you might want to go through all of the documents before deciding which to fix first or whether to do some of them manually.

import scraperwiki
for document_name in document_names:
    try:
        parse_document(document_name)
    except Exception as e:
        scraperwiki.sqlite.save([], {
            'documentName': document_name,
            'exceptionType': str(type(e)),
            'exceptionMessage': str(e)
        }, 'errors')

This catches any exception raised by a particular document, stores it in the database and then continues with the next document. Looking at the database afterwards, you might notice some trends in the errors that you can easily fix and some others where you might hard-code the correct parse.

Example 4: Exiting gracefully

When I’m scraping over 9000 pages and my script fails on page 8765, I like to be able to resume where I left off. I can often figure out where I left off based on the previous row that I saved to a database or file, but sometimes I can’t, particularly when I don’t have a unique index.


for bar in bars:
    try:
        foo(bar)
    except:
        print('Failure at bar = "%s"' % bar)
        raise

This will tell me which bar I left off on. It’s fancier if I save the information to the database, so here is how I might do that with ScraperWiki.

import scraperwiki
resume_index = scraperwiki.sqlite.get_var('resume_index', 0)
for i, bar in enumerate(bars[resume_index:]):
    try:
        foo(bar)
    except:
        scraperwiki.sqlite.save_var('resume_index', i)
        raise
scraperwiki.sqlite.save_var('resume_index', 0)

ScraperWiki has a limit on CPU time, so an error that often concerns me is the scraperwiki.CPUTimeExceededError. This error is raised after the script has used 80 seconds of CPU time; if you catch the exception, you have two CPU seconds to clean up. You might want to handle this error differently from other errors.

import scraperwiki
resume_index = scraperwiki.sqlite.get_var('resume_index', 0)
for i, bar in enumerate(bars[resume_index:]):
    try:
        foo(bar)
    except scraperwiki.CPUTimeExceededError:
        scraperwiki.sqlite.save_var('resume_index', i)
    except Exception as e:
        scraperwiki.sqlite.save_var('resume_index', i)
        scraperwiki.sqlite.save([], {
            'bar': bar,
            'exceptionType': str(type(e)),
            'exceptionMessage': str(e)
        }, 'errors')
scraperwiki.sqlite.save_var('resume_index', 0)

tl;dr

Expect exceptions to occur when you are scraping a randomly unreliable website with randomly inconsistent content, and consider handling them in ways that allow the script to keep running when one document of interest is bizarrely formatted or not available.

Source: https://blog.scraperwiki.com/2012/05/handling-exceptions-in-scrapers/

Thursday, 11 December 2014

Scraping Webmaster Tools with FMiner

The biggest problem (after the problem with their data quality) I am having with Google Webmaster Tools is that you can’t export all the data for external analysis. Luckily the guys from the FMiner.com web scraping tool contacted me a few weeks ago to test their tool. The problem with Webmaster Tools is that you can’t use web based scrapers and all the other screen scraping software tools were not that good in the steps you need to take to get to the data within Webmaster Tools. The software is available for Windows and Mac OSX users.

FMiner is a classical screen scraping app, installed on your desktop. Since you need to emulate real browser behaviour, you need to install it on your desktop. There is no coding required and their interface is visual based which makes it possible to start scraping within minutes. Another possibility I like is to upload a set of keywords, to scrape internal search engine result pages for example, something that is missing in a lot of other tools. If you need to scrape a lot of accounts, this tool provides multi-browser crawling which decreases the time needed.

This tool can be used for a lot of scraping jobs, including Google SERPs, Facebook Graph search, downloading files & images and collecting e-mail addresses. And for the real heavy scrapers, they also have built in a captcha solving API system so if you want to pass captchas while scraping, no problem.

Below you can find an introduction to the tool, with one of their tutorial video’s about scraping IMDB.com:

More basic and advanced tutorials can be found on their website: Fminer tutorials. Their tutorials show you a range of simple and complex tasks and how to use their software to get the data you need.

Guide for Scraping Webmaster Tools data

The software is capable of dealing with JavaScript and AJAX, one of the main requirements to scrape data from within Google Webmaster Tools.

Step 1: The first challenge is to login into webmaster tools. After opening a new project, first browse to https://www.google.com/webmasters/ and select the Recording button in the upper left corner.

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After browsing to this page, a goto action appears in the left panel. Click on this button and look for the “Action Options” button at the bottom of that panel. Tick the option Clear cookies before do it to avoid problems if you are already logged in for example.

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Step 2: Click the “Sign in Webmaster Tools” button. You will notice the Macro designer overview on the left registered a click as the first step.

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Step 3: Fill in your Google username and password. In the designer panel you will see the two Fill actions emerging.

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Step 4: After this step you should add some waiting time to be sure everything is fully loaded. Use the second button on the right side above the Macro Designer panel to add an action. 2000 milliseconds (2 seconds :)) will do the job.

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Step 5: Browse to the account of which you want to export the data from

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Step 6: Browse to the specific pages of which you want the data scraped

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Step 7:Scrape the data from the tables as shown in the video

Congratulations, now you are able to scrape data from Google Webmaster Tools :)

Step 8: One of the things I use it for is pulling the search query data per keyword, which you normally can’t export. To do that, you have to use a right mouse click on the keyword, which opens a menu with options. Go to open links recursively and select normal. This will loop through all the keywords.

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Step 9: This video will show you how to make use of the pagination elements to loop through all the pages:

You can also download the following file, which has a predefined set of actions to login in WMT and download the keywords, impressions and clicks: google_webmaster_tools_login.fmpx. Open the file and update the login details by clicking on those action buttons and insert your own Google account details.

Automating and scheduling scrapers

For people that want to automate and regularly download the data, you can setup a Scheduler config and within the project settings you can setup the program to send an e-mail after completion of the crawl:

Source: http://www.notprovided.eu/scraping-webmaster-tools-fminer/

Monday, 8 December 2014

Web scraping tutorial

There are three ways to access a website data. One is through a browser, the other is using a API (if the site provides one) and the last by parsing the web pages through code. The last one also known as Web Scraping is a technique of extracting information from websites using specially coded programs.

In this post we will take a quick look at writing a simple scraperusing the simplehtmldom library. But before we continue a word of caution:

Writing screen scrapers and spiders that consume large amounts of bandwidth, guess passwords, grab information from a site and use it somewhere else may well be a violation of someone’s rights and will eventually land you in trouble. Before writing  a screen scraper first see if the website offers an RSS feed or an API for the data you are looking. If not and you have to use a scraper, first check the websites policies regarding automated tools before proceeding.

Now that we have got all the legalities out of the way, lets start with the examples.

1. Installing simplehtmldom.
Simplehtmldom is a PHP library that facilitates the process of creating web scrapers. It is a HTML DOM parser written in PHP5 that let you manipulate HTML in a quick and easy way. It is a wonderful library that does away with the messy details of regular expressions and uses CSS selector style DOM access like those found in jQuery.

First download the library from sourceforge.  Unzip the library in you PHP includes directory or a directory where you will be testing the code.

Writing our first scraper.

Now that we are ready with the tools, lets write our first web scraper. For our initial idea let us see how to grab the sponsored links section from a google search page.

There are three ways to access a website data. One is through a browser, the other is using a API (if the site provides one) and the last by parsing the web pages through code. The last one also known as Web Scraping is a technique of extracting information from websites using specially coded programs.

In this post we will take a quick look at writing a simple scraperusing the simplehtmldom library. But before we continue a word of caution:

Writing screen scrapers and spiders that consume large amounts of bandwidth, guess passwords, grab information from a site and use it somewhere else may well be a violation of someone’s rights and will eventually land you in trouble. Before writing  a screen scraper first see if the website offers an RSS feed or an API for the data you are looking. If not and you have to use a scraper, first check the websites policies regarding automated tools before proceeding.

Source: http://www.codediesel.com/php/web-scraping-in-php-tutorial/

Monday, 1 December 2014

Why scraping and why TheWebMiner?

If you read this blog you are one of two things: you are either interested in web scraping and you have studied this domain for quite a while, or you are just curious about this relatively new field of interest and want to know what it is, how it’s done and especially why. Either way it’s fine!

In case you haven’t googled already this I can tell you that data extraction (or scraping) is a technique in which a computer program extracts data from human-readable output coming from another program (wikipedia). Basically it can collect all the information on a certain subject from certain places. It’s sort of the equivalent of ctrl+f, at the scale of the whole internet. It’s nothing like the search engines that we currently use because it can extract the data in a certain file, as excel, csv (coma separated values) or any other that the buyer wants, and also extracts only the relevant data, only the values that you are interested in.

I hope now that you understand the concept and you are wondering just why would you need such data. Well let’s take the example of an online store, pretty common nowadays, and of course the manager just like any manager wants his business to thrive, so, for that he has to keep up with the other online stores. Now the web scraping takes place: it is very useful for him to have, saved as excels all the competitor’s prices of certain products if not all of them. By this he can maintain a fair pricing policy and always be ahead of his competitors by knowing all of their prices and fluctuations.  Of course the data collecting can also be done manually but this is not effective because we are talking of thousand of products each one having its own page and so on. This is only one example of situation in which scrapping is useful but there are hundreds and each one of them it’s profitable for the company.

By now I’ve talked about what it is and why you should be interested in it, from now on I’m going to explain why you should use thewebminer.com. First of all, it’s easy: you only have to specify what type of data you want and from where and we’ll manage the rest. Throughout the project you will receive first of all an approximation of price, followed by a time approximation. All the time you will be in contact with us so you can find out at any point what is the state of your project. The pricing policy is reasonable and depends on factors like the project size or complexity. For very big projects a discount may be applicable so the total cost be within reason.

Now I believe that thewebminer.com is able to manage with any kind of situation or requirement from users all over the world and to convince you, free samples are available at any project you may have or any uncertainty or doubt.

Source:http://thewebminer.com/blog/2013/07/

Friday, 28 November 2014

Webscraping using readLines and RCurl

There is a massive amount of data available on the web. Some of it is in the form of precompiled, downloadable datasets which are easy to access. But the majority of online data exists as web content such as blogs, news stories and cooking recipes. With precompiled files, accessing the data is fairly straightforward; just download the file, unzip if necessary, and import into R. For “wild” data however, getting the data into an analyzeable format is more difficult. Accessing online data of this sort is sometimes reffered to as “webscraping”. Two R facilities, readLines() from the base package and getURL() from the RCurl package make this task possible.

readLines

For basic webscraping tasks the readLines() function will usually suffice. readLines() allows simple access to webpage source data on non-secure servers. In its simplest form, readLines() takes a single argument – the URL of the web page to be read:

web_page <- readLines("http://www.interestingwebsite.com")

As an example of a (somewhat) practical use of webscraping, imagine a scenario in which we wanted to know the 10 most frequent posters to the R-help listserve for January 2009. Because the listserve is on a secure site (e.g. it has https:// rather than http:// in the URL) we can't easily access the live version with readLines(). So for this example, I've posted a local copy of the list archives on the this site.

One note, by itself readLines() can only acquire the data. You'll need to use grep(), gsub() or equivalents to parse the data and keep what you need.

# Get the page's source
web_page <- readLines("http://www.programmingr.com/jan09rlist.html")
# Pull out the appropriate line
author_lines <- web_page[grep("<I>", web_page)]
# Delete unwanted characters in the lines we pulled out
authors <- gsub("<I>", "", author_lines, fixed = TRUE)
# Present only the ten most frequent posters
author_counts <- sort(table(authors), decreasing = TRUE)
author_counts[1:10]
[webscrape results]


We can see that Gabor Grothendieck was the most frequent poster to R-help in January 2009.

The RCurl package

To get more advanced http features such as POST capabilities and https access, you'll need to use the RCurl package. To do webscraping tasks with the RCurl package use the getURL() function. After the data has been acquired via getURL(), it needs to be restructured and parsed. The htmlTreeParse() function from the XML package is tailored for just this task. Using getURL() we can access a secure site so we can use the live site as an example this time.

# Install the RCurl package if necessary
install.packages("RCurl", dependencies = TRUE)
library("RCurl")
# Install the XML package if necessary
install.packages("XML", dependencies = TRUE)
library("XML")
# Get first quarter archives
jan09 <- getURL("https://stat.ethz.ch/pipermail/r-help/2009-January/date.html", ssl.verifypeer = FALSE)
jan09_parsed <- htmlTreeParse(jan09)
# Continue on similar to above
...

For basic webscraping tasks readLines() will be enough and avoids over complicating the task. For more difficult procedures or for tasks requiring other http features getURL() or other functions from the RCurl package may be required. For more information on cURL visit the project page here.

Source: http://www.r-bloggers.com/webscraping-using-readlines-and-rcurl-2/

Wednesday, 26 November 2014

Screen scrapers: To program or to purchase?

Companies today use screen scraping tools for a variety of purposes, including collecting competitive information, capturing product specs, moving data between legacy and new systems, and keeping inventory or price lists accurate.

Because of their popularity and reputation as being extremely efficient tools for quickly gathering applicable display data, screen scraping tools or browser add-ons are a dime a dozen: some free, some low cost, and some part of a larger solution. Alternatively, you can build your own if you are (or know) a programming whiz. Each tool has its potential pros and cons, however, to keep in mind as you determine which type of tool would best fit your business need.

Program-your-own screen scraper

Pros:

    Using in-house resources doesn't require additional budget

Cons:

    Properly creating scripts to automate screen scraping can take a significant amount of time initially, and continues to take time in order to maintain the process. If, for instance, objects from which you're gathering data move on a web page, the entire process will either need to be re-automated, or someone with programming acumen will have to edit the script every time there is a change.

    It's questionable whether or not this method actually saves time and resources

Free or cheap scrapers

Pros:

    Here again, budget doesn't ever enter the picture, and you can drive the process yourself.

    Some tools take care of at least some of the programming heavy lifting required to screen scrape effectively

Cons:

    Many inexpensive screen scrapers require that you get up to speed on their programming language—a time-consuming process that negates the idea of efficiency that prompted the purchase.

Screen scraping as part of a full automation solution

Pros:

    In the amount of time it takes to perform one data extraction task, you have a completely composed script that the system writes for you

    It's the easiest to use out of all of the options

    Screen scraping is only part of the package; you can leverage automation software to automate nearly any task or process including tasks in Windows, Excel automation, IT processes like uploads, backups, and integrations, and business processes like invoice processing.

    You're likely to get buy-in for other automation projects (and visibility for the efficiency you're introducing to the organization) if you pick a solution with a clear and scalable business purpose, not simply a tool to accomplish a single task.

Cons:

    This option has the highest price tag because of its comprehensive capabilities.

Looking for more information?

Here are some options to dig deeper into screen scraping, and deciding on the right tool for you:

 Watch a couple demos of what screen scraping looks like with an automation solution driving the process.

 Read our web data extraction guide for a complete overview.

 Try screen scraping today by downloading a free trial.

Source: https://www.automationanywhere.com/screen-scrapers

Monday, 24 November 2014

Data Mining Outsourcing in a Better and Unique Approach

Data mining outsourcing services are ideal for clarity in various decision making processes.  It is the ultimate goal of any organization and business to increase on its profits as well as strengthen the bond with its customers. Equipping the business in such a way that it’s very easy to detect frauds and manage risks in a convenient manner is equally important. Volumes of data that are irrelevant or cannot be used when raw needs to be converted to a more useful form.  The data mining outsourcing services can greatly help you to analyze and interpret data in a more diligent way.

This service to reliable, experienced and qualified hands is very important. Your research project or engineering project can be easily and conveniently handled by experienced staff who guarantees you an accuracy level of about 98% and a massive reduction in operating costs. The quality of work is unsurpassed and the presentation is done in a format that is easy and simple for you. The project is done in a very short time alleviating you delays as well as ensuring on-time completion of your projects. To enjoy a successful outsourcing experience, you need to bank on a famous and reliable expertise.

The only time to rely with data mining outsourcing services is when you do not have a reliable, experienced expertise in your business.  Statistics indicate that it’s very easy to lose business intelligence or expose the privacy of the customers through this process. However companies which offer secure outsourcing process are on the increase as a result of massive competition. It’s an opportunity to develop your potential of sourced data and improve your business in all fields. 

Data mining potential applications are infinite. However major applications are in the marketing research and scientific projects. It’s done both on large and small quantities of data by experienced staff well known for their best analytical procedures to guarantee you accurate and easy to use information. Data mining outsourcing services are the only perfect way to profitability.

Source:http://www.e-edge.biz/Data_Mining_Outsourcing_in_a_Better_and_Unique_Approach.html

Wednesday, 19 November 2014

NHL ending dry scraping of ice before overtime

TORONTO (AP) — The NHL will no longer dry scrape the ice before overtime.

Instituted this season in an effort to reduce the number of shootouts, the dry scraping will stop after Friday's games.

The general managers decided at their meeting Tuesday to make the change after the league talked to the players' union the past few days.

Beginning Saturday, ice crews around the league will again shovel the ice after regulation as they did in previous years. The GMs said the dry scrape was causing too much of a delay. Director of hockey operations Colin Campbell said the delays were lasting from more than four minutes to almost seven.

The dry scrape initially had been approved in hopes of reducing shootouts by improving scoring chances without unduly slowing play by recoating the ice.

The GMs also discussed expanded video review, including goaltender interference, and the possibility of three-on-three overtime. The American Hockey League is experimenting with the three-on-three format this season.

This annual meeting the day after the Hockey Hall of Fame induction usually doesn't produce actual changes, with the dry scrape providing an exception.

The main purpose is to set up the March meeting in Boca Raton, Florida, where these items will be further addressed.

Source:http://missoulian.com/sports/hockey/nhl-ending-dry-scraping-of-ice-before-overtime/article_3dd5473c-6102-5800-99f7-2c98be0f99ad.html

Tuesday, 18 November 2014

Scraping websites using the Scraper extension for Chrome

If you are using Google Chrome there is a browser extension for scraping web pages. It’s called “Scraper” and it is easy to use. It will help you scrape a website’s content and upload the results to google docs.

Walkthrough: Scraping a website with the Scraper extension
  •     Open Google Chrome and click on Chrome Web Store
  •     Search for “Scraper” in extensions
  •     The first search result is the “Scraper” extension
  •     Click the add to chrome button.
  •     Now let’s go back to the listing of UK MPs
  •     Open http://www.parliament.uk/mps-lords-and-offices/mps/
  •     Now mark the entry for one MP
  •     http://farm9.staticflickr.com/8490/8264509932_6cc8802992_o_d.png
  •     Right click and select “scrape similar…”
  •     http://farm9.staticflickr.com/8200/8264509972_f3a9e5d8e8_o_d.png
  •     A new window will appear – the scraper console
  •     http://farm9.staticflickr.com/8073/8263440961_9b94e63d56_b_d.jpg
  •     In the scraper console you will see the scraped content
  •     Click on “Save to Google Docs…” to save the scraped content as a Google Spreadsheet.
Walkthrough: extended scraping with the Scraper extension

Note: Before beginning this recipe – you may find it useful to understand a bit about HTML. Read our HTML primer.

Easy wasn’t it? Now let’s do something a little more complicated. Let’s say we’re interested in the roles a specific actress played. The source for all kinds of data on this is the IMDB (You can also search on sites like DBpedia or Freebase for this kinds of information; however, we’ll stick to IMDB to show the principle)

    Let’s say we’re interested in creating a timeline with all the movies the Italian actress Asia Argento ever starred; where do we start?

    The IMDB has a quite comprehensive archive of actors. Asia Argento’s site is: http://www.imdb.com/name/nm0000782/

    If you open the page you’ll see all the roles she ever played, together with a title and the year – let’s scrape this information

    Try to scrape it like we did above

    You’ll see the list comes out garbled – this is because the list here is structured quite differently.

    Go to the scraper console. Notice the small box on the upper left, saying XPath?

    XPath is a query language for HTML and XML.

    XPath can help you find the elements in the page you’re interested in – all you need to do is find the right element and then write the xpath for it.

    Now let’s assemble our table.

    You’ll see that our current Xpath – the one including the whole information is “//div[3]/div[3]/div[2]/div”

    http://farm9.staticflickr.com/8344/8264510130_ae31697fde_o_d.png

    Xpath is very simple it tells the computer to look at the HTML document and select <div> element number 3, then in this the third one, the second one and then all <div> elements (which if you count down our list, results in exactly where you are right now.
  •     However, we’d like to have the data separated out.
  •     To do this use the columns part of the scraper console…
  •     Let’s find our title first – look at the title using Inspect Element
  •     http://farm9.staticflickr.com/8355/8263441157_b4672d01b2_o_d.png
  •     See how the title is within a <b> tag? Let’s add the tag to our xpath.
  •     The expression seems to work well: let’s make this our first column
  •     In the “Columns” section, change the name of the first column to “title”
  •     Now let’s add the XPATH for the title to it
  •     The xpaths in the columns section are relative, that means “./b” will select the <b> element
  •     add “./b” to the xpath for the title column and click “scrape”
  •     http://farm9.staticflickr.com/8357/8263441315_42d6a8745d_o_d.png
  •     See how you only get titles?
  •     Now let’s continue for year? Years are within one <span>
  •     Create a new column by clicking on the small plus next to your “title” column
  •     Now create the “year” column with xpath “./span”
  •     http://farm9.staticflickr.com/8347/8263441355_89f4315a78_o_d.png
  •     Click on scrape and see how the year is added
  •     See how easily we got information out of a less structured webpage?
Source: http://schoolofdata.org/handbook/recipes/scraper-extension-for-chrome/

Sunday, 16 November 2014

Building Java Object Graph with Tour de France results – using screen scraping, java.util.Parser and assorted facilities

Last Saturday, the Tour de France 2011 departed. For people like myself, enjoying sports and working on Data Visualizations on the one hand and far fetched uses of SQL on the other, the Tour de France offers a wealth of data to work with: rankings for each stage in various categories, nationalities and teams to group by, distances and velocity, years to compare with one another and the like. So it has been my intention for some time to get hold of that data in a format I could work with.

Today I finally found some time to get it done. To locate the statistics for the Tour de France editions for the last few years and get them onto my laptop and into my database. This article describes the first part of that journey: how to get the stage results from some source on the internet into my locally running Java program in an appropriate object structure.

My starting point is the official Tour de France website:

Image

This website goes back to 2007 and also has the latest (2011) results. It presents the result in a format pleasing to the human eye – based on an HTML structure that is fairly pleasing to my groping Java code as well.

Analyzing the source of the Tour de France data

I start my explorations in Firefox, using the Firebug plugin. When I select the tab with the results for a particular stage, I inspect the (AJAX) call that is made to retrieve the stage results into the browser:

Image

The URL that was accessed is www.letour.fr/2010/TDF/LIVE/us/700/classement/ITE.html . When I access that URL directly, I see an HTML fragment with the individual ranking for the 7th stage in 2010. It turns out that with ITG instead of ITE in this URL, I get the overall ranking after the 7th Stage. Using IME in stead of ITE, I get the 7th stage’s climbers’ standing. And so on.

The HTML associated with the stage standing looks like this:

Image

Which is not as user friendly as the corresponding display in the browser:

Image

but still fairly well structured and programmatically interpretable.

Retrieving HTML fragments and parsing in Java

Consuming these HTML fragments with stage standings into my own Java code is very easy. Parsing the data and turning it into sensible Java Objects is slightly more work, but still quite feasible. From the Java Objects I next need to create a persistent storage for the data – that is the subject for another article.

Using the Java URL class and its openStream method to open an InputStream on whatever content can be found at the URL, it is dead easy to start reading the HTML from the Tour de France website into my Java program. I make use of the java.util.Scanner class to work my way through the HTML by Table Row (TR element). When you inspect the HTML fragments, it is clear early on that every individual rider’s entry corresponds with a TR element, so it seems only logical to have the Scanner break up the data by TR.

private static Stage processStage(int year, int stageSequence, Map<Integer, Rider> riders) throws java.io.IOException, java.net.MalformedURLException {

    String typeOfStanding = "ITE";
     URL stageStanding = new URL("http://www.letour.fr/"+year+"/TDF/LIVE/us/"
                                +(stageSequence==0?"0":stageSequence+"00") +
                                "/classement/"+typeOfStanding+".html");
    InputStream stream = stageStanding.openStream();
    Scanner scanner = new Scanner(stream);
    scanner.useDelimiter("</tr>");
    Stage stage = new Stage();
    stage.setSequence(stageSequence);
    boolean first = true;
    boolean firstStanding = true;
    while (scanner.hasNext()) {
        String entry = scanner.next();
        if (first) {
            first = false;
            Matcher regexMatcher = regexDistance.matcher(entry);
            if (regexMatcher.find()) {
                String distanceString = regexMatcher.group();
                stage.setTotalDistance(Float.parseFloat(distanceString.substring(0, distanceString.length() - 3)));
            }
        }
        if (!first) {
            String[] els = entry.split("/td>");
            if (els.length > 1) { // only the standing-entries have more than one td element
                Integer riderNumber = Integer.parseInt(extractValue(els[2]));

                Rider rider=null;
                if (riders.containsKey(riderNumber)) {
                    rider = riders.get(riderNumber);
                }
                else {
                    rider = new Rider(extractValue(els[1]),riderNumber, extractValue(els[3]));
                    riders.put(riderNumber,rider);
                }
                Standing standing =
                    new Standing(firstStanding ? 1 : (Integer.parseInt(extractValue(els[0]).replace(".", ""))),
                                  rider,extractValue(els[4]),
                                  extractValue(els[5]));
                firstStanding = false;
                stage.getStandings().add(standing);                }
        }
    } //while
    scanner.close();
    return stage;
}

Subsequently, the TR elements need to be broken up in the TD cell elements that contain the rank, rider’s name, their number, the team they ride for and the time for the stage as well as their lag with regard to the winner. I have used a simple split (on /td>) to extract the cells. The final logic for pulling the correct value from the cell is in the method extractValue. Note: this code is not very pretty, and I am not necessarily overly proud of it. On the other hand: it is one-time-use-only code and it is still fairly compact and easy to write and read.

private static String extractValue(String el) {
    String r = el.split("</")[0];
    if (r.lastIndexOf(">") > 0) {
        r = r.substring(r.lastIndexOf(">") + 1);
    }
    return r.split("<")[0];
}

I have created a few domain classes: Rider, Stage, Standing (as well as Tour) that are a business domain like representation of the Tour de France result data. Objects based on these classes are instantiated in the processStage method that is being invoked from the processTour method.

public static void processTour(Tour tour) throws IOException, MalformedURLException {
    if (tour.isPrologue())
      tour.getStages().add(processStage(tour.getYear(),0, tour.getRiders()));

    for (int i=1;i<= tour.getNumberOfStages();i++)  {
        tour.getStages().add(processStage(tour.getYear(),i, tour.getRiders()));
    }
}

When I run the TourManager class – a class that create a single Tour object for the Tour de France in 2010 –

public class TourManager {
     List<Tour> tours = new ArrayList<Tour>();
     public TourManager() {
        tours.add(new Tour(2010, 20, true));
        try {
            ProcessTourStandings.processTour(tours.get(0));
        } catch (MalformedURLException e) {
            System.out.println(e.getMessage());
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
     public static void main(String[] args) {
        TourManager tm = new TourManager();
        for (Tour tour : tm.getTours()) {
            for (Stage stage : tour.getStages()) {
                System.out.println("================ Stage " + stage.getSequence() + "(" + stage.getTotalDistance() +
                                   " km)");
                for (Standing standing : stage.getStandings()) {
                    if (standing.getRank() < 4) {
                        System.out.println(standing.getRank() + "." + standing.getRider().getName());
                    }
                }
            }
        }
    }

it will print the top 3 in every stage:

Image

Source:http://technology.amis.nl/2011/07/04/building-java-object-graph-with-tour-de-france-results-using-screen-scraping-java-util-parser-and-assorted-facilities/

Friday, 14 November 2014

Scraping Data: Site-specific Extractors vs. Generic Extractors

Scraping is becoming a rather mundane job with every other organization getting its feet wet with it for their own data gathering needs. There have been enough number of crawlers built – some open-sourced and others internal to organizations for in-house utilities. Although crawling might seem like a simple technique at the onset, doing this at a large-scale is the real deal. You need to have a distributed stack set up to take care of handling huge volumes of data, to provide data in a low-latency model and also to deal with fail-overs. This still is achievable after crossing the initial tech barrier and via continuous optimizations. (P.S. Not under-estimating this part because it still needs a team of Engineers monitoring the stats and scratching their heads at times).

Social Media Scraping

Focused crawls on a predefined list of sites

However, you bump into a completely new land if your goal is to generate clean and usable data sets from these crawls i.e. “extract” data in a format that your DB can process and aid in generating insights. There are 2 ways of tackling this:

a. site-specific extractors which give desired results

b. generic extractors that result in few surprises

Assuming you still do focused crawls on a predefined list of sites, let’s go over specific scenarios when you have to pick between the two-

1. Mass-scale crawls; high-level meta data - Use generic extractors when you have a large-scale crawling requirement on a continuous basis. Large-scale would mean having to crawl sites in the range of hundreds of thousands. Since the web is a jungle and no two sites share the same template, it would be impossible to write an extractor for each. However, you have to settle in with just the document-level information from such crawls like the URL, meta keywords, blog or news titles, author, date and article content which is still enough information to be happy with if your requirement is analyzing sentiment of the data.

cb1c0_one-size

A generic extractor case

Generic extractors don’t yield accurate results and often mess up the datasets deeming it unusable. Reason being

programatically distinguishing relevant data from irrelevant datasets is a challenge. For example, how would the extractor know to skip pages that have a list of blogs and only extract the ones with the complete article. Or delineating article content from the title on a blog page is not easy either.

To summarize, below is what to expect of a generic extractor.

Pros-

minimal manual intervention

low on effort and time

can work on any scale

Cons-

Data quality compromised

inaccurate and incomplete datasets

lesser details suited only for high-level analyses

Suited for gathering- blogs, forums, news

Uses- Sentiment Analysis, Brand Monitoring, Competitor Analysis, Social Media Monitoring.

2. Low/Mid scale crawls; detailed datasets - If precise extraction is the mandate, there’s no going away from site-specific extractors. But realistically this is do-able only if your scope of work is limited i.e. few hundred sites or less. Using site-specific extractors, you could extract as many number of fields from any nook or corner of the web pages. Most of the times, most pages on a website share similar templates. If not, they can still be accommodated for using site-specific extractors.

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Designing extractor for each website

Pros-

High data quality

Better data coverage on the site

Cons-

High on effort and time

Site structures keep changing from time to time and maintaining these requires a lot of monitoring and manual intervention

Only for limited scale

Suited for gathering - any data from any domain on any site be it product specifications and price details, reviews, blogs, forums, directories, ticket inventories, etc.

Uses- Data Analytics for E-commerce, Business Intelligence, Market Research, Sentiment Analysis

Conclusion

Quite obviously you need both such extractors handy to take care of various use cases. The only way generic extractors can work for detailed datasets is if everyone employs standard data formats on the web (Read our post on standard data formats here). However, given the internet penetration to the masses and the variety of things folks like to do on the web, this is being overly futuristic.

So while site-specific extractors are going to be around for quite some time, the challenge now is to tweak the generic ones to work better. At PromptCloud, we have added ML components to make them smarter and they have been working well for us so far.

What have your challenges been? Do drop in your comments.

Source: https://www.promptcloud.com/blog/scraping-data-site-specific-extractors-vs-generic-extractors/

Wednesday, 12 November 2014

'Scrapers' Dig Deep for Data on Web

At 1 a.m. on May 7, the website PatientsLikeMe.com noticed suspicious activity on its "Mood" discussion board. There, people exchange highly personal stories about their emotional disorders, ranging from bipolar disease to a desire to cut themselves.

It was a break-in. A new member of the site, using sophisticated software, was "scraping," or copying, every single message off PatientsLikeMe's private online forums.

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Bilal Ahmed wrote about his health on a site that was scraped. Andrew Quilty for The Wall Street Journal.

PatientsLikeMe managed to block and identify the intruder: Nielsen Co., the privately held New York media-research firm. Nielsen monitors online "buzz" for clients, including major drug makers, which buy data gleaned from the Web to get insight from consumers about their products, Nielsen says.

"I felt totally violated," says Bilal Ahmed, a 33-year-old resident of Sydney, Australia, who used PatientsLikeMe to connect with other people suffering from depression. He used a pseudonym on the message boards, but his PatientsLikeMe profile linked to his blog, which contains his real name.

After PatientsLikeMe told users about the break-in, Mr. Ahmed deleted all his posts, plus a list of drugs he uses. "It was very disturbing to know that your information is being sold," he says. Nielsen says it no longer scrapes sites requiring an individual account for access, unless it has permission.

Related Reading

    Digits: Escaping the 'Scrapers'
    Complete Coverage: What They Know

Journal Community

The market for personal data about Internet users is booming, and in the vanguard is the practice of "scraping." Firms offer to harvest online conversations and collect personal details from social-networking sites, résumé sites and online forums where people might discuss their lives.

The emerging business of web scraping provides some of the raw material for a rapidly expanding data economy. Marketers spent $7.8 billion on online and offline data in 2009, according to the New York management consulting firm Winterberry Group LLC. Spending on data from online sources is set to more than double, to $840 million in 2012 from $410 million in 2009.

The Wall Street Journal's examination of scraping—a trade that involves personal information as well as many other types of data—is part of the newspaper's investigation into the business of tracking people's activities online and selling details about their behavior and personal interests.

Some companies collect personal information for detailed background reports on individuals, such as email addresses, cell numbers, photographs and posts on social-network sites.

Others offer what are known as listening services, which monitor in real time hundreds or thousands of news sources, blogs and websites to see what people are saying about specific products or topics.

One such service is offered by Dow Jones & Co., publisher of the Journal. Dow Jones collects data from the Web—which may include personal information contained in news articles and blog postings—that help corporate clients monitor how they are portrayed. It says it doesn't gather information from password-protected parts of sites.

It's rarely a coincidence when you see Web ads for products that match your interests. WSJ's Christina Tsuei explains how advertisers use cookies to track your online habits.

The competition for data is fierce. PatientsLikeMe also sells data about its users. PatientsLikeMe says the data it sells is anonymized, no names attached.

Nielsen spokesman Matt Anchin says the company's reports to its clients include publicly available information gleaned from the Internet, "so if someone decides to share personally identifiable information, it could be included."

Internet users often have little recourse if personally identifiable data is scraped: There is no national law requiring data companies to let people remove or change information about themselves, though some firms let users remove their profiles under certain circumstances.

California has a special protection for public officials, including politicians, sheriffs and district attorneys. It makes it easier for them to remove their home address and phone numbers from these databases, by filling out a special form stating they fear for their safety.

Data brokers long have scoured public records, such as real-estate transactions and courthouse documents, for information on individuals. Now, some are adding online information to people's profiles.

Many scrapers and data brokers argue that if information is available online, it is fair game, no matter how personal.

"Social networks are becoming the new public records," says Jim Adler, chief privacy officer of Intelius Inc., a leading paid people-search website. It offers services that include criminal background checks and "Date Check," which promises details about a prospective date for $14.95.

"This data is out there," Mr. Adler says. "If we don't bring it to the consumer's attention, someone else will."

Scraping for Your Real Name

PeekYou.com has applied for a patent for a way to, among other things, match people's real names to pseudonyms they use on blogs, Twitter and online forums.

Read PeekYou.com's patent application.

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New York-based PeekYou LLC has applied for a patent for a method that, among other things, matches people's real names to the pseudonyms they use on blogs, Twitter and other social networks. PeekYou's people-search website offers records of about 250 million people, primarily in the U.S. and Canada.

PeekYou says it also is starting to work with listening services to help them learn more about the people whose conversations they are monitoring. It says it hands over only demographic information, not names or addresses.

Employers, too, are trying to figure out how to use such data to screen job candidates. It's tricky: Employers legally can't discriminate based on gender, race and other factors they may glean from social-media profiles.

One company that screens job applicants for employers, InfoCheckUSA LLC in Florida, began offering limited social-networking data—some of it scraped—to employers about a year ago. "It's slowly starting to grow," says Chris Dugger, national account manager. He says he's particularly interested in things like whether people are "talking about how they just ripped off their last employer."

Scrapers operate in a legal gray area. Internationally, anti-scraping laws vary. In the U.S., court rulings have been contradictory. "Scraping is ubiquitous, but questionable," says Eric Goldman, a law professor at Santa Clara University. "Everyone does it, but it's not totally clear that anyone is allowed to do it without permission."

Scrapers and listening companies say what they're doing is no different from what any person does when gathering information online—they just do it on a much larger scale.

"We take an incomprehensible amount of information and make it intelligent," says Chase McMichael, chief executive of InfiniGraph, a Palo Alto, Calif., "listening service" that helps companies understand the likes and dislikes of online customers.

Scraping services range from dirt cheap to custom-built. Some outfits, such as 80Legs.com in Texas, will scrape a million Web pages for $101. One Utah company, screen-scraper.com, offers do-it-yourself scraping software for free. The top listening services can charge hundreds of thousands of dollars to monitor and analyze Web discussions.

Some scrapers-for-hire don't ask clients many questions.

"If we don't think they're going to use it for illegal purposes—they often don't tell us what they're going to use it for—generally, we'll err on the side of doing it," says Todd Wilson, owner of screen-scraper.com, a 10-person firm in Provo, Utah, that operates out of a two-room office. It is one of at least three firms in a scenic area known locally as "Happy Valley" that specialize in scraping.

Enlarge Image

Some of the computer code behind screen-scraper.com's software. Chris Detrick for The Wall Street Journal

Screen-scraper charges between $1,500 and $10,000 for most jobs. The company says it's often hired to conduct "business intelligence," working for companies who want to scrape competitors' websites.

One recent assignment: A major insurance company wanted to scrape the names of agents working for competitors. Why? "We don't know," says Scott Wilson, the owner's brother and vice president of sales. Another job: attempting to scrape Facebook for a multi-level marketing company that wanted email addresses of users who "like" the firm's page—as well as their friends—so they all could be pitched products.

Scraping often is a cat-and-mouse game between websites, which try to protect their data, and the scrapers, who try to outfox their defenses. Scraping itself isn't difficult: Nearly any talented computer programmer can do it. But penetrating a site's defenses can be tough.

One defense familiar to most Internet users involves "captchas," the squiggly letters that many websites require people to type to prove they're human and not a scraping robot. Scrapers sometimes fight back with software that deciphers captchas.

More From the Series

    Web's New Goldmine: Your Secrets

    Personal Details Exposed Via Biggest Websites

    Microsoft Quashed Bid to Boost Web Privacy

    On Web's Cutting Edge, Anonymity in Name Only

    Stalking by Cellphone

    Google Agonizes Over Privacy

    The Tracking Ecosystem

    On the Web, Children Face Intensive Tracking

Some professional scrapers stage blitzkrieg raids, mounting around a dozen simultaneous attacks on a website to grab as much data as quickly as possible without being detected or crashing the site they're targeting.

Raids like these are on the rise. "Customers for whom we were regularly blocking about 1,000 to 2,000 scrapes a month are now seeing three times or in some cases 10 times as much scraping," says Marino Zini, managing director of Sentor Anti Scraping System. The company's Stockholm team blocks scrapers on behalf of website clients.

At Monster.com, the jobs website that stores résumés for tens of millions of individuals, fighting scrapers is a full-time job, "every minute of every day of every week," says Patrick Manzo, global chief privacy officer of Monster Worldwide Inc. Facebook, with its trove of personal data on some 500 million users, says it takes legal and technical steps to deter scraping.

At PatientsLikeMe, there are forums where people discuss experiences with AIDS, supranuclear palsy, depression, organ transplants, post-traumatic stress disorder and self-mutilation. These are supposed to be viewable only by members who have agreed not to scrape, and not by intruders such as Nielsen.

"It was a bad legacy practice that we don't do anymore," says Dave Hudson, who in June took over as chief executive of the Nielsen unit that scraped PatientsLikeMe in May. "It's something that we decided is not acceptable, and we stopped."

Mr. Hudson wouldn't say how often the practice occurred, and wouldn't identify its client.

The Nielsen unit that did the scraping is now part of a joint venture with McKinsey & Co. called NM Incite. It traces its roots to a Cincinnati company called Intelliseek that was founded in 1997. One of its most successful early businesses was scraping message boards to find mentions of brand names for corporate clients.

In 2001, the venture-capital arm of the Central Intelligence Agency, In-Q-Tel Inc., was among a group of investors that put $8 million into the business.

Intelliseek struggled to set boundaries in the new business of monitoring individual conversations online, says Sundar Kadayam, Intelliseek's co-founder. The firm decided it wouldn't be ethical to use automated software to log into private message boards to scrape them.

But, he says, Intelliseek occasionally would ask employees to do that kind of scraping if clients requested it. "The human being can just sign in as who they are," he says. "They don't have to be deceitful."

In 2006, Nielsen bought Intelliseek, which had revenue of more than $10 million and had just become profitable, Mr. Kadayam says. He left one year after the acquisition.

At the time, Nielsen, which provides television ratings and other media services, was looking to diversify into digital businesses. Nielsen combined Intelliseek with a New York startup it had bought called BuzzMetrics.

The new unit, Nielsen BuzzMetrics, quickly became a leader in the field of social-media monitoring. It collects data from 130 million blogs, 8,000 message boards, Twitter and social networks. It sells services such as "ThreatTracker," which alerts a company if its brand is being discussed in a negative light. Clients include more than a dozen of the biggest pharmaceutical companies, according to the company's marketing material.

Like many websites, PatientsLikeMe has software that detects unusual activity. On May 7, that software sounded an alarm about the "Mood" forum.

David Williams, the chief marketing officer, quickly determined that the "member" who had triggered the alert actually was an automated program scraping the forum. He shut down the account.

The next morning, the holder of that account e-mailed customer support to ask why the login and password weren't working. By the afternoon, PatientsLikeMe had located three other suspect accounts and shut them down. The site's investigators traced all of the accounts to Nielsen BuzzMetrics.

On May 18, PatientsLikeMe sent a cease-and-desist letter to Nielsen. Ten days later, Nielsen sent a letter agreeing to stop scraping. Nielsen says it was unable to remove the scraped data from its database, but a company spokesman later said Nielsen had found a way to quarantine the PatientsLikeMe data to prevent it from being included in its reports for clients.

PatientsLikeMe's president, Ben Heywood, disclosed the break-in to the site's 70,000 members in a blog post. He also reminded users that PatientsLikeMe also sells its data in an anonymous form, without attaching user's names to it. That sparked a lively debate on the site about the propriety of selling sensitive information. The company says most of the 350 responses to the blog post were supportive. But it says a total of 218 members quit.

In total, PatientsLikeMe estimates that the scraper obtained about 5% of the messages in the site's forums, primarily in "Mood" and "Multiple Sclerosis."

Source: http://online.wsj.com/articles/SB10001424052748703358504575544381288117888

Tuesday, 11 November 2014

My Experience in Choosing a Web Scraping Service

Recently I decided to outsource a web scraping project to another company. I typed “web scraping service” in Google, chose six services from the first two search result pages and sent the project specifications to all of them to get quotes. Eventually I decided to go another way and did not order the services, but my experience may be useful for others who want to entrust web scraping jobs to third party services.

If you are interested in price comparisons only and not ready to read the whole story just scroll down.

A list of web scraping services I sent my project to:

    www.datahen.com - Canadian web scraping service with nice web design
    webdata-scraping.com - Indian service by Keval Kothari
    www.iwebscraping.com - India based web scraping company (same as www.3idatascraping.com)
    scrapinghub.com - A scraping service founded by creators of Scrapy
    web-scraper.com - Yet another web scraping service
    grepsr.com - A scraping service that we already reviewed two years ago

Sending the request


All the services except scrapinghub.com have quite simple forms for the description of the project requirements. Basically, you just need to give your contact details and a project description in any form. Some of them are pretty (like datahen.com), some of them are more ascetic (like web-scraper.com), but all of them allow you to send your requirements to developers.

Scrapinghub.com has a quite long form, but most of the fields are optional and all the questions are quite natural. If you really know what you need, then it won’t be hard to answer all of them; moreover they rather help you to describe your need in detail.

Note, that in the context of the project I didn’t make a request for a scraper itself. I asked to receive data on a weekly basis only.

Getting responses

Since I sent my request on Sunday it would have been ok not to receive responses the same day, but I got the first response in 3 hrs! It was from web-scraper.com and stated that this project will cost me $250 monthly. Simple and clear. Thank you, Thang!

Right after that, I received the second response. This time it was Keval from webdata-scraping.com. He had some questions regarding the project. Then after two days he wrote me that it would be hard to scrape some of my data with the software he uses, and that he will try to use a custom scraper. After that he disappeared… ((

Then on Monday I received Cost & ETAT details from datahen.com. It looked quite professional and contained not only price, but also time estimation. They were ready to create such a scraper in 3-4 days for $249 and then maintain it for just $65/month.

On the same day I received a quote from iwebscraping.com. It was $60 per week. Everything is fine, but I’d like to mention that it wasn’t the last letter from them. After I replied to them (right after receiving the quote), I received a reminder letter from them every other day for about a week. So be ready for aggressive marketing if you ask them for a quote )).

Finally in two days after requesting a quote I got a response from scrapinghub.com. Paul Tremberth wrote me that they were ready to build a scraper for $1200 and then maintain it for $300/month.

It is interesting that I have never received an answer from grepsr.com! Two years ago it was the first web scraping service we faced on the web, but now they simply ignored my request! Or perhaps they didn’t receive it somehow? Anyway I had no time for investigation.

So what?

Let us put everything together. Out of six web scraping  services I received four quotes with the following prices:

Service     Setup fee     Monthly fee

web-scraper.com     -     $250
datahen.com     $249     $65
iwebscraping.com     -     $240
scrapinghub.com     $1200     $300


From this table you can see that  scrapinghub.com appears to be the most expensive service among those compared.

EDIT: These $300/month gives you as much support and development needed to fix a 5M multi-site web crawler, for example. If you need a cheaper solution you can use their Autoscraping tool, which is free, and would have costed around $2/month to crawl at my requested rates.

The average cost of monthly scraping is about $250, but from a long term perspective datahen.com may save you money due to their low monthly fee.

That’s it! If I had enough money available it would be interesting to compare all these services in operation and provide you a more complete report, but this is all I have for now.

If you have anything to share about your experience in using similar services, please contribute to this post by commenting on it below. Cheers!

Source: http://scraping.pro/choosing-web-scraping-service/