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

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.

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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

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

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/

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

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

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

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/

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/

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/

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/