Data Extraction, Web Screen Scraping Tool, Mozenda Scraper

Web Scraping

Web scraping, also known as Web data extraction or Web harvesting, is a software method of extracting data from websites. Web scraping is closely related and similar to Web indexing, which indexes Web content. Web indexing is the method used by most search engines. The difference with Web scraping is that it focuses more on the translation of unstructured content on the Web, characteristically in rich text format like that of HTML, into controlled data that can be analyzed stored and in a spreadsheet or database. Web scraping also makes Web browsing more efficient and productive for users. For example, Web scraping automates weather data monitoring, online price comparison, and website change recognition and data integration. 

This clever method that uses specially coded software programs is also used by public agencies. Government operations and Law enforcement authorities use data scrape methods to develop information files useful against crime and evaluation of criminal behaviors. Medical industry researchers get the benefit and use of Web scraping to gather up data and analyze statistics concerning diseases such as AIDS and the most recent strain of influenza like the recent swine flu H1N1 epidemic.

Data scraping is an automatic task performed by a software program that extracts data output from another program, one that is more individual friendly. Data scraping is a helpful device for programmers who have to generate a line through a legacy system when it is no longer reachable with up to date hardware. The data generated with the use of data scraping takes information from something that was planned for use by an end user.

One of the top providers of Web Scraping software, Mozenda, is a Software as a Service company that provides many kinds of users the ability to affordably and simply extract and administer web data. Using Mozenda, individuals will be able to set up agents that regularly extract data then store this data and finally publish the data to numerous locations. Once data is in the Mozenda system, individuals may format and repurpose data and use it in other applications or just use it as intelligence. All data in the Mozenda system is safe and sound and is hosted in a class A data warehouses and may be accessed by users over the internet safely through the Mozenda Web Console.

One other comparative software is called the Djuggler. The Djuggler is used for creating web scrapers and harvesting competitive intelligence and marketing data sought out on the web. With Dijuggles, scripts from a Web scraper may be stored in a format ready for quick use. The adaptable actions supported by the Djuggler software allows for data extraction from all kinds of webpages including dynamic AJAX, pages tucked behind a login, complicated unstructured HTML pages, and much more. This software can also export the information to a variety of formats including Excel and other database programs.

Web scraping software is a ground-breaking device that makes gathering a large amount of information fairly trouble free. The program has many implications for any person or companies who have the need to search for comparable information from a variety of places on the web and place the data into a usable context. This method of finding widespread data in a short amount of time is relatively easy and very cost effective. Web scraping software is used every day for business applications, in the medical industry, for meteorology purposes, law enforcement, and government agencies.

Source:http://www.articlesbase.com/databases-articles/data-extraction-web-screen-scraping-tool-mozenda-scraper-3568330.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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Follow Some Tips For Data Scraping Services

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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

Data Mining Explained

Overview

Data mining is the crucial process of extracting implicit and possibly useful information from data. It uses analytical and visualization techniques to explore and present information in a format which is easily understandable by humans.

Data mining is widely used in a variety of profiling practices, such as fraud detection, marketing research, surveys and scientific discovery.

In this article I will briefly explain some of the fundamentals and its applications in the real world.

Herein I will not discuss related processes of any sorts, including Data Extraction and Data Structuring.

The Effort

Data Mining has found its application in various fields such as financial institutions, health-care & bio-informatics, business intelligence, social networks data research and many more.

Businesses use it to understand consumer behavior, analyze buying patterns of clients and expand its marketing efforts. Banks and financial institutions use it to detect credit card frauds by recognizing the patterns involved in fake transactions.

The Knack

There is definitely a knack to Data Mining, as there is with any other field of web research activities. That is why it is referred as a craft rather than a science. A craft is the skilled practicing of an occupation.

One point I would like to make here is that data mining solutions offers an analytical perspective into the performance of a company depending on the historical data but one need to consider unknown external events and deceitful activities. On the flip side it is more critical especially for Regulatory bodies to forecast such activities in advance and take necessary measures to prevent such events in future.

In Closing

There are many important niches of Web Data Research that this article has not covered. But I hope that this article will provide you a stage to drill down further into this subject, if you want to do so!

Should you have any queries, please feel free to mail me. I would be pleased to answer each of your queries in detail.

Source: http://ezinearticles.com/?Data-Mining-Explained&id=4341782

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/

Affordable Tooth Extractions

In recent times, the cost of dental care has skyrocketed. This includes all types of dentistry including teeth cleaning, extractions, and dental surgery. For those who live in Denver, CO, there are many options to choose from when paying for routine or emergency dental care. In fact, having a tooth extraction Denver might just be more easily afforded than what some may be aware of.

The flat fee for a tooth extraction in Denver may vary between dental offices. The type of extraction can also cause a difference in the price. A simple extraction may cost between $60-$75, but a wisdom tooth extraction that requires more time and effort could cost much more.

One of the great aspects of having dental services performed in Denver is the variety of payment forms that many dental offices accept. Most dental offices in this area accept several different health insurance plans that will allow patients to only be required to pay a small copay at the time of service. If you have chosen an in-network dental provider for your plan, this copay can be even less.

Many dental offices also provide services to those who have state medicaid or medicare as well. While cosmetic dental work may not be covered by these forms of health care, extractions are covered because they are considered a necessary part of the patients good health. Yearly checkups and teeth cleanings are also normally covered as a preventative measure to avoid bad dental health.

For those who may not have any type of health insurance, dental insurance, or state provided health care plan, most dental offices will offer a payment plan. The total cost will be calculated and can be divided up over a few months to make dental care more easily affordable. This will need to be arranged before services and you may need to pay a percentage of the cost upfront before any dental work is performed.

So, if you live in the Denver area and need to have a tooth extraction or other dental care, do not fear that it is impossible to obtain. By calling each dental office and discussing the types of payment forms they accept, you may find a payment plan that fits your budget nicely. You can compare the prices and options of all dentists in your area so that you can make a well informed decision more easily.

Source:http://ezinearticles.com/?Affordable-Tooth-Extractions&id=3241427

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

Do blog scraping sites violate the blog owner's copyright?

I noticed that my blog has been posted on one of these website scraping sites. This is the kind of site that has no original content, but just repeats or scrapes content others have written and does it to get some small amount of ad income from ads on the scraping site. In essence the scraping site is taking advantage of the content of the originating site in order to make a few dollars from people who go to the site looking for something else. Some of these websites prey on misspelling. If you accidentally misspell the name of an original site, you just may end up with one of these patently commercial scraping sites.

Google defines scraping as follows:

•    Sites that copy and republish content from other sites without adding any original content or value
•    Sites that copy content from other sites, modify it slightly (for example, by substituting synonyms or using automated techniques), and republish it
•    Sites that reproduce content feeds from other sites without providing some type of unique organization or benefit to the user

My question, as set out in the title to this post, is whether or not scraping is a violation of copyright. It turns out that the answer is likely very complicated.  You have to look at the definition of a scraping site very carefully. Let me give you some hypotheticals to show what I mean.

Let's suppose that I write a blog and put a link in my blog post to your blog. Does that link violate your copyright? I can't imagine that anyone would think that there was problem with linking to another website on the Web. In this case, there is no content from the originating site, just a link.

But let's carry the hypothetical a little further. What if I put a link to your site and quote some of your content? Does this violate copyright law? If you are acquainted with any of the terminology of copyright law; think fair use. The issue here is whether or not the "quoted" material is a substantial reproduction of the entire original content? I would have the opinion that duplicating an entire blog post either with or without attribution would be a violation of the originator's copyright.

So is the scraping website protected by the "fair use" doctrine? Does the fact that the motivation for listing the original websites is to make money have anything to do with how you would decide if there was or was not a violation of the originator's copyright? By the way, the copyright does not make a distinction between a commercial and non-commercial use of the original constituting or not constituting a violation of copyright. The fact that the reproducing (scraping) party does not make money from the reproduction is not a factor in the issue of violation, although it may ultimately be an issue as to the amount of damages assessed.

Does the fact that the actions of the scraper annoy me, make any difference? I would answer, not in the least. Whether or not you are annoyed by the violation of the copyright makes no difference as to whether or not there is a violation. Likewise, you have no independent claims for your wounded feelings because of the copied content. Copyright is a statutory action (i.e. based on statutory law) and unless the cause of action is recognized by the law, there is no cause of action. Now, in an outrageous case, you may have  some kind of tort (personal injury) claim, but that is way outside of my hypothetical situation.

So what is the answer? Does scraping violate the originator's copyright? If only a small portion of the blog is copied (scraped) then I would have to have the opinion that it is not. Essentially, no matter what the motivation of the scrapper, there is not enough content copied to violate the fair use doctrine. Now, that is my opinion. Your's might differ. That is what makes lawsuits.

Do I think there are other reasons why scraping websites are objectionable? Certainly, but those reasons have nothing to do with copyright and they are probably the subject of another different blog post. So, if you are reading this from scraping website, bear in mind that there may be a serious problem with that type of website.

Source:http://genealogysstar.blogspot.in/2013/05/do-blog-scraping-sites-violate-blog.html

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

A quick guide on web scraping: Why and how

Web scraping, which is the collection and cleaning of online data, is the first step in any
data-driven project. Here’s a short video that explains what scraping is, and how to create
automated scraping jobs using a digital tool.

This is a 15-minute video created by an instructor at Ohio State University. In the first six
minutes, the instructor talks about why we need web scraping; he then shows how to use a
scraping tool, OutWit Hub, to collect data scattered in a large database.

FYI: read reviews by Reporters’ Lab of OutWit Hub and other web scraping tools.

Source: http://www.mulinblog.com/quick-guide-web-scraping/

Finding & Removing Spam Blogs Who Scrape Content Onto Free Hosted Blogs

The more popular you become in the blogging world, the more crap you have to deal with!
Content scraping is one chore that can be dealt with swiftly once you understand what to do.
This post contains links which you can use to quickly and easily report content scrapers and spam blogs.
Please share this post and help clean up spam blogs and punish content scrapers.
First step is to find your url’s which have been scraped of content and then get the scrapers spam blog removed.

Some of the tools i use to do this are:

    Google Webmaster Tools
    Google Alerts
Finding Scraped Content
Login to your Google Webmaster Tools account and go to traffic > links to your site.
You should see something like this:
Webmaster Tools Links to Your Site

The first domain is a site which has copied and embedded my homepage which i have already dealt with.
The second site is a search engine.
The third domain is the one i want to deal with.

A common method scrapers use is to post the scraped content from your rss feed on to a free hosted blog like WordPress.com or blogger.com.

Once you click the WordPress.com link in webmaster tools, you’ll find all the url’s which have been scraped.
Links to Your Site

There’s 32 url’s which have been linked to so its simply a matter of clicking each of your links and finding the culprits.

The first link is my homepage which has been linked to by legit domains like WordPress developers.
The others are mainly linked to by spam blogs who have scraped the content and used a free hosted service which in this case is WordPress.com.
WordPress.com Links to Your Site
 Reporting & Removing Spam Blogs

Once you have the url’s of the content scraping blogs as seen in the screenshot above:

    Fill in this basic form to report spam to WordPress.com
    Fill in this form to report copyright content to WordPress.com
    Use this form to report Blogspot and Blogger.com content which has been scraped.
    Fill in one of these forms to remove content from Google

Google Alerts

Its very easy to setup a Google alert to find your post titles when they get scraped.
If you’ve setup the WordPress SEO plugin correctly, you should have included your site title at the end of all your post titles.
Then all you need to do is setup a Google alert for your site title and you’ll be notified every time a scraper links to your content.

Link Notifications

You may also receive a pingback or trackback if you have this feature enabled in your discussion settings.

Link Notifications
RSS Feed Links

Most content scrapers use automated software to scrape the content from RSS feeds.
Make sure you configure your Reading settings so only a summary is displayed.
Reading Settings Feed Summary

Next step is to configure the settings in Yoast’s SEO plugin so links back to your site are included in all RSS feed post summaries.

RSS Feed Links

This will help search engines identify you and your domain as the original author of the content.
There’s other services like copyscape and dmca which can help you protect your sites content if you’re prepared to pay a premium.
That’s it folks.
Its easy to find and get spam sites removed once you know what to do.
Hope you don’t have to deal with this garbage to often.
Ever found out your content has been scraped?
What did you do about it?

Source: http://wpsites.net/blogging/content-scraping-monitoring-and-prevention-tips/

Web Scraping’s 2013 Review – part 1

Here we are, almost having ended another year and having the chance to analyze the aspects of the Web scraping market over the last twelve months. First of all i want to underline all the buzzwords on the tech field as published in the Yahoo’s year in review article . According to Yahoo, the most searched items wore

•     iPhone (including 4, 5, 5s, 5c, and 6)
•     Samsung (including Galaxy, S4, S3, Note)
•     Siri
•     iPad Cases
•     Snapchat
•     Google Glass
•     Apple iPad
•     BlackBerry Z10
•     Cloud Computing

It’s easy to see that none of this terms regards in any way with the field of data mining, and they rather focus on the gadgets and apps industry, which is just one of the ways technology can evolve to. Regarding actual data mining industry there were a lot of talks about it in this year’s MIT’s Engaging Data 2013 Conference. One of the speakers Noam Chomsky gave an acid speech relating data extraction and its connection to the Big Data phenomena that is also on everyone’s lips this year. He defined a good way to see if Big Data works by following a series of few simple factors: 1. It’s the analysis, not the raw data, that counts. 2. A picture is worth a thousand words 3. Make a big data portal (Not sure if Facebook is planning on dominating in cloud services some day) 4. Use a hybrid organizational model (We’re asleep already, soon)  let’s move 5. Train employees Other interesting declaration  was given by EETimes saying, “Data science will do more for medicine in the next 10 years than biological science.” which says a lot about the volume of required extracted data.

Because we want to cover as many as possible events about data mining this article will be a two parter, so don’t forget to check our blog tomorrow when the second part of this article will come up!

Source:http://thewebminer.com/blog/2013/12/