Web Data Scraping Services Have Various Method Of Business

Magnetic or optical data removal or Data Scraping Services is a term that refers to the elimination of digital storage media. Data Scraping Services of the method varies, depending on medium and method used in the process.

Similarly, patents, models, business strategies and other confidential business information, including sensitive data, can be easily accessed by others if the data is not deleted.As I said in the beginning, Data Scraping Services methods vary depending on the storage medium. For each storage medium, there are a variety of Data Scraping Services techniques.

Optical media such as  that can be destroyed by the plastic granulating. This method does not extract information, but makes recovery almost impossible. However, removal of thin film that coats the top of the disk, scraping, sanding by hand or destroy physical data. In contrast, using the microwave, a less traditional technologies, stable and disk storage layer of the thin film is very effective for the most common cause sparks to load.

Typical modern magnetic media and hard drives, tape backup units of such media is possible, but in the face of such devices requires considerable financial investment in the plant. Acids, in particular, nitric acid, 50% concentration in the iron oxide layer to react with violence, it will be completely destroyed within a few minute. In some cases it may be a storage alternative for incineration. However, this may inadvertently expose caseinogens operator and may be restricted in certain countries.

Data Scraping Services, on the other hand, is defined by Wikipedia as "an automatic search for large stores of data for patterns of practice." In other words, you already know, and you learn things about it useful analysis.

Data Scraping Services is often accompanied by a lot of complex algorithms based on statistical methods. How do you see the data in the first place - is not. Data Scraping Services analysis, you only care about what is already there in many cases, a single-pass binary wipe (to write random zeroes and ones riding) will permanently deletes all data from the storage device to remove.

use of materials recovery.
It is for this reason that the technology has been left until last.
Data Scraping Services, screen scraping is not.
This is a great simplification, so I will work a bit.

Fast-forwarding to the web world today, screen scraping is the information relates to websites. This means that computer programs "crawl" or can "spider" through web sites, data retrieval. people, We deserved pages, text data Scraping Services, automated data collection, data extraction and web site even bloody website if we have a problem it presents some.

Data Scraping Services, on the other hand, is defined by Wikipedia as "an automatic search for large stores of data for patterns of practice." In other words, you already know, and you learn things about it useful analysis. Data Scraping Services is often accompanied by a lot of complex algorithms based on statistical methods. How do you see the data in the first place - is not. Data Scraping Services analysis, you only care about what is already there.

Source:http://www.articlesbase.com/outsourcing-articles/web-data-scraping-services-have-various-method-of-business-5594515.html

Scraping By

In his classic 1976 Chesapeake portrait, Beautiful Swimmers, William Warner described the scrape boat as "a workboat unlike any other I had ever seen on the Bay." Seeming half as wide as it was long, he said, it looked like a "a miniature battleship." There's a reason for that, of course. It's a classic case of form following function; the boat evolved for one purpose, to ply the Bay's grassy shallows for shedding blue crabs.

Said to "float on a heavy dew," scrape boats run from 26 to 30 feet long and 9 to 10 feet wide. The hull is a shallow-V deadrise that quickly flattens toward the stern, enabling the boat to pull its twin scrapes—rectangular steel frames, each with a trailing mesh bag—in knee-deep waters. The broad beam might sound ungainly, but the hull tapers toward the stern—betraying its sailboat origins. And it has a graceful sheer, flowing from a bow height of a few feet to little more than a foot above the water amidships.

And you want a low freeboard when you spend the whole day hoisting aboard scrapes, which weigh 50 pounds apiece, not including the load of sea grass and crabs that come in too. Low sides or not, there's a higher than average inci-dence of back problems among scrape boat crabbers. They spend long days bending in precisely the position back doctors say puts undue pressure on the lower back as they sort through rolls of grasses to pluck out the peelers and softies. And that alone may be why crab potting is now the far more common way of catching soft crabs.

Some people think that's good, assuming that dragging a scrape across the Bay's beleaguered grass flats must be destructive. But the smooth bar of the scrape, unlike a toothed dredge, doesn't uproot grasses. In fact, where scraping is traditional, the grass beds seem relatively resilient. I've often thought if Maryland and Virginia had stuck with scraping as the major legal way to soft-crab, overfishing might not have become a problem. Pots can be deployed everywhere and by the thousands, whereas scraping is limited to grass beds and to ground covered at three miles per hour; and even the sturdiest waterman can only pull two of them by hand. But peeler pots seem here to stay, and other soft crabbers have taken to using a single, large scrape operated from larger workboats by hydraulic power.

The bottom line is that these lovely, superbly functional expressions of Chesapeake crabbing culture now number only in the dozens, if you count working, wooden models. There are some fiberglass scrape boat hulls in service, and a Carolina skiff or two has been adapted for the task. They are functional, but have little art to them.

It is probably a sign of how fast scrape boats are going that the Smithsonian Institution recently took the lines off Darlene, a scraper worked by Morris Marsh of Smith Island, for its archives. You can see photos of scrape boats, and learn more about the 140-year old history of scraping, from Paula Johnson's fine book, The Workboats of Smith Island. Mr. Marsh, still going strong in his late 60s, is the scraper who took Warner out nearly 40 years ago when he was researching Beautiful Swimmers.

Indeed, scraping seems to win over those who master it. Marsh's father-in-law, Ed Harrison, scraped for almost 70 years, nearly wearing through the cross-planked bottom of his boat—from the inside—with decades of walking the planks, tending his scrapes. And an islander who scrapes with Marsh today, David Laird, says he is 71—one year younger than Scotty Boy, the scrape boat he took over from his dad in 1958. "I wouldn't even know how to crab in another boat," Laird says.

Soft crabs may well be caught—or farmed—a century from now on the Chesapeake; but no one will devise a way to take them so intimately and beautifully from the shallowest marsh edges and tiniest crevices in the shore as the scrapers do.

Source:http://www.articlesbase.com/culture-articles/scraping-by-1560919.html

Choose Mining Wear Parts Wisely

It is important to choose a reputable supplier of mining wear parts; one that has been acknowledged as a leader in mining expertise. You will want to research and seek out a company that specializes in the engineering, manufacturing, procurement and design of mining wear parts and who has access to a multitude of patterns and templates to choose from.

It is vital to find a company that invites you to put them to the test; a company that is committed to selling more than just a product, standing behind the parts that they design and manufacture with an unprecedented industry guarantee. Some companies are so confident in their products that each wear part is stamped with their logo, identifying it as a superior product.

You will also want to find a company that takes pride in establishing strong customer relationships and who employs people who are as equally committed to providing outstanding service with customer satisfaction a priority. Your research will help you find a mining wear parts company that guarantees that if they do not have the part available, that they will find it for you or are capable of custom designing products to your exact specifications.

If you stop to consider the ramifications of an equipment malfunction or breakdown on production quotas, the significance of reliable parts becomes readily apparent. The impact can be far reaching if it halts production while the necessary repairs are completed. The ugly reality is that downtime incurs financial losses.

While the cost of aftermarket replacement mining wear parts is one factor, the installation of the part is equally as important. It is vital that aftermarket parts are built to a rugged standard to endure the rigorous industrial demands placed on them. Mining wear parts are routinely subjected to high stress abrasion and impact. The fabricated parts need to have the structural strength to be wear resistant with extended usage. Hardened manganese is the preferred material of choice to impart added strength and avoid premature breakage and replacement. Using inferior quality parts may result in the necessity of replacing them prematurely if they do not withstand the wear and tear that they are subjected to daily. While a few dollars may be saved initially by purchasing inferior mining wear parts, production costs can dramatically increase if frequent breakdowns occur and manpower hours are wasted in the field. Efficient use of manpower is an important budget consideration. Reliability is an absolute necessity w
hen you have production deadlines to meet and operations can quickly grind to a standstill when production is halted.

Quality assurance management monitors the consistency of the parts, demanding that they are machined within precise measurements. In addition, they focus on striving to improve the quality of parts as new technology becomes available. Using precision made, high quality wear parts can make your business more competitive, giving you an advantage and improving your bottom line.

Source:http://ezinearticles.com/?Choose-Mining-Wear-Parts-Wisely&id=6691631

Scraping table from html web with CloudStat

You need to use the data from internet, but don’t type, you can just extract or scrape them if you know the web URL.

Thanks to XML package from R. It provides amazing readHTMLtable() function.

For a study case,

I want to scrape data:

    US Airline Customer Score.
    World Top Chess Players (Men).

A. Scraping US Airline Customer Score table from

http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines

Code:

airline = ‘http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines’

airline.table = readHTMLTable(airline, header=T, which=1,stringsAsFactors=F)

Result:

B. Scraping World Top Chess players (Men) table from http://ratings.fide.com/top.phtml?list=men

Code:

chess = ‘http://ratings.fide.com/top.phtml?list=men’

chess.table = readHTMLTable(chess, header=T, which=5,stringsAsFactors=F)

Result:

Done. You had successfully scraping data from any web page with CloudStat.

You can get the full version of this study case (code and result) at Scraping table from html web.

Then, you can analyze as usual! Great! No more retype the data. Enjoy!

Source:http://www.r-bloggers.com/scraping-table-from-html-web-with-cloudstat/

Data Extraction - A Guideline to Use Scrapping Tools Effectively

So many people around the world do not have much knowledge about these scrapping tools. In their views, mining means extracting resources from the earth. In these internet technology days, the new mined resource is data. There are so many data mining software tools are available in the internet to extract specific data from the web. Every company in the world has been dealing with tons of data, managing and converting this data into a useful form is a real hectic work for them. If this right information is not available at the right time a company will lose valuable time to making strategic decisions on this accurate information.

This type of situation will break opportunities in the present competitive market. However, in these situations, the data extraction and data mining tools will help you to take the strategic decisions in right time to reach your goals in this competitive business. There are so many advantages with these tools that you can store customer information in a sequential manner, you can know the operations of your competitors, and also you can figure out your company performance. And it is a critical job to every company to have this information at fingertips when they need this information.

To survive in this competitive business world, this data extraction and data mining are critical in operations of the company. There is a powerful tool called Website scraper used in online digital mining. With this toll, you can filter the data in internet and retrieves the information for specific needs. This scrapping tool is used in various fields and types are numerous. Research, surveillance, and the harvesting of direct marketing leads is just a few ways the website scraper assists professionals in the workplace.

Screen scrapping tool is another tool which useful to extract the data from the web. This is much helpful when you work on the internet to mine data to your local hard disks. It provides a graphical interface allowing you to designate Universal Resource Locator, data elements to be extracted, and scripting logic to traverse pages and work with mined data. You can use this tool as periodical intervals. By using this tool, you can download the database in internet to you spread sheets. The important one in scrapping tools is Data mining software, it will extract the large amount of information from the web, and it will compare that date into a useful format. This tool is used in various sectors of business, especially, for those who are creating leads, budget establishing seeing the competitors charges and analysis the trends in online. With this tool, the information is gathered and immediately uses for your business needs.

Another best scrapping tool is e mailing scrapping tool, this tool crawls the public email addresses from various web sites. You can easily from a large mailing list with this tool. You can use these mailing lists to promote your product through online and proposals sending an offer for related business and many more to do. With this toll, you can find the targeted customers towards your product or potential business parents. This will allows you to expand your business in the online market.

There are so many well established and esteemed organizations are providing these features free of cost as the trial offer to customers. If you want permanent services, you need to pay nominal fees. You can download these services from their valuable web sites also.

Source: http://ezinearticles.com/?Data-Extraction---A-Guideline-to-Use-Scrapping-Tools-Effectively&id=3600918

Online Data Entry and Data Mining Services

Data entry job involves transcribing a particular type of data into some other form. It can be either online or offline. The input data may include printed documents like Application forms, survey forms, registration forms, handwritten documents etc.

Data entry process is an inevitable part of the job to any organization. One way or other each organization demands data entry. Data entry skills vary depends upon the nature of the job requirement, in some cases data to be entered from a hard copy formats and in some other cases data to be entered directly into a web portal. Online data entry job generally requires the data to be entered in to any online data base.

For a super market, data associate might be required to enter the goods which have sold in a particular day and the new goods received in a particular day to maintain the stock well in order. Also, by doing this the concerned authorities will get an idea about the sale particulars of each commodity as they requires. In another example, an office the account executive might be required to input the day to day expenses in to the online accounting database in order to keep the account well in order.

The aim of the data mining process is to collect the information from reliable online sources as per the requirement of the customer and convert it to a structured format for the further use. The major source of data mining is any of the internet search engine like Google, Yahoo, Bing, AOL, MSN etc. Many search engines such as Google and Bing provide customized results based on the user's activity history. Based on our keyword search, the search engine lists the details of the websites from where we can gather the details as per our requirement.

Collect the data from the online sources such as Company Name, Contact Person, Profile of the Company, Contact Phone Number of Email ID Etc. are doing for the marketing activities. Once the data is gathered from the online sources into a structured format, the marketing authorities will start their marketing promotions by calling or emailing the concerned persons, which may result to create a new customer. So basically data mining is playing a vital role in today's business expansions. By outsourcing the data entry and its related works, you can save the cost that would be incurred in setting up the necessary infrastructure and employee cost.

Source:http://ezinearticles.com/?Online-Data-Entry-and-Data-Mining-Services&id=7713395

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

Microfinance Data Scraping

I went to the Datakind‘s New York Datadive last November and met the Microfinance Information Exchange (MIX), a group that ‘delivers data services, analysis, research and business information on the institutions that provide financial services to the world’s poor’. They wanted to see whether web-scraping could save them from manually gathering data. So fellow divers and I showed MIX the utility of web-scraping. Over the course of a day, about six people scraped data about microfinance institutions from a bunch of websites, saving MIX an estimated year of manual data entry.

Over the past few months, I worked further with MIX to study who has access to what sorts of financial services. DataKind just put up our blog post about the project. Read the post, or just look at the map and explore the data.

Source:https://blog.scraperwiki.com/2012/05/microfinance-data-scraping/

Content Scraping Reuses Blog Posts without Permission

What do popular blogs and websites such as Social Media Examiner, Copy Blogger, CNN.com, Mashable, and Type A Parent have in common? No, it’s not traffic and a loyal online community, each was a victim of the content scraping site “BuzzMyFx.” Although most bloggers fall victim to content scrapers at least once, the offending website was such an extreme case the backlash against it was fast and furious. Thanks to the quick action of many angry bloggers, BuzzMyFix was taken down in a matter of days.

If you’re not familiar with content scraping sites and aren’t sure why they’re bad and what you can do if you fall prey, read on. Not knowing what steps you can take to remove your content from a scraping site can mean someone else is profiting from your hard work.

What is content scraping?

Content scraping is when a blog or website pulls in other bloggers’ content without permission, in many cases passing it off as their own. Instead of stocking their sites with unique content, they steal entire blog posts. Some do leave the original authors’ bylines, but there are plenty that don’t provide attribution at all. This is not a good thing at all.

If you don’t care about someone taking your content and putting it on their blogs and websites without your permission, you should. These sites are stealing traffic, search engine rankings, and even advertising revenue from bloggers. Moreover, by ignoring scraping sites you’re giving the message that this practice is OK.

It’s not OK.

How was BuzzMyFx different?

BuzzMyFx was a little different from your usual scrapers. Bloggers didn’t just find their content had been posted on this site, they learned their entire blogs — down to the design and comments — had been cloned. Plus, any bloggers checking to see if their blogs were being cloned immediately found themselves being scraped as well. Dozens, if not hundreds of blogs were affected. However, bloggers didn’t take this incident sitting down. They spread the word and contacted the site’s host en masse. Thanks to their swift action, and the high number of complaints, the site was removed quickly.

How can I tell if my content is being scraped?

Fortunately for content creators, scrapers are a lazy bunch. Because their sites are automated, and they don’t check or read the content being pulled, they don’t take many precautions to ensure the people they scrape from don’t find their sites. In fact, they may not even care. Fortunately, this makes it easy to learn if your content is being stolen.

    Link to your own articles — When you write a blog post and link to other (of your own) blog posts within that post, it’s not only good SEO. You also will get pingbacks whenever someone else steals your content because of your interlinks. You’re alerted when someone links to your content, and when content is published with your links, you’ll get that alert.

    Google Alerts — If your name, blog’s name, or other unique keywords are set up as Google Alerts, you’ll receive an e-mail every time content is published with these keywords.

    Analytics — When people click on your links that are in scraped content, it will show up as referring traffic in your analytics program. You should always check referring traffic so you can thank the referring site owner, but also to make sure no one is stealing your content.

What steps can I take to remove my content from a scraper?

If you find your content is being stolen, know you have several options. First, you’ll need to find out who owns the scraping site. You can find this out by doing a WHOis domain lookup, which will enable you to search for the website’s details, including the name of the webmaster, contact info, and the name of the site’s host.

Keep in mind that sometimes the website’s owner will pay extra to have his or her name kept private, but you will always be able to find the name of the host. Once you have this information, you can take the necessary steps to have your content removed.

    Contact the site’s owner personally: Your first step should always be a polite request to remove your content immediately. Let the website owner know he or she is in violation of the Digital Millennium Copyright Act (DMCA), and you will take the necessary steps to report him if he doesn’t comply.

    Contact the site’s host: If you can’t find the name of the person who owns the site, or if he won’t comply with your takedown request, contact the website’s host. You’ll have to prove your content is being stolen. As the host can be held liable for allowing the content theft, it’s in their best interest to contact the website owner and request removal.

    Contact Google: You can contact Google and fill out a form to have them remove the website from their search engines.

    Spread the word: Let all your blogging friends know about content scrapers when you come across them. The more people who take action against content scrapers, the less likely they are to do it again.

Contacting the webmaster with a takedown notice doesn’t have to be an intimidating process, either. The website Plagiarism Today has a wonderful set of stock letters to use to contact webmasters, web hosts, and even Google. All you have to do is insert the necessary information.

Content scrapers and cloners may try to steal your content, but you don’t have to let them. Stand up for what’s yours.

Source: http://www.dummies.com/how-to/content/content-scraping-reuses-blog-posts-without-permiss.html

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/

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/

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

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

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/

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/

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.

cutlery

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/

'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.

Enlarge Image

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.

Enlarge Image

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

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/

Why People Hesitate To Try Data Mining

What is hindering a number of people from venturing into the promising world of data mining? Despite so much encouragement, promotions, testimonials, and evidences of the benefits of online data collection, still only a handful take the challenge and really gain the pay offs it has to offer.

It may sound unthinkable that such an opportunity for success has been neglected by many. It may also sound absurd why many well-meaning individuals are hindered from enjoying the benefits of the blessings of the 21st century.

The Causes

After considerable observation and analysis of the human psyche, one can understand the underlying reasons behind the hesitance to try the profitable data mining service. The most common reasons why people are afraid to try new technology or why they remain passive and uninvolved are: fear; lack of knowledge; and pride.

Fear. The most paralyzing of human emotions is fear. It can, to some extent, cause a person to be insane, unprofitable, sick, and lost. Although fear is a normal reaction to certain stimuli and a natural feeling experienced by humans, it must always be monitored and controlled.  Usually, people share common fears, such as: fear of change; fear of anything new; and fear of the unknown.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/people-hesitate-try-data-mining/

Why Web Scraping is Indispensable

The 21st century has opened the gates to hidden treasures and unlimited access to information globally without the constraints of time and space, through Internet technology. Along with this development comes the necessity for each business or company to get as much information as possible in order in order to thrive in the ever increasing demand for new innovations, comparisons, and trends.

Web scraping has consequently become an indispensable option to achieve all the needed data as quickly and efficiently as possible. In this view, data mining then appears to be the best and the only way to answer the present demand for updates, data, coping, foreknowledge, analysis, and evaluation. Indeed, information has inevitably become a valuable commodity and the most sought after product among online and offline entrepreneurs.

Need for Data

The increasing need for new data makes it possible for the experts to become increasingly creative in accessing information worldwide. The more knowledge one has, the better are his or her chances of growing and surviving. There seems to be no other time in the human existence where data has become so much a major source of revenue as the contemporary times.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/web-scraping-indispensable/

How can I circumvent page view limits when scraping web data using Python?

I am using Python to scrape US postal code population data from http:/www.city-data.com, through this directory: http://www.city-data.com/zipDir.html. The specific pages I am trying to scrape are individual postal code pages with URLs like this: http://www.city-data.com/zips/01001.html. All of the individual zip code pages I need to access have this same URL Format, so my script simply does the following for postal_code in range:

    Creates URL given postal code
    Tries to get response from URL
    If (2), Check the HTTP of that URL
    If HTTP is 200, retrieves the HTML and scrapes the data into a list
    If HTTP is not 200, pass and count error (not a valid postal code/URL)
    If no response from URL because of error, pass that postal code and count error
    At end of script, print counter variables and timestamp

The problem is that I run the script and it works fine for ~500 postal codes, then suddenly stops working and returns repeated timeout errors. My suspicion is that the site's server is limiting the page views coming from my IP address, preventing me from completing the amount of scraping that I need to do (all 100,000 potential postal codes).

My question is as follows: Is there a way to confuse the site's server, for example using a proxy of some kind, so that it will not limit my page views and I can scrape all of the data I need?

Thanks for the help! Here is the code:

##POSTAL CODE POPULATION SCRAPER##

import requests

import re

import datetime

def zip_population_scrape():

    """
    This script will scrape population data for postal codes in range
    from city-data.com.
    """
    postal_code_data = [['zip','population']] #list for storing scraped data

    #Counters for keeping track:
    total_scraped = 0
    total_invalid = 0
    errors = 0


    for postal_code in range(1001,5000):

        #This if statement is necessary because the postal code can't start
        #with 0 in order for the for statement to interate successfully
        if postal_code <10000:
            postal_code_string = str(0)+str(postal_code)
        else:
            postal_code_string = str(postal_code)

        #all postal code URLs have the same format on this site
        url = 'http://www.city-data.com/zips/' + postal_code_string + '.html'

        #try to get current URL
        try:
            response = requests.get(url, timeout = 5)
            http = response.status_code

            #print current for logging purposes
            print url +" - HTTP:  " + str(http)

            #if valid webpage:
            if http == 200:

                #save html as text
                html = response.text

                #extra print statement for status updates
                print "HTML ready"

                #try to find two substrings in HTML text
                #add the substring in between them to list w/ postal code
                try:           

                    found = re.search('population in 2011:</b> (.*)<br>', html).group(1)

                    #add to # scraped counter
                    total_scraped +=1

                    postal_code_data.append([postal_code_string,found])

                    #print statement for logging
                    print postal_code_string + ": " + str(found) + ". Data scrape successful. " + str(total_scraped) + " total zips scraped."
                #if substrings not found, try searching for others
                #and doing the same as above   
                except AttributeError:
                    found = re.search('population in 2010:</b> (.*)<br>', html).group(1)

                    total_scraped +=1

                    postal_code_data.append([postal_code_string,found])
                    print postal_code_string + ": " + str(found) + ". Data scrape successful. " + str(total_scraped) + " total zips scraped."

            #if http =404, zip is not valid. Add to counter and print log        
            elif http == 404:
                total_invalid +=1

                print postal_code_string + ": Not a valid zip code. " + str(total_invalid) + " total invalid zips."

            #other http codes: add to error counter and print log
            else:
                errors +=1

                print postal_code_string + ": HTTP Code Error. " + str(errors) + " total errors."

        #if get url fails by connnection error, add to error count & pass
        except requests.exceptions.ConnectionError:
            errors +=1
            print postal_code_string + ": Connection Error. " + str(errors) + " total errors."
            pass

        #if get url fails by timeout error, add to error count & pass
        except requests.exceptions.Timeout:
            errors +=1
            print postal_code_string + ": Timeout Error. " + str(errors) + " total errors."
            pass


    #print final log/counter data, along with timestamp finished
    now= datetime.datetime.now()
    print now.strftime("%Y-%m-%d %H:%M")
    print str(total_scraped) + " total zips scraped."
    print str(total_invalid) + " total unavailable zips."
    print str(errors) + " total errors."



Source: http://stackoverflow.com/questions/25452798/how-can-i-circumvent-page-view-limits-when-scraping-web-data-using-python

Web data scraping (online news comments) with Scrapy (Python)

Since you seem like the try-first ask-question later type (that's a very good thing), I won't give you an answer, but a

(very detailed) guide on how to find the answer.

The thing is, unless you are a yahoo developer, you probably don't have access to the source code you're trying to

scrape. That is to say, you don't know exactly how the site is built and how your requests to it as a user are being

processed on the server-side. You can, however, investigate the client-side and try to emulate it. I like using Chrome

Developer Tools for this, but you can use others such as FF firebug.

So first off we need to figure out what's going on. So the way it works, is you click on the 'show comments' it loads

the first ten, then you need to keep clicking for the next ten comments each time. Notice, however, that all this

clicking isn't taking you to a different link, but lively fetches the comments, which is a very neat UI but for our

case requires a bit more work. I can tell two things right away:

    They're using javascript to load the comments (because I'm staying on the same page).
    They load them dynamically with AJAX calls each time you click (meaning instead of loading the comments with the

page and just showing them to you, with each click it does another request to the database).

Now let's right-click and inspect element on that button. It's actually just a simple span with text:

<span>View Comments (2077)</span>

By looking at that we still don't know how that's generated or what it does when clicked. Fine. Now, keeping the

devtools window open, let's click on it. This opened up the first ten. But in fact, a request was being made for us to

fetch them. A request that chrome devtools recorded. We look in the network tab of the devtools and see a lot of

confusing data. Wait, here's one that makes sense:

http://news.yahoo.com/_xhr/contentcomments/get_comments/?content_id=42f7f6e0-7bae-33d3-aa1d-

3dfc7fb5cdfc&_device=full&count=10&sortBy=highestRated&isNext=true&offset=20&pageNumber=2&_media.modules.content_commen

ts.switches._enable_view_others=1&_media.modules.content_comments.switches._enable_mutecommenter=1&enable_collapsed_com

ment=1

See? _xhr and then get_comments. That makes a lot of sense. Going to that link in the browser gave me a JSON object

(looks like a python dictionary) containing all the ten comments which that request fetched. Now that's the request you

need to emulate, because that's the one that gives you what you want. First let's translate this to some normal reqest

that a human can read:

go to this url: http://news.yahoo.com/_xhr/contentcomments/get_comments/
include these parameters: {'_device': 'full',
          '_media.modules.content_comments.switches._enable_mutecommenter': '1',
          '_media.modules.content_comments.switches._enable_view_others': '1',
          'content_id': '42f7f6e0-7bae-33d3-aa1d-3dfc7fb5cdfc',
          'count': '10',
          'enable_collapsed_comment': '1',
          'isNext': 'true',
          'offset': '20',
          'pageNumber': '2',
          'sortBy': 'highestRated'}

Now it's just a matter of trial-and-error. However, a few things to note here:

    Obviously the count is what decides how many comments you're getting. I tried changing it to 100 to see what

happens and got a bad request. And it was nice enough to tell me why - "Offset should be multiple of total rows". So

now we understand how to use offset

    The content_id is probably something that identifies the article you are reading. Meaning you need to fetch that

from the original page somehow. Try digging around a little, you'll find it.

    Also, you obviously don't want to fetch 10 comments at a time, so it's probably a good idea to find a way to fetch

the number of total comments somehow (either find out how the page gets it, or just fetch it from within the article

itself)

    Using the devtools you have access to all client-side scripts. So by digging you can find that that link to

/get_comments/ is kept within a javascript object named YUI. You can then try to understand how it is making the

request, and try to emulate that (though you can probably figure it out yourself)

    You might need to overcome some security measures. For example, you might need a session-key from the original

article before you can access the comments. This is used to prevent direct access to some parts of the sites. I won't

trouble you with the details, because it doesn't seem like a problem in this case, but you do need to be aware of it in

case it shows up.

    Finally, you'll have to parse the JSON object (python has excellent built-in tools for that) and then parse the

html comments you are getting (for which you might want to check out BeautifulSoup).

As you can see, this will require some work, but despite all I've written, it's not an extremely complicated task

either.

So don't panic.

It's just a matter of digging and digging until you find gold (also, having some basic WEB knowledge doesn't hurt).

Then, if you face a roadblock and really can't go any further, come back here to SO, and ask again. Someone will help

you.


Source: http://stackoverflow.com/questions/20218855/web-data-scraping-online-news-comments-with-scrapy-python