Hand Scraped Flooring: Points to Keep in Mind

The demand for hand-scraped flooring is growing. Yet, this type of flooring, in terms of appearance, isn't like any other. If you are one of the many considering it for your home, what points do you need to keep in mind as you look for the right type of hand-scraped hardwood?

First, nearly all species – domestic and exotic – are available as this distressed variety. Species from white oak to Brazilian cherry are all available with this distressed and rustic look. And, any floor of a building can have hand-scraped flooring, as both solid and engineered types are distressed. As you look at different types of hand-scraped flooring, think about where you will be installing it into your home, and plan accordingly with the right type of solid or engineered hardwood.

What's most notable about hand-scraped hardwood is its creation. All planks are distressed by hand, and as a result, no two appear similar. Multiple methods are used for distressing hardwood, including the following techniques for aging, scraping, or finishing.

Aged hardwood goes by one of two names: Time Worn Aged or Antique. Both are similar, but a lower grade is used for Antique flooring. In addition to being aged, the hardwood's distressed appearance is accented further through darker staining, highlighting the grain, or contouring.

Scraping techniques alter the texture of the hardwood, making an otherwise smooth surface rough. Wire Brushed is a term used to indicate hand-scraped flooring with removed sapwood and accented grain. Hand-sculpted, on the other hand, still has texture but is smoother than other varieties. Hardwood that is Hand Hewn and Rough Sawn has the roughest texture for hand-scraped flooring, with even saw marks visible.

Flooring that uses finish to give hardwood an aged texture is usually sold as French Bleed. Such hand-scraped flooring has deeper beveled edges, and the joints of the floor are highlighted with darker stain. Also a somewhat superficial type of hand-scraped flooring is pegged. Considered to be decorative only, pegged flooring must not be fastened directly onto a subfloor.

If you want an even less uniform appearance for your floor, consider having it custom distressed. In this case, after the unfinished hardwood is installed, a professional comes in to alter it through beating with chains, pickeling, fastening with antique nails, or bleaching. After, a finish is applied.

Also as you look at hand-scraped hardwood, think about your flooring long term. Will you want a distressed appearance a decade or more down the line? If not, plan ahead by going with flooring that can be sanded down: solid hardwood or an engineered variety with a thicker wear layer.

If, on the other hand, you plan to keep the hand-scraped flooring, think about how you will refinish it years down the line. Ideally, to keep up the distressed look without diminishing it through sanding, you will need a floor abrader to remove only the finish, or be prepared to have a professional refinish your floors.

Source:http://www.articlesbase.com/home-improvement-articles/hand-scraped-flooring-points-to-keep-in-mind-5435851.html

Web Data Scraping Services At Lowest Rate For Business Directory

We are the world's most trusted provider directory, your business data scrape, and scrape email scraping and sending the data needed. We scour the entire directory database or doctors, lawyers, brokers, financial advisers, etc. As the scraping of a particular industry category wise database scraping or data that can be adapted.

We are pioneers in the worldwide web scraping and data services. We must understand the value of our customer database, we email id with the greatest effort to collect data. We are lawyers, doctors, brokers, realtors, schools, students, universities, IT managers, pubs, bars, nightclubs, dance clubs, financial advisers, liquor stores, Face book, Twitter, pharmaceutical companies, mortgage broker scraped data, accounting firms, car dealers , artists, shop health and job portals.

Our business database development services to try and get real quality at the lowest possible industry. Example worked. We have a quick turnaround time can be a business mailing database. Our business database development services to try and get real quality at the lowest possible industry. Example worked. We have a quick turnaround time can be a business mailing database.

We are the world's most trusted provider directory, your business data scrape, and scrape email scraping and sending the data needed. We scour the entire directory database or doctors, lawyers, brokers, financial advisers, etc., as the scraping of a particular industry category wise database scraping or data that can be adapted.

We are pioneers in the worldwide web scraping and data services. We must understand the value of our customer database, we email id with the greatest effort to collect data. We are lawyers, doctors, brokers, realtors, schools, students, universities, IT managers, pubs, bars, nightclubs, dance clubs, financial advisers, liquor stores, Face book, Twitter, pharmaceutical companies, mortgage broker scraped data, accounting firms, car dealers , artists, shop health and job portals.

What a great resource for specific information or content with little success to gather and have tried to organize themselves in a folder? You no longer need to worry, and data processing services through our website search are the best solution for your problem.

We currently have an "information explosion" phase of the walk, where there is so much information and content information for an event or a small group of channels.

Order without the benefit of you and your customers a little truth to that information. You use information and material is easy to organize in a way that is needed. Something other than a small business guide, simply create a separate folder in less than an hour.

Our technology-specific Web database for you to a similar configuration and database development to use. In addition, we finished our services can help you through the data to identify the sources of information for web pages to follow. This is a cost effective way to create a database.

We offer directory database, company name, address, the state, country, phone, email and website URL to take. In recent projects we have completed. We have a quick turnaround time can be a business mailing database. Our business database development services to try and get real quality at the lowest possible industry.

Source:http://www.articlesbase.com/outsourcing-articles/web-data-scraping-services-at-lowest-rate-for-business-directory-5757029.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/

Extractions and Skin Care

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

What is an extraction?

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

Why Do It?

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

Why Not?

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

Who Decides?

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

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

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

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

Git workflow for Scrapy projects

Our customers often ask us what’s the best workflow for working with Scrapy projects. A popular approach we have seen and used in the past is to split the spiders folder (typically project/spiders) into two folders: project/spiders_prod and project/spiders_dev, and use the SPIDER_MODULES setting to control which spiders are loaded on each environment. This works reasonably well, until you have to make changes to common code used by many spiders (ie. code outside the spiders folder), for example common base spiders.

Nowadays, DVCS (in particular, git) have become more popular and people are quite used to branching, so we recommend using a simple git workflow (similar to GitHub flow) where you branch for every change you make. You keep all changes in a branch while they’re being tested and finally merge to master when they’re finished. This means that master branch is always stable and contains only “production-ready” spiders.

If you are using our Scrapy Cloud platform, you can have 2 projects (myproject-dev, myproject-prod) and use myproject-dev to test the changes in your branch.  scrapy deploy in Scrapy 0.17 now adds the branch name to the version name (when using version=GIT or version=HG), so you can see which branch you are going to run directly on the panel. This is particularly useful with large teams working on a single Scrapy project, to avoid stepping into each other when making changes to common code.

Here is a concrete example to illustrate how this workflow works:y

•    the developer decides to work on issue 123 (could be a new spider or fixes to an existing spider)
•    the developer creates a new branch to work on the issue
•    git checkout -b issue123
•    the developer finishes working on the code and deploys to the panel (this assumes scrapy.cfg is configured with a deploy target, and using version=GIT – see here for more information)
•    scrapy deploy dev
•    the developer goes into the panel and runs the spider, where he’ll see the branch name (issue123) that will be run
•    the developer checks the scraped data looks fine through the item browser in the panel
•    whenever issues are found, the developer makes more fixes (always working on the same branch) and deploys new versions
•    once all issues are fixed, the developer merges the branch and deploys to production project
•    git checkout master
•    git merge issue123
•    git pull # make sure to pull latest code before deploying
•    scrapy deploy prod

We recommend you keep your common spiders well-tested and use Spider Contracts extensively to test your final spiders. Otherwise experience tell us that base spiders end up being copied (instead of reused) out of fear of breaking old spiders that depend on them, thus turning their maintenance into a nightmare.

Source:http://blog.scrapinghub.com/2013/03/06/git-workflow-scrapy-projects/

Scrape it – Save it – Get it

I imagine I’m talking to a load of developers. Which is odd seeing as I’m not a developer. In fact, I decided to lose my coding virginity by riding the ScraperWiki digger! I’m a journalist interested in data as a beat so all I need to do is scrape. All my programming will be done on ScraperWiki, as such this is the only coding home I know. So if you’re new to ScraperWiki and want to make the site a scraping home-away-from-home, here are the basics for scraping, saving and downloading your data:

With these three simple steps you can take advantage of what ScraperWiki has to offer – writing, running and debugging code in an easy to use editor; collaborative coding with chat and user viewing functions; a dashboard with all your scrapers in one place; examples, cheat sheets and documentation; a huge range of libraries at your disposal; a datastore with API callback; and email alerts to let you know when your scrapers break.

So give it a go and let us know what you think!

Source:https://blog.scraperwiki.com/2011/04/scrape-it-save-it-get-it/

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/

What you have to know before requesting web scraping services?

Before you request web scraping services you have to know what are your needs (what data you need, structure of it and where you can find this data).

Step 1: Define what data you need?

Data needs depending on purpose, if you want to find new customers you probably need contact data from players in your industry. Also if you want to study your competitors you need to define who are they. Only after that you can select data sources (websites feeds or other electronic sources) for this extraction.

In many cases for discovering and defining data sources are used search engines like Google, Bing, Yahoo, and others.

Step 2: Structure of data

Data structure it’s directly linked to usage purpose. In many cases data structure it’s a table where a row represents an entity and a cell of this row represents a property of this entity. In other cases Data structure is a a chart or another graphic representation builder with data extracted from a web source.

Step 3: Number of data extraction

In many cases is needed one time data extraction. In other cases when you need a regular report, are needed periodically extractions.

If you have defined all of above points you are ready to request a quote and an amount estimation from this contact form.

Source: http://thewebminer.com/blog/2013/08/

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 KNN Classifier

Q1   

Suppose a data analyst working for an insurance company was asked to build a predictive model for predicting weather a customer will buy a mobile home insurance policy. S/he tried kNN classifier with different number of neighbours (k=1,2,3,4,5). S/he got the following F-scores measured on the training data: (1.0; 0.92; 0.90; 0.85; 0.82). Based on that the analyst decided to deploy kNN with k=1. Was it a good choice? How would you select an optimal number of neighbours in this case?

1 Answer

It is not a good idea to select a parameter of a prediction algorithm using the whole training set as the result will be biased towards this particular training set and has no information about generalization performance (i.e. performance towards unseen cases). You should apply a cross-validation technique e.g. 10-fold cross-validation to select the best K (i.e. K with largest F-value) within a range. This involves splitting your training data in 10 equal parts retain 9 parts for training and 1 for validation. Iterate such that each part has been left out for validation. If you take enough folds this will allow you as well to obtain statistics of the F-value and then you can test whether these values for different K values are statistically significant.

See e.g. also: http://pic.dhe.ibm.com/infocenter/spssstat/v20r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Falg_knn_training_crossvalidation.htm

The subtlety here however is that there is likely a dependency between the number of data points for prediction and the K-value. So If you apply cross-validation you use 9/10 of the training set for training...Not sure whether any research has been performed on this and how to correct for that in the final training set. Anyway most software packages just use the abovementioned techniques e.g. see SPSS in the link. A solution is to use leave-one-out cross-validation (each data samples is left out once for testing) in that case you have N-1 training samples(the original training set has N).

Source:http://stackoverflow.com/questions/21121509/data-mining-knn-classifier?rq=1

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

Is It Time to End Screen Scraping?

As the industry works to improve the way online banking information is shared with personal financial management apps, a debate is brewing over whether to end the decades-old practice of screen scraping.

Proponents of the popular method say it is a valuable supplement to direct data feeds that may be incomplete or out-of-date. But screen scraping also raises risk concerns, since like other data collection methods it requires consumers to cough up their banking credentials.

"I have not talked to a bank that hasn't confirmed it's a growing problem in their organization," said Jim Routh, the chairman of the products and services committee at Financial Services Information Sharing and Analysis Center.

Financial institutions worry that data aggregators may not take all the appropriate security precautions. According to the FS-ISAC, an industry organization, startups are entering the aggregation market without making security a higher priority.

Routh, who is Aetna's chief information security officer and a former global head of application and mobile security for JPMorgan Chase, said the upstarts do some things well, but "protecting credentials isn't necessarily high on their priorities." The problem is worsened by data aggregators that collect marketing data, such as the device a consumer is using, to understand their behaviors across channels, he said.

The FS-ISAC has proposed creating a standard application programming interface to share information from bank accounts. The API would serve as the conduit for data when consumers wish to use a web or mobile app to receive push bill reminders, to verify their bank accounts or for numerous other PFM use cases.

The proposed API would also be designed to reduce the storage of financial data. But if the industry embraces the model, it would be harder for aggregators to do screen-scraping.

For years, PFM companies have used this tool to obtain customers' banking account information. With consumers' permission, aggregators log in with the customer's user name and password to grab financial data and use it to populate the mobile or web app of the customer's choice — whether or not the bank supports the technique.

Yodlee, which works with more than 300 banks as well as startups, argues that there is a place and a need for aggregators to collect data through various techniques to provide the best customer experience.

Brian Costello, vice president of operations and security at Yodlee, said his company uses a combination of methods to gather customer account data. If it couldn't get data from a direct feed, it could also screen scrape.

If the industry moved to embracing only one data exchange method, Yodlee could be more vulnerable to the problem of receiving outdated information from the banks.

When a bank changes an annual percentage rate, if it doesn't update the data feed it sends to the aggregator right away, the PFM services that rely on that data will appear stale. (Services like Credit Karma, Mint and Wallaby, for example, rely on aggregation technology to recommend financial products to consumers according to price, among other things.)

Proper maintenance of data feeds, of course, takes time and money — resources many banks are short on. But delays could also result from the bankers' dilemma: On the one hand, they want to let customers aggregate their accounts to gather intelligence on their competitors. On the other hand, they may have reservations about their rivals collecting that same data in the battle for wallet share.

"Banks are under tremendous pressure to retain and obtain more clients," said Costello.

Screen scraping also has maintenance requirements, though. The FS-ISAC white paper draft said the approach "requires some coordination from the FI to allow what appears to be an automated attack against their application. To avoid blocking the aggregator's attempt to screen scrape the financial institution's application with this or other current security controls, a whitelist of aggregator IPs are set up and maintained by the FIs."

Like Costello, Marc West, president of digital channels at Fiserv, said a combination of data collection methods is better than a standard data exchange approach that might fail to extract the necessary information. Any data feed, said West, offers a limited set of data and information, while a scrape can enable a custom data extract.

But Aetna's Routh said moving to a real-time API model would improve a recurring issue caused by screen scraping: customer service hiccups. A consumer may call the company behind the personal financial app when a link to an account is broken. The PFM provider might tell him to call the bank, when the problem could lie with the aggregator not knowing of an update to the bank's code.

"The consumer gets in the middle of a customer service issue that is thorny at best and unsolvable at worst," Routh said. "Unfortunately that happens more frequently than anyone would like to it happen.

The new model, then, is "inevitable" in Routh's point of view because of the risk and economics involved. "This won't happen overnight," he said. "It needs some legs."

Kristin Moyer, a research vice president in industry advisory services and banking and investment services at Gartner, said she expects more banks to embrace APIs as a way to compete in a digital world.

Already financial institutions like Capital One, Agricole Bank and Fidor Bank are piloting and testing the OAuth specification, which lets banks keep ownership of the customer log-in data but requires them to make available an API. (The FS-ISAC is also promoting OAuth 2.0 as a way to strengthen aggregation security.)

"It's something we will see a lot more of in the next two to three years," said Moyer. "It's an exciting time…I think the use of APIs will enable us as an industry [to do things] that we never really imagined possible before."

LESSONS ABROAD

The move away from screen scraping has already happened in some countries that lack a data exchange standard. Regulators in Poland, for example, recently recommended the practice halt. Responding to the guidance, mBank is one of the banks that changed its aggregation roadmap.

The bank, which spun off from BRE Bank, had been piloting a PFM service with friends and family and has now suspended the pilot. It had, however, already made use of aggregation technology so consumers, who weren't customers of the bank, could get loan decisions from mBank within half an episode of "Modern Family." Indeed, the bank would screen scrape consumers' external bank accounts to make a loan decision within five to 15 minutes. Now, loan decisions have to be made at a branch or for a smaller dollar amount after a consumer sends the bank a copy of an electronic statement.

"Right now we have to put it on the shelf. We haven't killed it. We want to resurrect it," said Michal Panowicz, senior director at mBank.

Overall, he sounds calm about the setback. "This is a regulator decision," said Panowicz. "We have to respect that. …We have to live with them on good footing."

But that doesn't mean it has given up on aggregation. Payday lenders can continue to screen scrape financial data in order to make loan decisions in Poland — which makes it an uneven playing field.

"We will try to convey the logic that [screen scraping] cannot be stopped," said Panowicz.

He views it as a longer term game for something he believes is valuable to consumers. mBank like other banks wants to realize the true aggregation dream: letting customers quickly switch bank accounts and products if they wish.

"To be honest, it's the most exciting part about aggregation... to move accounts to us without spending a minute of physical labor," he said.

Source:http://www.americanbanker.com/news/technology/is-it-time-to-end-screen-scraping-1071118-1.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/

Screenscraping from Java using jsoup – effective data gathering from websites

In a recent article I discussed screenscraping in a in hindsight fairly clumsy way (http://technology.amis.nl/blog/12786/building-java-object-graph-with-tour-de-france-results-using-screen-scraping-java-util-parser-and-assorted-facilities). While preparing for a series of articles on data visualizations, I had need of statistics regarding the Olympic Games – more specifically: the overall medal count per country during the 2008 Bejing Olympic Games. This information is readily available from dozens of websites. However, I could not find one hat offered the data in easy to process XML or CSV format – all websites had human consumers in mind.

Using screenscraping – we use a programmatic facility to consume the content that is intended to be displayed on screen to human users and subsequently process that content by extracting the required data from it. Some web-pages are easier to scrape than others – this depends on the richness of the HTML (the poorer the better for scraping), the required interactivity (JavaScript, AJAX – the less the better) and the structure used to present the data (tables, frequently despised by web developers, work rather well).

I came across a tool for screenscraping from Java, called jsoup – http://jsoup.org/. It turned out to be so incredibly easy to use – that I thouht I should share it.

Getting going with jsoup is as easy as can be:

1. download jsoup-1.6.1.jar (or whatever the latest version is) from http://jsoup.org/download

2. add this jar as a dependency in your project and/or application CLASSPATH

3. make use of jsoup in the code that does the screenscraping.

A simple example of code that uses jsoup (more examples on: http://jsoup.org/cookbook/):

One of the websites offering the overall medal count is http://www.databaseolympics.com/games/gamesyear.htm?g=26. The page looks as follows:

Image

Well, more importantly, the page looks like this:

Image

This means in terms of screenscraping: I will find the medal count for each country inside a TABLE element with styleclass pt8. Each country has a TR element. Only the first TR element does not represent a country score, as it is the table header. The first TD element in the TR represents the country. The name of the country can be retrieved as the text content from the A element in the TD. The next TD elements contain the numbers of medals in Gold, Silver, Bronze and Total.

The corresponding Java code with jsoup boils down to:

public static void main(String[] args) throws IOException, SQLException, InterruptedException {

        Document doc = Jsoup.connect(OlympicMedalMirrorProcessor.baseUrl + "?g=26").get();
        String title = doc.title();
        System.out.println(title);
        Element table = doc.select("table.pt8").get(0);
        Elements trs = table.select("tr");
        Iterator trIter = trs.iterator();
        boolean firstRow = true;
        while (trIter.hasNext()) {


            Element tr = (Element)trIter.next();
            if (firstRow) {
                firstRow = false;
                continue;
            }
            Elements tds = tr.select("td");
            Iterator tdIter = tds.iterator();
            int tdCount = 1;
            String country = null;
            Integer gold = null;
            Integer silver = null;
            Integer bronze = null;
            Integer total = null;
            // process new line
            while (tdIter.hasNext()) {

                Element td = (Element)tdIter.next();
                switch (tdCount++) {
                case 1:
                    country = td.select("a").text();
                    break;
                case 2:
                    gold = Integer.parseInt(td.text());
                    break;
                case 3:
                    silver = Integer.parseInt(td.text());
                    break;
                case 4:
                    bronze = Integer.parseInt(td.text());
                    break;
                case 5:
                    total = Integer.parseInt(td.text());
                    break;
                }

            }
            System.out.println(country + ": gold " + gold + " silver " + silver + " bronze " + bronze + " total " +
                               total);
        } //table rows

Source:http://technology.amis.nl/2011/08/03/screenscraping-from-java-using-jsoup-effective-data-gathering-from-websites/

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/

A Content Marketer's Guide to Data Scraping

As digital marketers, big data should be what we use to inform a lot of the decisions we make. Using intelligence to understand what works within your industry is absolutely crucial within content campaigns, but it blows my mind to know that so many businesses aren't focusing on it.

One reason I often hear from businesses is that they don't have the budget to invest in complex and expensive tools that can feed in reams of data to them. That said, you don't always need to invest in expensive tools to gather valuable intelligence — this is where data scraping comes in.

Just so you understand, here's a very brief overview of what data scraping is from Wikipedia:

    "Data scraping is a technique in which a computer program extracts data from human-readable output coming from another program."

Essentially, it involves crawling through a web page and gathering nuggets of information that you can use for your analysis. For example, you could search through a site like Search Engine Land and scrape the author names of each of the posts that have been published, and then you could correlate this to social share data to find who the top performing authors are on that website.

Hopefully, you can start to see how this data can be valuable. What's more, it doesn't require any coding knowledge — if you're able to follow my simple instructions, you can start gathering information that will inform your content campaigns. I've recently used this research to help me get a post published on the front page of BuzzFeed, getting viewed over 100,000 times and channeling a huge amount of traffic through to my blog.

Disclaimer: One thing that I really need to stress before you read on is the fact that scraping a website may breach its terms of service. You should ensure that this isn't the case before carrying out any scraping activities. For example, Twitter completely prohibits the scraping of information on their site. This is from their Terms of Service:

    "crawling the Services is permissible if done in accordance with the provisions of the robots.txt file, however, scraping the Services without the prior consent of Twitter is expressly prohibited"

Google similarly forbids the scraping of content from their web properties:

    Google's Terms of Service do not allow the sending of automated queries of any sort to our system without express permission in advance from Google.

So be careful, kids.

Content analysis

Mastering the basics of data scraping will open up a whole new world of possibilities for content analysis. I'd advise any content marketer (or at least a member of their team) to get clued up on this.

Before I get started on the specific examples, you'll need to ensure that you have Microsoft Excel on your computer (everyone should have Excel!) and also the SEO Tools plugin for Excel (free download here). I put together a full tutorial on using the SEO tools plugin that you may also be interested in.

Alongside this, you'll want a web crawling tool like Screaming Frog's SEO Spider or Xenu Link Sleuth (both have free options). Once you've got these set up, you'll be able to do everything that I outline below.

So here are some ways in which you can use scraping to analyse content and how this can be applied into your content marketing campaigns:

1. Finding the different authors of a blog

Analysing big publications and blogs to find who the influential authors are can give you some really valuable data. Once you have a list of all the authors on a blog, you can find out which of those have created content that has performed well on social media, had a lot of engagement within the comments and also gather extra stats around their social following, etc.

I use this information on a daily basis to build relationships with influential writers and get my content placed on top tier websites. Here's how you can do it:

Step 1: Gather a list of the URLs from the domain you're analysing using Screaming Frog's SEO Spider. Simply add the root domain into Screaming Frog's interface and hit start (if you haven't used this tool before, you can check out my tutorial here).

Once the tool has finished gathering all the URLs (this can take a little while for big websites), simply export them all to an Excel spreadsheet.

Step 2: Open up Google Chrome and navigate to one of the article pages of the domain you're analysing and find where they mention the author's name (this is usually within an author bio section or underneath the post title). Once you've found this, right-click their name and select inspect element (this will bring up the Chrome developer console).

Within the developer console, the line of code associated to the author's name that you selected will be highlighted (see the below image). All you need to do now is right-click on the highlighted line of code and press Copy XPath.

For the Search Engine Land website, the following code would be copied:

//*[@id="leftCol"]/div[2]/p/span/a

This may not make any sense to you at this stage, but bear with me and you'll see how it works.

Step 3: Go back to your spreadsheet of URLs and get rid of all the extra information that Screaming Frog gives you, leaving just the list of raw URLs – add these to the first column (column A) of your worksheet.

Step 4: In cell B2, add the following formula:

=XPathOnUrl(A2,"//*[@id='leftCol']/div[2]/p/span/a")

Just to break this formula down for you, the function XPathOnUrl allows you to use the XPath code directly within (this is with the SEO Tools plugin installed; it won't work without this). The first element of the function specifies which URL we are going to scrape. In this instance I've selected cell A2, which contains a URL from the crawl I did within Screaming Frog (alternatively, you could just type the URL, making sure that you wrap it within quotation marks).

Finally, the last part of the function is our XPath code that we gathered. One thing to note is that you have to remove the quotation marks from the code and replace them with apostrophes. In this example, I'm referring to the "leftCol" section, which I've changed to ‘leftCol' — if you don't do this, Excel won't read the formula correctly.

Once you press enter, there may be a couple of seconds delay whilst the SEO Tools plugin crawls the page, then it will return a result. It's worth mentioning that within the example I've given above, we're looking for author names on article pages, so if I try to run this on a URL that isn't an article (e.g. the homepage) I will get an error.

For those interested, the XPath code itself works by starting at the top of the code of the URL specified and following the instructions outlined to find on-page elements and return results. So, for the following code:

//*[@id='leftCol']/div[2]/p/span/a

We're telling it to look for any element (//*) that has an id of leftCol (@id='leftCol') and then go down to the second div tag after this (div[2]), followed by a p tag, a span tag and finally, an a tag (/p/span/a). The result returned should be the text within this a tag.

Don't worry if you don't understand this, but if you do, it will help you to create your own XPath. For example, if you wanted to grab the output of an a tag that has rel=author attached to it (another great way of finding page authors), then you could use some XPath that looked a little something like this:

//a[@rel='author']

As a full formula within Excel it would look something like this:

=XPathOnUrl(A2,"//a[@rel='author']")

Once you've created the formula, you can drag it down and apply it to a large number of URLs all at once. This is a huge time-saver as you'd have to manually go through each website and copy/paste each author to get the same results without scraping – I don't need to explain how long this would take.

Now that I've explained the basics, I'll show you some other ways in which scraping can be used…

2. Finding extra details around page authors

So, we've found a list of author names, which is great, but to really get some more insight into the authors we will need more data. Again, this can often be scraped from the website you're analysing.

Most blogs/publications that list the names of the article author will actually have individual author pages. Again, using Search Engine Land as an example, if you click my name at the top of this post you will be taken to a page that has more details on me, including my Twitter profile, Google+ profile and LinkedIn profile. This is the kind of data that I'd want to gather because it gives me a point of contact for the author I'm looking to get in touch with.

Here's how you can do it.

Step 1: First we need to get the author profile URLs so that we can scrape the extra details off of them. To do this, you can use the same approach to find the author's name, with just a little addition to the formula:

=XPathOnUrl(A2,"//a[@rel='author']", <strong>"href"</strong>)

The addition of the "href" part of the formula will extract the output of the href attribute of the atag. In Lehman terms, it will find the hyperlink attached to the author name and return that URL as a result.

Step 2: Now that we have the author profile page URLs, you can go on and gather the social media profiles. Instead of scraping the article URLs, we'll be using the profile URLs.

So, like last time, we need to find the XPath code to gather the Twitter, Google+ and LinkedIn links. To do this, open up Google Chrome and navigate to one of the author profile pages, right-click on the Twitter link and select Inspect Element.

Once you've done this, hover over the highlighted line of code within Chrome's developer tools, right-click and select Copy XPath.

Step 3: Finally, open up your Excel spreadsheet and add in the following formula (using the XPath that you've copied over):

=XPathOnUrl(C2,"//*[@id='leftCol']/div[2]/p/a[2]", "href")

Remember that this is the code for scraping Search Engine Land, so if you're doing this on a different website, it will almost certainly be different. One important thing to highlight here is that I've selected cell C2 here, which contains the URL of the author profile page and not just the article page. As well as this, you'll notice that I've included "href" at the end because we want the actual Twitter profile URL and not just the words ‘Twitter'.

You can now repeat this same process to get the Google+ and LinkedIn profile URLs and add it to your spreadsheet. Hopefully you're starting to see the value in this, and how it can be used to gather a lot of intelligence that can be used for all kinds of online activity, not least your SEO and social media campaigns.

3. Gathering the follower counts across social networks

Now that we have the author's social media accounts, it makes sense to get their follower counts so that they can be ranked based on influence within the spreadsheet.

Here are the final XPath formulae that you can plug straight into Excel for each network to get their follower counts. All you'll need to do is replace the text INSERT SOCIAL PROFILE URL with the cell reference to the Google+/LinkedIn URL:

Google+:

=XPathOnUrl(<strong>INSERTGOOGLEPROFILEURL</strong>,"//span[@class='BOfSxb']")

LinkedIn:

=XPathOnUrl(<strong>INSERTLINKEDINURL</strong>,"//dd[@class='overview-connections']/p/strong")

4. Scraping page titles

Once you've got a list of URLs, you're going to want to get an idea of what the content is actually about. Using this quick bit of XPath against any URL will display the title of the page:

=XPathOnUrl(A2,"//title")

To be fair, if you're using the SEO Tools plugin for Excel then you can just use the built-in feature to scrape page titles, but it's always handy to know how to do it manually!

A nice extra touch for analysis is to look at the number of words used within the page titles. To do this, use the following formula:

=CountWords(A2)

From this you can get an understanding of what the optimum title length of a post within a website is. This is really handy if you're pitching an article to a specific publication. If you make the post the best possible fit for the site and back up your decisions with historical data, you stand a much better chance of success.

Taking this a step further, you can gather the social shares for each URL using the following functions:

Twitter:

=TwitterCount(<strong>INSERTURLHERE</strong>)

Facebook:

=FacebookLikes(<strong>INSERTURLHERE</strong>)

Google+:

=GooglePlusCount(<strong>INSERTURLHERE</strong>)

Note: You can also use a tool like URL Profiler to pull in this data, which is much better for large data sets. The tool also helps you to gather large chunks of data from other social networks, link data sources like Ahrefs, Majestic SEO and Moz, which is awesome.

If you want to get even more social stats then you can use the SharedCount API, and this is how you go about doing it…

Firstly, create a new column in your Excel spreadsheet and add the following formula (where A2 is the URL of the webpage you want to gather social stats for):

=CONCATENATE("http://api.sharedcount.com/?url=",A2)

You should now have a cell that contains your webpage URL prefixed with the SharedCount API URL. This is what we will use to gather social stats. Now here's the Excel formula to use for each network (where B2 is the cell that contaiins the formula above):

StumbleUpon:

=JsonPathOnUrl(B2,"StumbleUpon")

Reddit:

=JsonPathOnUrl(B2,"Reddit")

Delicious:

=JsonPathOnUrl(B2,"Delicious")

Digg:

=JsonPathOnUrl(B2,"Diggs")

Pinterest:

=JsonPathOnUrl(B2,"Pinterest")

LinkedIn:

=JsonPathOnUrl(B2,"Linkedin")

Facebook Shares:

=JsonPathOnUrl(B2,"Facebook.share_count")

Facebook Comments:

=JsonPathOnUrl(B2,"Facebook.comment_count")

Once you have this data, you can start looking much deeper into the elements of a successful post. Here's an example of a chart that I created around a large sample of articles that I analysed within Upworthy.com.

The chart looks at the average number of social shares that an article on Upworthy receives vs the number of words within its title. This is invaluable data that can be used across a whole host of different on-page elements to get the perfect article template for the site you're pitching to.

See, big data is useful!

5. Date/time the post was published

Along with analysing the details of headlines that are working within a site, you may want to look at the optimal posting times for best results. This is something that I regularly do within my blogs to ensure that I'm getting the best possible return from the time I spend writing.

Every site is different, which makes it very difficult for an automated, one-size-fits-all tool to gather this information. Some sites will have this data within the <head> section of their webpages, but others will display it directly under the article headline. Again, Search Engine Land is a perfect example of a website doing this…

So here's how you can scrape this information from the articles on Search Engine Land:

=XPathOnUrl(<strong>INSERTARTICLEURL</strong>,"//*[@class='dateline']/text()")

Now you've got the date and time of the post. You may want to trim this down and reformat it for your data analysis, but you've got it all in Excel so that should be pretty easy.

Extra reading

Data scraping is seriously powerful, and once you've had a bit of a play around with it you'll also realise that it's not that complicated. The examples that I've given are just a starting point but once you get your creative head on, you'll soon start to see the opportunities that arise from this intelligence.

Here's some extra reading that you might find useful:

    http://findmyblogway.com/scraping-communities-with-xpath/

    http://builtvisible.com/data-entry-is-a-waste-of-time/

    http://www.seotakeaways.com/data-scraping-guide-for-seo/

    http://okdork.com/2014/04/30/the-step-by-step-guide-to-10x-growth-for-any-blog/

TL;DR

    Start using actual data to inform your content campaigns instead of going on your gut feeling.

    Gather intelligence around specific domains you want to target for content placement and create the perfect post for their audience.

    Get clued up on XPath and JSON through using the SEO Tools plugin for Excel.

    Spend more time analysing what content will get you results as opposed to what sites will give you links!

    Check the website's ToS before scraping.

Source:http://moz.com/blog/a-content-marketers-guide-to-data-scraping

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/

Scraping webdata from a website that loads data in a streaming fashion

I'm trying to scrape some data off of the FEC.gov website using python for a project of mine. Normally I use python

mechanize and beautifulsoup to do the scraping.

I've been able to figure out most of the issues but can't seem to get around a problem. It seems like the data is

streamed into the table and mechanize.Browser() just stops listening.

So here's the issue: If you visit http://query.nictusa.com/cgi-bin/can_ind/2011_P80003338/1/A ... you get the first 500

contributors whose last name starts with A and have given money to candidate P80003338 ... however, if you use

browser.open() at that url all you get is the first ~5 rows.

I'm guessing its because mechanize isn't letting the page fully load before the .read() is executed. I tried putting a

time.sleep(10) between the .open() and .read() but that didn't make much difference.

And I checked, there's no javascript or AJAX in the website (or at least none are visible when you use the 'view-

source'). SO I don't think its a javascript issue.

Any thoughts or suggestions? I could use selenium or something similar but that's something that I'm trying to avoid.

-Will

2 Answers

Why not use an html parser like lxml with xpath expressions.

I tried

>>> import lxml.html as lh
>>> data = lh.parse('http://query.nictusa.com/cgi-bin/can_ind/2011_P80003338/1/A')
>>> name = data.xpath('/html/body/table[2]/tr[5]/td[1]/a/text()')
>>> name
[' AABY, TRYGVE']
>>> name = data.xpath('//table[2]/*/td[1]/a/text()')
>>> len(name)
500
>>> name[499]
' AHMED, ASHFAQ'
>>>



Similarly, you can create xpath expression of your choice to work with.


Source: http://stackoverflow.com/questions/9435512/scraping-webdata-from-a-website-that-loads-data-in-a-streaming-

fashion

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_comments.switches._enable_view_others=1&_media.modules.content_comments.switches._enable_mutecommenter=1&enable_collapsed_comment=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