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 Have Various Method Of Business

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

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

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

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

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

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

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

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

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

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

Scraping By

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

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

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

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

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

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

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

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

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

Choose Mining Wear Parts Wisely

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

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

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

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

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

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

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

Basic Information About Tooth Extraction Cost

In order to maintain the good health of teeth, one must be devoted and must take proper care of one's teeth. Dentists play a huge role in this regard and their support is important in making people aware of their oral conditions, so that they receive the necessary health services concerning the problems of the mouth.

The flat fee of teeth-extraction varies from place to place. Nonetheless, there are still some average figures that people can refer to. Simple extraction of teeth might cause around 75 pounds, but if people need to remove the wisdom teeth, the extraction cost would be higher owing to the complexity of extraction involved.

There are many ways people can adopt in order to reduce the cost of extraction of tooth. For instance, they can purchase the insurance plans covering medical issues beforehand. When conditions arise that might require extraction, these insurance claims can take care of the costs involved.

Some of the dental clinics in the country are under the network of Medicare system. Therefore, it is possible for patients to make claims for these plans to reduce the amount of money expended in this field. People are not allowed to make insurance claims while they undergo cosmetic dental care like diamond implants, but extraction of teeth is always regarded as a necessity for patients; so most of the claims that are made in this front are settled easily.

It is still possible for them to pay less at the moment of the treatment, even if they have not opted for dental insurance policies. Some of the clinics offer plans which would allow patients to pay the tooth extraction cost in the form of installments. This is one of the better ways that people can consider if they are unable to pay the entire cost of tooth extraction immediately.

In fact, the cost of extracting one tooth is not very high and it is affordable to most people. Of course, if there are many other oral problems that you encounter, the extraction cost would be higher. Dentists would also consider the other problems you have and charge you additional fees accordingly. Not brushing the teeth regularly might aid in the development of plaque and this can make the cost of tooth extraction higher.

Maintaining a good oral health is important and it reflects the overall health of an individual.

To conclude, you need to know the information about cost of extraction so you can get the right service and must also follow certain easy practices to reduce the tooth extraction cost.

Source:http://ezinearticles.com/?Basic-Information-About-Tooth-Extraction-Cost&id=6623204

Data Mining - Techniques and Process of Data Mining

Data mining as the name suggest is extracting informative data from a huge source of information. It is like segregating a drop from the ocean. Here a drop is the most important information essential for your business, and the ocean is the huge database built up by you.

Recognized in Business

Businesses have become too creative, by coming up with new patterns and trends and of behavior through data mining techniques or automated statistical analysis. Once the desired information is found from the huge database it could be used for various applications. If you want to get involved into other functions of your business you should take help of professional data mining services available in the industry

Data Collection

Data collection is the first step required towards a constructive data-mining program. Almost all businesses require collecting data. It is the process of finding important data essential for your business, filtering and preparing it for a data mining outsourcing process. For those who are already have experience to track customer data in a database management system, have probably achieved their destination.

Algorithm selection

You may select one or more data mining algorithms to resolve your problem. You already have database. You may experiment using several techniques. Your selection of algorithm depends upon the problem that you are want to resolve, the data collected, as well as the tools you possess.

Regression Technique

The most well-know and the oldest statistical technique utilized for data mining is regression. Using a numerical dataset, it then further develops a mathematical formula applicable to the data. Here taking your new data use it into existing mathematical formula developed by you and you will get a prediction of future behavior. Now knowing the use is not enough. You will have to learn about its limitations associated with it. This technique works best with continuous quantitative data as age, speed or weight. While working on categorical data as gender, name or color, where order is not significant it better to use another suitable technique.

Classification Technique

There is another technique, called classification analysis technique which is suitable for both, categorical data as well as a mix of categorical and numeric data. Compared to regression technique, classification technique can process a broader range of data, and therefore is popular. Here one can easily interpret output. Here you will get a decision tree requiring a series of binary decisions.

Our best wishes are with you for your endeavors.

Source: http://ezinearticles.com/?Data-Mining---Techniques-and-Process-of-Data-Mining&id=5302867

Scraping bids out for SS United States

Yesterday we posted that the Independence Seaport Museum doesn’t have the money to support the upkeep of the USS Olympia nor does it have the money to dredge the channel to tow her away.  On the other side of the river the USS New Jersey Battleship Museum is also having financial troubles. Given the current troubles centered around the Delaware River it almost seems a shame to report that the SS United States, which has been sitting of at Pier 84 in South Philadelphia for the last fourteen years,  is now being inspected by scrap dealers.  Then again, she is a rusting, gutted shell.  Perhaps it is time to let the old lady go.    As reported in Maritime Matters:

SS UNITED STATES For Scrap?

An urgent message was sent out today to the SS United States Conservancy alerting members that the fabled liner, currently laid up at Philadelphia, is being inspected by scrap merchants.

“Dear SS United States Conservancy Members and Supporters:

The SS United States Conservancy has learned that America’s national flagship, the SS United States, may soon be destroyed. The ship’s current owners, Genting Hong Kong (formerly Star Cruises Limited), through its subsidiary, Norwegian Cruise Line (NCL), are currently collecting bids from scrappers.

The ship’s current owners listed the vessel for sale in February, 2009. While NCL graciously offered the Conservancy first right of refusal on the vessel’s sale, the Conservancy has not been in a financial position to purchase the ship outright. However, the Conservancy has been working diligently to lay the groundwork for a public-private partnership to save and sustain the ship for generations to come.

Source:http://www.oldsaltblog.com/2010/03/scraping-bids-out-for-ss-united-states/

ScraperWiki: A story about two boys, web scraping and a worm

“It’s like a buddy movie.” she said.
Not quite the kind of story lead I’m used to. But what do you expect if you employ journalists in a tech startup?
“Tell them about that computer game of his that you bought with your pocket money.”
She means the one with the risqué name.
I think I’d rather tell you about screen scraping, and why it is fundamental to the nature of data.

About how Julian spent almost a decade scraping himself to death until deciding to step back out and build a tool to make it easier.

I’ll give one example.
Two boys
In 2003, Julian wanted to know how his MP had voted on the Iraq war.
The lists of votes were there, on the www.parliament.uk website. But buried behind dozens of mouse clicks.
Julian and I wrote some software to read the pages for us, and created what eventually became TheyWorkForYou.

We could slice and dice the votes, mix them with some knowledge from political anaroks, and create simple sentences. Mini computer generated stories.

“Louise Ellman voted very strongly for the Iraq war.”
You can see it, and other stories, there now. Try the postcode of the ScraperWiki office, L3 5RF.

I remember the first lobbiest I showed it to. She couldn’t believe it. Decades of work done in an instant by a computer. An encyclopedia of data there in a moment.

Web Scraping

It might seem like a trick at first, as if it was special to Parliament. But actually, everyone does this kind of thing.

Google search is just a giant screen scraper, with one secret sauce algorithm guessing its ranking data.
Facebook uses scraping as a core part of its viral growth to let users easily import their email address book.

There’s lots of messy data in the world. Talk to a geek or a tech company, and you’ll find a screen scraper somewhere.

Why is this?
It’s Tautology

On the surface, screen scrapers look just like devices to work round incomplete IT systems.

Parliament used to publish quite rough HTML, and certainly had no database of MP voting records. So yes, scrapers are partly a clever trick to get round that.

But even if Parliament had published it in a structured format, their publishing would never have been quite right for what we wanted to do.

We still would have had to write a data loader (search for ‘ETL’ to see what a big industry that is). We still would have had to refine the data, linking to other datasets we used about MPs. We still would have had to validate it, like when we found the dead MP who voted.

It would have needed quite a bit of programming, that would have looked very much like a screen scraper.

And then, of course, we still would have had to build the application, connecting the data to the code that delivered the tool that millions of wonks and citizens use every year.

Core to it all is this: When you’re reusing data for a new purpose, a purpose the original creator didn’t intend, you have to work at it.

Put like that, it’s a tautology.
A journalist doesn’t just want to know what the person who created the data wanted them to know.
Scrape Through
So when Julian asked me to be CEO of ScraperWiki, that’s what went through my head.
Secrets buried everywhere.

The same kind of benefits we found for politics in TheyWorkForYou, but scattered across a hundred countries of public data, buried in a thousand corporate intranets.

If only there was a tool for that.
A Worm
And what about my pocket money?
Nicola was talking about Fat Worm Blows a Sparky.
Julian’s boss’s wife gave it its risqué name while blowing bubbles in the bath. It was 1986. Computers were new. He was 17.

Fat Worm cost me £9.95. I was 12.
[Loading screen]
I was on at most £1 a week, so that was ten weeks of savings.
Luckily, the 3D graphics were incomprehensibly good for the mid 1980s. Wonder who the genius programmer is.
I hadn’t met him yet, but it was the start of this story.

Source:https://blog.scraperwiki.com/2011/05/scraperwiki-a-story-about-two-boys-web-scraping-and-a-worm/

Ethics in data journalism: mass data gathering – scraping, FOI and deception

Mass data gathering – scraping, FOI, deception and harm

The data journalism practice of ‘scraping’ – getting a computer to capture information from online sources – raises some ethical issues around deception and minimisation of harm. Some scrapers, for example, ‘pretend’ to be a particular web browser, or pace their scraping activity more slowly to avoid detection. But the deception is practised on another computer, not a human – so is it deception at all? And if the ‘victim’ is a computer, is there harm?

The tension here is between the ethics of virtue (“I do not deceive”) and teleological ethics (good or bad impact of actions). A scraper might include a small element of deception, but the act of scraping (as distinct from publishing the resulting information) harms no human. Most journalists can live with that.

The exception is where a scraper makes such excessive demands on a site that it impairs that site’s performance (because it is repetitively requesting so many pages in a small space of time). This not only negatively impacts on the experience of users of the site, but consequently the site’s publishers too (in many cases sites will block sources of heavy demand, breaking the scraper anyway).

Although the harm may be justified against a wider ‘public good’, it is unnecessary: a well designed scraper should not make such excessive demands, nor should it draw attention to itself by doing so. The person writing such a scraper should ensure that it does not run more often than is necessary, or that it runs more slowly to spread the demands on the site being scraped. Notably in this regard, ProPublica’s scraping project Upton “helps you be a good citizen [by avoiding] hitting the site you’re scraping with requests that are unnecessary because you’ve already downloaded a certain page” (Merrill, 2013).

Attempts to minimise that load can itself generate ethical concerns. The creator of seminal data journalism projects chicagocrime.org and Everyblock, Adrian Holovaty, addresses some of these in his series on ‘Sane data updates’ and urges being upfront about

    “which parts of the data might be out of date, how often it’s updated, which bits of the data are updated … and any other peculiarities about your process … Any application that repurposes data from another source has an obligation to explain how it gets the data … The more transparent you are about it, the better.” (Holovaty, 2013)

Publishing scraped data in full does raise legal issues around the copyright and database rights surrounding that information. The journalist should decide whether the story can be told accurately without publishing the full data.

Issues raised by scraping can also be applied to analogous methods using simple email technology, such as the mass-generation of Freedom of Information requests. Sending the same FOI request to dozens or hundreds of authorities results in a significant pressure on, and cost to, public authorities, so the public interest of the question must justify that, rather than its value as a story alone. Journalists must also check the information is not accessible through other means before embarking on a mass-email.

Source: http://onlinejournalismblog.com/2013/09/18/ethics-in-data-journalism-mass-data-gathering-scraping-foi-and-deception/

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

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

Some of the tools i use to do this are:

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

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

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

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

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

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

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

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

Google Alerts

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

Link Notifications

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

Link Notifications
RSS Feed Links

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

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

RSS Feed Links

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

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

Web Scraping’s 2013 Review – part 2

As promised we came back with the second part of this year’s web scraping review. Today we will focus not only on events of 2013 that regarded web scraping but also Big data and what this year meant for this concept.

First of all, we could not talked about the conferences in which data mining was involved without talking about TED conferences. This year the speakers focused on the power of data analysis to help medicine and to prevent possible crises in third world countries. Regarding data mining, everyone agreed that this is one of the best ways to obtain virtual data.

Also a study by MeriTalk  a government IT networking group, ordered by NetApp showed this year that companies are not prepared to receive the informational revolution. The survey found that state and local IT pros are struggling to keep up with data demands. Just 59% of state and local agencies are analyzing the data they collect and less than half are using it to make strategic decisions. State and local agencies estimate that they have just 46% of the data storage and access, 42% of the computing power, and 35% of the personnel they need to successfully leverage large data sets.

Some economists argue that it is often difficult to estimate the true value of new technologies, and that Big Data may already be delivering benefits that are uncounted in official economic statistics. Cat videos and television programs on Hulu, for example, produce pleasure for Web surfers — so shouldn’t economists find a way to value such intangible activity, whether or not it moves the needle of the gross domestic product?

We will end this article with some numbers about the sumptuous growth of data available on the internet.  There were 30 billion gigabytes of video, e-mails, Web transactions and business-to-business analytics in 2005. The total is expected to reach more than 20 times that figure in 2013, with off-the-charts increases to follow in the years ahead, according to researches conducted by Cisco, so as you can see we have good premises to believe that 2014 will be at least as good as 2013.

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

Scraping R-bloggers with Python – Part 2

In my previous post I showed how to write a small simple python script to download the pages of R-bloggers.com. If you followed that post and ran the script, you should have a folder on your hard drive with 2409 .html files labeled post1.html , post2.html and so forth. The next step is to write a small script that extract the information we want from each page, and store that information in a .csv file that is easily read by R. In this post I will show how to extract the post title, author name and date of a given post and store it in a .csv file with a unique id.

To do this open a document in your favorite python editor (I like to use aquamacs) and name it: extraction.py. As in the previous post we start by importing the modules that we will use for the extraction:

from BeautifulSoup import BeautifulSoup

import os
import re

As in the previous post we will be using the BeautifulSoup module to extract the relevant information from the pages. The os module is used to get a list of file from the directory where we have saved the .html files, and finally the re module allows us to use regular expressions to format the titles that include a comma value or a newline value (\n). We need to remove these as they would mess up the formatting of the .csv file.

After having read in the modules, we need to get a list of files that we can iterate over. First we need to specify the path were the files are saved, and then we use the os module to get all the filenames in the specified directory:

path = "/Users/thomasjensen/Documents/RBloggersScrape/download"

listing = os.listdir(path)

It might be that there are other files in the given directory, hence we apply a filter, in shape of a list comprehension, to weed out any file names that do not match our naming scheme:

listing = [name for name in listing if re.search(r"post\d+\.html",name) != None]

Notice that a regular expression was used to determine whether a given name in the list matched our naming scheme. For more on regular expressions have a look at this site.

The final steps in preparing our extraction is to change the working directory to where we have our .html files, and create an empty dictionary:

os.chdir(path)
data = {}

Dictionaries are one of the great features of Python. Essentially a dictionary is a mapping of a key to a specific value, however the fact that dictionaries can be nested within each other, allows us to create data structures similar to R’s data frames.

Now we are ready to begin extracting information from our downloaded pages. Much as in the previous post, we will loop over all the file names, read each file into Python and create a BeautifulSoup object from the file:

for page in listing:
    site = open(page,"rb")
    soup = BeautifulSoup(site)

In order to store the values we extract from a given page, we update the dictionary with a unique key for the page. Since our naming scheme made sure that each file had a unique name, we simply remove the .html part from the page name, and use that as our key:

key = re.sub(".html","",page)

data.update({key:{}})

This will create a mapping between our key and an empty dictionary, nested within the data dictionary. Once this is done we can start extract information and store it in our newly created nested dictionary. The content we want is located in the main column, which has the id tag “leftcontent” in the html code. To get at this we use find() function on soup object created above:

content = soup.find("div", id = "leftcontent")

The first “h1” tag in our content object contains the title, so again we will use the find() function on the content object, to find the first “h1” tag:

title = content.findNext("h1").text

To get the text within the “h1” tag the .text had been added to our search with in the content object.

To find the author name, we are lucky that there is a class of “div” tags called “meta” which contain a link with the author name in it. To get the author name we simply find the meta div class and search for a link. Then we pull out the text of the link tag:

author = content.find("div",{"class":"meta"}).findNext("a").text

Getting the date is a simple matter as it is nested within div tag with the class “date”:

date = content.find("div",{"class":"date"}).text

Once we have the three variables we put them in dictionaries that are nested within the nested dictionary we created with the key:

data[key]["title"] = title
data[key]["author"] = author
data[key]["date"] = date

Once we have run the loop and gone through all posts, we need to write them in the right format to a .csv file. To begin with we open a .csv file names output:

output = open("/Users/thomasjensen/Documents/RBloggersScrape/output.csv","wb")

then we create a header that contain the variable names and write it to the output.csv file as the first row:

variables = unicode(",".join(["id","date","author","title"]))
header = variables + "\n"
output.write(header.encode("utf8"))

Next we pull out all the unique keys from our dictionary that represent individual posts:

keys = data.keys()

Now it is a simple matter of looping through all the keys, pull out the information associated with each key, and write that information to the output.csv file:

for key in keys:
    print key
    id = key
    date = re.sub(",","",data[key]["date"])
    author = data[key]["author"]
    title = re.sub(",","",data[key]["title"])
    title = re.sub("\\n","",title)
    linelist = [id,date,author,title]
    linestring = unicode(",".join(linelist))
    linestring = linestring + "\n"
    output.write(linestring.encode("utf-8"))

Notice that we first create four variables that contain the id, date, author and title information. With regards to the title we use two regular expressions to remove any commas and “\n” from the title, as these would create new columns or new line breaks in the output.csv file. Finally we put the variables together in a list, and turn the list into a string with the list items separated by a comma. Then a linebreak is added to the end of the string, and the string is written to the output.csv file. As a last step we close the file connection:

output.close()

And that is it. If you followed the steps you should now have a csv file in your directory with 2409 rows, and four variables – ready to be read into R. Stay tuned for the next post which will show how we can use this data to see how R-bloggers has developed since 2005. The full extraction script is shown below:

from BeautifulSoup import BeautifulSoup

import os
import re

 path = "/Users/thomasjensen/Documents/RBloggersScrape/download"
 listing = os.listdir(path)

listing = [name for name in listing if re.search(r"post\d+\.html",name) != None]
 os.chdir(path)
 data = {}
 for page in listing:
site = open(page,"rb")
soup = BeautifulSoup(site)
key = re.sub(".html","",page)
print key
data.update({key:{}})
 content = soup.find("div", id = "leftcontent")
title = content.findNext("h1").text
author = content.find("div",{"class":"meta"}).findNext("a").text
date = content.find("div",{"class":"date"}).text
data[key]["title"] = title
data[key]["author"] = author
data[key]["date"] = date

 output = open("/Users/thomasjensen/Documents/RBloggersScrape/output.csv","wb")

 keys = data.keys()
 variables = unicode(",".join(["id","date","author","title"]))
 header = variables + "\n"
 output.write(header.encode("utf8"))
 for key in keys:
print key
id = key
date = re.sub(",","",data[key]["date"])
author = data[key]["author"]
title = re.sub(",","",data[key]["title"])
title = re.sub("\\n","",title)
linelist = [id,date,author,title]
linestring = unicode(",".join(linelist))
linestring = linestring + "\n"
output.write(linestring.encode("utf-8"))
 output.close()

Source:http://www.r-bloggers.com/scraping-r-bloggers-with-python-part-2/

Data Mining and Frequent Datasets

I've been doing some work for my exams in a few days and I'm going through some past papers but unfortunately there are no corresponding answers. I've answered the question and I was wondering if someone could tell me if I am correct.

My question is

    (c) A transactional dataset, T, is given below:
    t1: Milk, Chicken, Beer
    t2: Chicken, Cheese
    t3: Cheese, Boots
    t4: Cheese, Chicken, Beer,
    t5: Chicken, Beer, Clothes, Cheese, Milk
    t6: Clothes, Beer, Milk
    t7: Beer, Milk, Clothes

    Assume that minimum support is 0.5 (minsup = 0.5).

    (i) Find all frequent itemsets.

Here is how I worked it out:

    Item : Amount
    Milk : 4
    Chicken : 4
    Beer : 5
    Cheese : 4
    Boots : 1
    Clothes : 3

Now because the minsup is 0.5 you eliminate boots and clothes and make a combo of the remaining giving:

    {items} : Amount
    {Milk, Chicken} : 2
    {Milk, Beer} : 4
    {Milk, Cheese} : 1
    {Chicken, Beer} : 3
    {Chicken, Cheese} : 3
    {Beer, Cheese} : 2

Which leaves milk and beer as the only frequent item set then as it is the only one above the minsup?

data mining

Nanor

3 Answers

There are two ways to solve the problem:

    using Apriori algorithm
    Using FP counting

Assuming that you are using Apriori, the answer you got is correct.

The algorithm is simple:

First you count frequent 1-item sets and exclude the item-sets below minimum support.

Then count frequent 2-item sets by combining frequent items from previous iteration and exclude the item-sets below support threshold.

The algorithm can go on until no item-sets are greater than threshold.

In the problem given to you, you only get 1 set of 2 items greater than threshold so you can't move further.

There is a solved example of further steps on Wikipedia here.

You can refer "Data Mining Concepts and Techniques" by Han and Kamber for more examples.

141

There is more than two algorithms to solve this problem. I will just mention a few of them: Apriori, FPGrowth, Eclat, HMine, DCI, Relim, AIM, etc. –  Phil Mar 5 '13 at 7:18

OK to start, you must first understand, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

Now, the amount of raw data stored in corporate databases is exploding. From trillions of point-of-sale transactions and credit card purchases to pixel-by-pixel images of galaxies, databases are now measured in gigabytes and terabytes. (One terabyte = one trillion bytes. A terabyte is equivalent to about 2 million books!) For instance, every day, Wal-Mart uploads 20 million point-of-sale transactions to an A&T massively parallel system with 483 processors running a centralized database.

Raw data by itself, however, does not provide much information. In today's fiercely competitive business environment, companies need to rapidly turn these terabytes of raw data into significant insights into their customers and markets to guide their marketing, investment, and management strategies.

Now you must understand that association rule mining is an important model in data mining. Its mining algorithms discover all item associations (or rules) in the data that satisfy the user-specified minimum support (minsup) and minimum confidence (minconf) constraints. Minsup controls the minimum number of data cases that a rule must cover. Minconf controls the predictive strength of the rule.

Since only one minsup is used for the whole database, the model implicitly assumes that all items in the data are of the same nature and/or have similar frequencies in the data. This is, however, seldom the case in real- life applications. In many applications, some items appear very frequently in the data, while others rarely appear. If minsup is set too high, those rules that involve rare items will not be found. To find rules that involve both frequent and rare items, minsup has to be set very low.

This may cause combinatorial explosion because those frequent items will be associated with one another in all possible ways. This dilemma is called the rare item problem. This paper proposes a novel technique to solve this problem. The technique allows the user to specify multiple minimum supports to reflect the natures of the items and their varied frequencies in the database. In rule mining, different rules may need to satisfy different minimum supports depending on what items are in the rules.

Given a set of transactions T (the database), the problem of mining association rules is to discover all association rules that have support and confidence greater than the user-specified minimum support (called minsup) and minimum confidence (called minconf).

I hope that once you understand the very basics of data mining that the answer to this question shall become apparent.

1

The Apriori algorithm is based on the idea that for a pair o items to be frequent, each individual item should also be frequent. If the hamburguer-ketchup pair is frequent, the hamburger itself must also appear frequently in the baskets. The same can be said about the ketchup.

So for the algorithm, it is established a "threshold X" to define what is or it is not frequent. If an item appears more than X times, it is considered frequent.

The first step of the algorithm is to pass for each item in each basket, and calculate their frequency (count how many time it appears). This can be done with a hash of size N, where the position y of the hash, refers to the frequency of Y.

If item y has a frequency greater than X, it is said to be frequent.

In the second step of the algorithm, we iterate through the items again, computing the frequency of pairs in the baskets. The catch is that we compute only for items that are individually frequent. So if item y and item z are frequent on itselves, we then compute the frequency of the pair. This condition greatly reduces the pairs to compute, and the amount of memory taken.

Once this is calculated, the frequencies greater than the threshold are said frequent itemset.

Source: http://stackoverflow.com/questions/14164853/data-mining-and-frequent-datasets?rq=1

Using Kimono Labs to Scrape the Web for Free

Historically, I have written and presented about big data—using data to create insights, and how to automate your data ingestion process by connecting to APIs and leveraging advanced database technologies.

Recently I spoke at SMX West about leveraging the rich data in webmaster tools. After the panel, I was approached by the in-house SEO of a small company, who asked me how he could extract and leverage all the rich data out there without having a development team or large budget. I pointed him to the CSV exports and some of the more hidden tools to extract Google data, such as the GA Query Builder and the YouTube Analytics Query Builder.

However, what do you do if there is no API? What do you do if you want to look at unstructured data, or use a data source that does not provide an export?

For today's analytics pros, the world of scraping—or content extraction (sounds less black hat)—has evolved a lot, and there are lots of great technologies and tools out there to help solve those problems. To do so, many companies have emerged that specialize in programmatic content extraction such as Mozenda, ScraperWiki, ImprtIO, and Outwit, but for today's example I will use Kimono Labs. Kimono is simple and easy to use and offers very competitive pricing (including a very functional free version). I should also note that I have no connection to Kimono; it's simply the tool I used for this example.

Before we get into the actual "scraping" I want to briefly discuss how these tools work.

The purpose of a tool like Kimono is to take unstructured data (not organized or exportable) and convert it into a structured format. The prime example of this is any ranking tool. A ranking tool reads Google's results page, extracts the information and, based on certain rules, it creates a visual view of the data which is your ranking report.

Kimono Labs allows you to extract this data either on demand or as a scheduled job. Once you've extracted the data, it then allows you to either download it via a file or extract it via their own API. This is where Kimono really shines—it basically allows you to take any website or data source and turn it into an API or automated export.

For today's exercise I would like to create two scrapers.

A. A ranking tool that will take Google's results and store them in a data set, just like any other ranking tool. (Disclaimer: this is meant only as an example, as scraping Google's results is against Google's Terms of Service).

B. A ranking tool for Slideshare. We will simulate a Slideshare search and then extract all the results including some additional metrics. Once we have collected this data, we will look at the types of insights you are able to generate.

1. Sign up

Signup is simple; just go to http://www.kimonolabs.com/signup and complete the form. You will then be brought to a welcome page where you will be asked to drag their bookmarklet into your bookmarks bar.

The Kimonify Bookmarklet is the trigger that will start the application.

2. Building a ranking tool

Simply navigate your browser to Google and perform a search; in this example I am going to use the term "scraping." Once the results pages are displayed, press the kimonify button (in some cases you might need to search again). Once you complete your search you should see a screen like the one below:

It is basically the default results page, but on the top you should see the Kimono Tool Bar. Let's have a close look at that:

The bar is broken down into a few actions:

    URL – Is the current URL you are analyzing.

    ITEM NAME – Once you define an item to collect, you should name it.

    ITEM COUNT – This will show you the number of results in your current collection.

    NEW ITEM – Once you have completed the first item, you can click this to start to collect the next set.

    PAGINATION – You use this mode to define the pagination link.

    UNDO – I hope I don't have to explain this ;)

    EXTRACTOR VIEW – The mode you see in the screenshot above.

    MODEL VIEW – Shows you the data model (the items and the type).

    DATA VIEW – Shows you the actual data the current page would collect.

    DONE – Saves your newly created API.

After you press the bookmarklet you need to start tagging the individual elements you want to extract. You can do this simply by clicking on the desired elements on the page (if you hover over it, it changes color for collectable elements).

Kimono will then try to identify similar elements on the page; it will highlight some suggested ones and you can confirm a suggestion via the little checkmark:

A great way to make sure you have the correct elements is by looking at the count. For example, we know that Google shows 10 results per page, therefore we want to see "10" in the item count box, which indicates that we have 10 similar items marked. Now go ahead and name your new item group. Each collection of elements should have a unique name. In this page, it would be "Title".

Now it's time to confirm the data; just click on the little Data icon to see a preview of the actual data this page would collect. In the data view you can switch between different formats (JSON, CSV and RSS). If everything went well, it should look like this:

As you can see, it not only extracted the visual title but also the underlying link. Good job!

To collect some more info, click on the Extractor icon again and pick out the next element.

Now click on the Plus icon and then on the description of the first listing. Since the first listing contains site links, it is not clear to Kimono what the structure is, so we need to help it along and click on the next description as well.

As soon as you do this, Kimono will identify some other descriptions; however, our count only shows 8 instead of the 10 items that are actually on that page. As we scroll down, we see some entries with author markup; Kimono is not sure if they are part of the set, so click the little checkbox to confirm. Your count should jump to 10.

Now that you identified all 10 objects, go ahead and name that group; the process is the same as in the Title example. In order to make our Tool better than others, I would like to add one more set— the author info.

Once again, click the Plus icon to start a new collection and scroll down to click on the author name. Because this is totally unstructured, Google will make a few recommendations; in this case, we are working on the exclusion process, so press the X for everything that's not an author name. Since the word "by" is included, highlight only the name and not "by" to exclude that (keep in mind you can always undo if things get odd).

Once you've highlighted both names, results should look like the one below, with the count in the circle being 2 representing the two authors listed on this page.

Out of interest I did the same for the number of people in their Google+ circles. Once you have done that, click on the Model View button, and you should see all the fields. If you click on the Data View you should see the data set with the authors and circles.

As a final step, let's go back to the Extractor view and define the pagination; just click the Pagination button (it looks like a book) and select the next link. Once you have done that, click Done.

You will be presented with a screen similar to this one:

Here you simply name your API, define how often you want this data to be extracted and how many pages you want to crawl. All of these settings can be changed manually; I would leave it with On demand and 10 pages max to not overuse your credits.

Once you've saved your API, there are a ton of options (too many to review here). Kimono has a great learning section you can check out any time.

To collect the listings requires a quick setup. Click on the pagination tab, turn it on and set your schedule to On demand to pull data when you ask it to. Your screen should look like this:

Now press Crawl and Kimono will start collecting your data. If you see any issues, you can always click on Edit API and go back to the extraction screen.

Once the crawl is completed, go to the Test Endpoint tab to view or download your data (I prefer CSV because you can easily open it in Excel, CSV, Spotfire, etc.) A possible next step here would be doing this for multiple keywords and then analyzing the impact of, say, G+ Authority on rankings. Again, many of you might say that a ranking tool can already do this, and that's true, but I wanted to cover the basics before we dive into the next one.

3. Extracting SlideShare data

With Slideshare's recent growth in popularity it has become a document sharing tool of choice for many marketers. But what's really on Slideshare, who are the influencers, what makes it tick? We can utilize a custom scraper to extract that kind data from Slideshare.

To get started, point your browser to Slideshare and pick a keyword to search for.

For our example I want to look at presentations that talk about PPC in English, sorted by popularity, so the URL would be:

http://www.slideshare.net/search/slideshow?ft=presentations&lang=en&page=1&q=ppc&qf=qf1&sort=views&ud=any

Once you are on that page, pick the Kimonify button as you did earlier and tag the elements. In this case I will tag:

    Title
    Description
    Category
    Author
    Likes
    Slides

Once you have tagged those, go ahead and add the pagination as described above.

That will make a nice rich dataset which should look like this:

Hit Done and you're finished. In order to quickly highlight the benefits of this rich data, I am going to load the data into Spotfire to get some interesting statics (I hope).

4. Insights

Rather than do a step-by-step walktrough of how to build dashboards, which you can find here, I just want to show you some insights you can glean from this data:

    Most Popular Authors by Category. This shows you the top contributors and the categories they are in for PPC (squares sized by Likes)

    Correlations. Is there a correlation between the numbers of slides vs. the number of likes? Why not find out?
    Category with the most PPC content. Discover where your content works best (most likes).

5. Output

One of the great things about Kimono we have not really covered is that it actually converts websites into APIs. That means you build them once, and each time you need the data you can call it up. As an example, if I call up the Slideshare API again tomorrow, the data will be different. So you basically appified Slisdeshare. The interesting part here is the flexibility that Kimono offers. If you go to the How to Use slide, you will see the way Kimono treats the Source URL In this case it looks like this:

The way you can pull data from Kimono aside from the export is their own API; in this case you call the default URL,

http://www.kimonolabs.com/api/YOURPAIID?apikey=YO...

You would get the default data from the original URL; however, as illustrated in the table above, you can dynamically adjust elements of the source URL.

For example, if you append "&q=SEO"

(http://www.kimonolabs.com/api/YOURPAIID?apikey=YOURAPIKEY&q=SEO)

you would get the top slides for SEO instead of PPC. You can change any of the URL options easily.

I know this was a lot of information, but believe me when I tell you, we just scratched the surface. Tools like Kimono offer a variety of advanced functions that really open up the possibilities. Once you start to realize the potential, you will come up with some amazing, innovative ideas. I would love to see some of them here shared in the comments. So get out there and start scraping … and please feel free to tweet at me or reply below with any questions or comments!

Source: http://moz.com/blog/web-scraping-with-kimono-labs

Web Scraping for Fun & Profit

There’s a number of ways to retrieve data from a backend system within mobile projects. In an ideal world, everything would have a RESTful JSON API – but often, this isn’t the case.Sometimes, SOAP is the language of the backend. Sometimes, it’s some proprietary protocol which might not even be HTTP-based. Then, there’s scraping.

Retrieving information from web sites as a human is easy. The page communicates information using stylistic elements like headings, tables and lists – this is the communication protocol of the web. Machines retrieve information with a focus on structure rather than style, typically using communication protocols like XML or JSON. Web scraping attempts to bridge this human protocol into a machine-readable format like JSON. This is what we try to achieve with web scraping.

As a means of getting to data, it don’t get much worse than web scraping. Scrapers were often built with Regular Expressions to retrieve the data from the page. Difficult to craft, impossible to maintain, this means of retrieval was far from ideal. The risks are many – even the slightest layout change on a web page can upset scraper code, and break the entire integration. It’s a fragile means for building integrations, but sometimes it’s the only way.

Having built a scraper service recently, the most interesting observation for me is how far we’ve come from these “dark days”. Node.js, and the massive ecosystem of community built modules has done much to change how these scraper services are built.

Effectively Scraping Information

Websites are built on the Document Object Model, or DOM. This is a tree structure, which represents the information on a page.By interpreting the source of a website as a DOM, we can retrieve information much more reliably than using methods like regular expression matching. The most popular method of querying the DOM is using jQuery, which enables us to build powerful and maintainable queries for information. The JSDom Node module allows us to use a DOM-like structure in serverside code.

For purpose of Illustration, we’re going to scrape the blog page of FeedHenry’s website. I’ve built a small code snippet that retrieves the contents of the blog, and translates it into a JSON API. To find the queries I need to run, first I need to look at the HTML of the page. To do this, in Chrome, I right-click the element I’m looking to inspect on the page, and click “Inspect Element”.

Screen Shot 2014-09-30 at 10.44.38

Articles on the FeedHenry blog are a series of ‘div’ elements with the ‘.itemContainer’ class

Searching for a pattern in the HTML to query all blog post elements, we construct the `div.itemContainer` query. In jQuery, we can iterate over these using the .each method:

var posts = [];

$('div.itemContainer').each(function(index, item){

  // Make JSON objects of every post in here, pushing to the posts[] array

});

From there, we pick off the heading, author and post summary using a child selector on the original post, querying the relevant semantic elements:

    Post Title, using jQuery:

    $(item).find('h3').text()trim() // trim, because titles have white space either side

    Post Author, using jQuery:

    $(item).find('.catItemAuthor a').text()

    Post Body, using jQuery:

    $(item).find('p').text()

Adding some JSDom magic to our snippet, and pulling together the above two concept (iterating through posts, and picking off info from each post), we get this snippet:

var request = require('request'),

jsdom = require('jsdom');

jsdom.env(

  "http://www.feedhenry.com/category/blog",

  ["http://code.jquery.com/jquery.js"],

  function (errors, window) {

    var $ = window.$, // Alias jQUery

    posts = [];

    $('div.itemContainer').each(function(index, item){

      item = $(item); // make queryable in JQ

      posts.push({

        heading : item.find('h3').text().trim(),

        author : item.find('.catItemAuthor a').text(),

        teaser : item.find('p').text()

      });

    });

    console.log(posts);

  }

);

A note on building CSS Queries

As with styling web sites with CSS, building effective CSS queries is equally as important when building a scraper. It’s important to build queries that are not too specific, or likely to break when the structure of the page changes. Equally important is to pick a query that is not too general, and likely to select extra data from the page you don’t want to retrieve.

A neat trick for generating the relevant selector statement is to use Chrome’s “CSS Path” feature in the inspector. After finding the element in the inspector panel, right click, and select “Copy CSS Path”. This method is good for individual items, but for picking repeating patterns (like blog posts), this doesn’t work though. Often, the path it gives is much too specific, making for a fragile binding. Any changes to the page’s structure will break the query.

Making a Re-usable Scraping Service

Now that we’ve retrieved information from a web page, and made some JSON, let’s build a reusable API from this. We’re going to make a FeedHenry Blog Scraper service in FeedHenry3. For those of you not familiar with service creation, see this video walkthrough.

We’re going to start by creating a “new mBaaS Service”, rather than selecting one of the off-the-shelf services. To do this, we modify the application.js file of our service to include one route, /blog, which includes our code snippet from earlier:

// just boilerplate scraper setup

var mbaasApi = require('fh-mbaas-api'),

express = require('express'),

mbaasExpress = mbaasApi.mbaasExpress(),

cors = require('cors'),

request = require('request'),

jsdom = require('jsdom');

var app = express();

app.use(cors());

app.use('/sys', mbaasExpress.sys([]));

app.use('/mbaas', mbaasExpress.mbaas);

app.use(mbaasExpress.fhmiddleware());

// Our /blog scraper route

app.get('/blog', function(req, res, next){

  jsdom.env(

    "http://www.feedhenry.com/category/blog",

    ["http://code.jquery.com/jquery.js"],

    function (errors, window) {

      var $ = window.$, // Alias jQUery

      posts = [];

      $('div.itemContainer').each(function(index, item){

        item = $(item); // make queryable in JQ

        posts.push({

          heading : item.find('h3').text().trim(),

          author : item.find('.catItemAuthor a').text(),

          teaser : item.find('p').text()

        });

      });

      return res.json(posts);

    }

  );

});

app.use(mbaasExpress.errorHandler());

var port = process.env.FH_PORT || process.env.VCAP_APP_PORT || 8001;

var server = app.listen(port, function() {});

We’re also going to write some documentation for our service, so we (and other developers) can interact with it using the FeedHenry discovery console. We’re going to modify the README.md file to document what we’ve just done using API Blueprint documentation format:

# FeedHenry Blog Web Scraper

This is a feedhenry blog scraper service. It uses the `JSDom` and `request` modules to retrieve the contents of the FeedHenry developer blog, and parse the content using jQuery.

# Group Scraper API Group

# blog [/blog]

Blog Endpoint

## blog [GET]

Get blog posts endpoint, returns JSON data.

+ Response 200 (application/json)

    + Body

            [{ blog post}, { blog post}, { blog post}]

We can now try out the scraper service in the studio, and see the response:

Scraping – The Ultimate in API Creation?

Now that I’ve described some modern techniques for effectively scraping data from web sites, it’s time for some major caveats. First,  WordPress blogs like ours already have feeds and APIs available to developers - there’s no need to ever scrape any of this content. Web Scraping is not a replacement for an API. It should be used only as a last resort, after every endeavour to discover an API has already been made. Using a web scraper in a commercial setting requires much time set aside to maintain the queries, and an agreement with the source data is being scraped on to alert developers in the event the page changes structure.

With all this in mind, it can be a useful tool to iterate quickly on an integration when waiting for an API, or as a fun hack project.

Source: http://www.feedhenry.com/web-scraping-fun-profit/

Get started with screenscraping using Google Chrome’s Scraper extension

How do you get information from a website to a Excel spreadsheet? The answer is screenscraping. There are a number of softwares and plattforms (such as OutWit Hub, Google Docs and Scraper Wiki) that helps you do this, but none of them are – in my opinion – as easy to use as the Google Chrome extension Scraper, which has become one of my absolutely favourite data tools.

What is a screenscraper?

I like to think of a screenscraper as a small robot that reads websites and extracts pieces of information. When you are able to unleash a scraper on hundreads, thousands or even more pages it can be an incredibly powerful tool.

In its most simple form, the one that we will look at in this blog post, it gathers information from one webpage only.

Google Chrome’s Scraper

Scraper is an Google Chrome extension that can be installed for free at Chrome Web Store.

Image

Now if you installed the extension correctly you should be able to see the option “Scrape similar” if you right-click any element on a webpage.

The Task: Scraping the contact details of all Swedish MPs

Image

This is the site we’ll be working with, a list of all Swedish MPs, including their contact details. Start by right-clicking the name of any person and chose Scrape similar. This should open the following window.

Understanding XPaths

At w3schools you’ll find a broader introduction to XPaths.

Before we move on to the actual scrape, let me briefly introduce XPaths. XPath is a language for finding information in an XML structure, for example an HTML file. It is a way to select tags (or rather “nodes”) of interest. In this case we use XPaths to define what parts of the webpage that we want to collect.

A typical XPath might look something like this:

    //div[@id="content"]/table[1]/tr

Which in plain English translates to:

    // - Search the whole document...

    div[@id="content"] - ...for the div tag with the id "content".

    table[1] -  Select the first table.

    tr - And in that table, grab all rows.

Over to Scraper then. I’m given the following suggested XPath:

    //section[1]/div/div/div/dl/dt/a

The results look pretty good, but it seems we only get names starting with an A. And we would also like to collect to phone numbers and party names. So let’s go back to the webpage and look at the HTML structure.

Right-click one of the MPs and chose Inspect element. We can see that each alphabetical list is contained in a section tag with the class “grid_6 alpha omega searchresult container clist”.

 And if we open the section tag we find the list of MPs in div tags.

We will do this scrape in two steps. Step one is to select the tags containing all information about the MPs with one XPath. Step two is to pick the specific pieces of data that we are interested in (name, e-mail, phone number, party) and place them in columns.

Writing our XPaths

In step one we want to try to get as deep into the HTML structure as possible without losing any of the elements we are interested in. Hover the tags in the Elements window to see what tags correspond to what elements on the page.

In our case this is the last tag that contains all the data we are looking for:

    //section[@class="grid_6 alpha omega searchresult container clist"]/div/div/div/dl

Click Scrape to test run the XPath. It should give you a list that looks something like this.

Scroll down the list to make sure it has 349 rows. That is the number of MPs in the Swedish parliament. The second step is to split this data into columns. Go back to the webpage and inspect the HTML code.

I have highlighted the parts that we want to extract. Grab them with the following XPaths:

    name: dt/a
    party: dd[1]
    region: dd[2]/span[1]
    seat: dd[2]/span[2]
    phone: dd[3]
    e-mail: dd[4]/span/a

Insert these paths in the Columns field and click Scrape to run the scraper.

Click Export to Google Docs to get the data into a spreadsheet.

Source: http://dataist.wordpress.com/2012/10/12/get-started-with-screenscraping-using-google-chromes-scraper-extension/

Screen-scraping with WWW::Mechanize

Screen-scraping is the process of emulating an interaction with a Web site - not just downloading pages, but filling out forms, navigating around the site, and dealing with the HTML received as a result. As well as for traditional lookups of information - like the example we'll be exploring in this article - we can use screen-scraping to enhance a Web service into doing something the designers hadn't given us the power to do in the first place. Here's an example:

I do my banking online, but get quickly bored with having to go to my bank's site, log in, navigate around to my accounts and check the balance on each of them. One quick Perl module (Finance::Bank::HSBC) later, and now I can loop through each of my accounts and print their balances, all from a shell prompt. Some more code, and I can do something the bank's site doesn't ordinarily let me - I can treat my accounts as a whole instead of individual accounts, and find out how much money I have, could possibly spend, and owe, all in total.

Another step forward would be to schedule a crontab every day to use the HSBC option to download a copy of my transactions in Quicken's QIF format, and use Simon Cozens' Finance::QIF module to interpret the file and run those transactions against a budget, letting me know whether I'm spending too much lately. This takes a simple Web-based system from being merely useful to being automated and bespoke; if you can think of how to write the code, then you can do it. (It's probably wise for me to add the caveat, though, that you should be extremely careful working with banking information programatically, and even more careful if you're storing your login details in a Perl script somewhere.)

Back to screen-scrapers, and introducing WWW::Mechanize, written by Andy Lester and based on Skud's WWW::Automate. Mechanize allows you to go to a URL and explore the site, following links by name, taking cookies, filling in forms and clicking "submit" buttons. We're also going to use HTML::TokeParser to process the HTML we're given back, which is a process I've written about previously.

The site I've chosen to demonstrate on is the BBC's Radio Times site, which allows users to create a "Diary" for their favorite TV programs, and will tell you whenever any of the programs is showing on any channel. Being a London Perl M[ou]nger, I have an obsession with Buffy the Vampire Slayer. If I tell this to the BBC's site, then it'll tell me when the next episode is, and what the episode name is - so I can check whether it's one I've seen before. I'd have to remember to log into their site every few days to check whether there was a new episode coming along, though. Perl to the rescue! Our script will check to see when the next episode is and let us know, along with the name of the episode being shown.

Here's the code:

  #!/usr/bin/perl -w
  use strict;
  use WWW::Mechanize;
  use HTML::TokeParser;

If you're going to run the script yourself, then you should register with the Radio Times site and create a diary, before giving the e-mail address you used to do so below.

  my $email = ";
  die "Must provide an e-mail address" unless $email ne ";

We create a WWW::Mechanize object, and tell it the address of the site we'll be working from. The Radio Times' front page has an image link with an ALT text of "My Diary", so we can use that to get to the right section of the site:

  my $agent = WWW::Mechanize->new();
  $agent->get("http://www.radiotimes.beeb.com/");
  $agent->follow("My Diary");

The returned page contains two forms - one to allow you to choose from a list box of program types, and then a login form for the diary function. We tell WWW::Mechanize to use the second form for input. (Something to remember here is that WWW::Mechanize's list of forms, unlike an array in Perl, is indexed starting at 1 rather than 0. Our index is, therefore,'2.')

  $agent->form(2);

Now we can fill in our e-mail address for the '<INPUT name="email" type="text">' field, and click the submit button. Nothing too complicated.

  $agent->field("email", $email);
  $agent->click();

WWW::Mechanize moves us to our diary page. This is the page we need to process to find the date details from. Upon looking at the HTML source for this page, we can see that the HTML we need to work through is something like:

  <input>
  <tr><td></td></tr>
  <tr><td></td><td></td><td class="bluetext">Date of episode</td></tr>
  <td></td><td></td>
  <td class="bluetext"><b>Time of episode</b></td></tr>
  <a href="page_with_episode_info"></a>

This can be modeled with HTML::TokeParser as below. The important methods to note are get_tag - which will move the stream on to the next opening for the tag given - and get_trimmed_text, which returns the text between the current and given tags. For example, for the HTML code "<b>Bold text here</b>", my $tag = get_trimmed_text("/b") would return "Bold text here" to $tag.

Also note that we're initializing HTML::TokeParser on '\$agent->{content}' - this is an internal variable for WWW::Mechanize, exposing the HTML content of the current page.

  my $stream = HTML::TokeParser->new(\$agent->{content});
  my $date;
    # <input>
  $stream->get_tag("input");
  # <tr><td></td></tr><tr>
  $stream->get_tag("tr"); $stream->get_tag("tr");
  # <td></td><td></td>
  $stream->get_tag("td"); $stream->get_tag("td");
  # <td class="bluetext">Date of episode</td></tr>
  my $tag = $stream->get_tag("td");
  if ($tag->[1]{class} and $tag->[1]{class} eq "bluetext") {
      $date = $stream->get_trimmed_text("/td");
      # The date contains '&nbsp;', which we'll translate to a space.
      $date =~ s/\xa0/ /g;
  }
   # <td></td><td></td>
  $stream->get_tag("td");
  # <td class="bluetext"><b>Time of episode</b> 
  $tag = $stream->get_tag("td");
  if ($tag->[1]{class} eq "bluetext") {
      $stream->get_tag("b");
      # This concatenates the time of the showing to the date.
      $date .= ", from " . $stream->get_trimmed_text("/b");
  }
  # </td></tr><a href="page_with_episode_info"></a>
  $tag = $stream->get_tag("a");
  # Match the URL to find the page giving episode information.
  $tag->[1]{href} =~ m!src=(http://.*?)'!;

We have a scalar, $date, containing a string that looks something like "Thursday 23 January, from 6:45pm to 7:30pm.", and we have an URL, in $1, that will tell us more about that episode. We tell WWW::Mechanize to go to the URL:

  $agent->get($1);

The navigation we want to perform on this page is far less complex than on the last page, so we can avoid using a TokeParser for it - a regular expression should suffice. The HTML we want to parse looks something like this:

  <br><b>Episode</b><br>  The Episode Title<br>

We use a regex delimited with '!' in order to avoid having to escape the slashes present in the HTML, and store any number of alphanumeric characters after some whitespace, all between <br> tags after the Episode header:

  $agent->{content} =~ m!<br><b>Episode</b><br>\s+?(\w+?)<br>!;

$1 now contains our episode, and all that's left to do is print out what we've found:

  my $episode = $1;
  print "The next Buffy episode ($episode) is on $date.\n";

And we're all set. We can run our script from the shell:

  $ perl radiotimes.pl

  The next Buffy episode (Gone) is Thursday Jan. 23, from 6:45 to 7:30 p.m.
I hope this gives a light-hearted introduction to the usefulness of the modules involved. As a note for your own experiments, WWW::Mechanize supports cookies - in that the requestor is a normal LWP::UserAgent object - but they aren't enabled by default. If you need to support cookies, then your script should call "use HTTP::Cookies; $agent->cookie_jar(HTTP::Cookies->new);" on your agent object in order to enable session-volatile cookies for your own code.
Happy screen-scraping, and may you never miss a Buffy episode again.

Source: http://www.perl.com/pub/2003/01/22/mechanize.html

Big Data Democratization via Web Scraping

Big Data Democratization via Web Scraping

If  we had to put democratization of data inline with the classroom definition of democracy, it would read- Data by the people, for the people, of the people. Makes a lot of sense, doesn’t it? It resonates with the generic feeling we have these days with respect to easy access to data for our daily tasks. Thanks to the internet revolution, and now the social media.

Big-data-crawling

Big Data web Crawling

By the people- most of the public data on the web is a user group’s sentiments, analyses and other information.

Of the people- Although the “of” here does not literally mean that the data is owned, all such data on the internet either relates to the user group itself or its views on things.

For the people- Most of this data is presented via channels (either social media, news, etc.) for public benefit be it travel tips, daily news feeds, product price comparisons, etc.

Essentially, data democratization has come to mean that by leveraging cloud computing, data that’s mostly user-generated on the internet has become accessible by all industries- big or small for their own internal use (commercial or not). This democratization has been put to use for unearthing hidden patterns from big blobs of datasets. Use cases have evolved with the consumer internet landscape and Big Data is now being used for various other means quite unanticipated.

With respect to the democratization, we’ve also heard enough about how data analytics is paving way beyond data analysts within companies and becoming available to even the non-tech-savvies. But did anyone mention DaaS providers who aid in the very first phase of data acquisition? Data scraping or web crawling (whatever your lingo is) has come to become an indivisible part of data democratization, especially when talking large-scale. The first step into bringing the public data to use is acquiring it which is where setting up web crawlers internally or partnering with DaaS providers comes to play. This blog guides towards making a choice. Its not always all the data that companies crunch or should crunch from the web. There’s obviously certain channels that are of more interest to the community than the rest and there lies the barrier- to identify sources of higher ROI and acquire data in a machine-readable format.

DaaS providers usually come to help with the entire data acquisition pipeline- starting from picking the right sources through crawl, extraction, dedup as well as data normalization based on specific requirements. Once the data has been acquired, its most likely published on another channel. Such network effect bolsters the democracy.

Steps in Data Acquisition Pipeline

crawl-extract-norm

Note- PromptCloud only delivers structured data as per the schema provided.

So while democratization may refer to easy access of computing resources in order to draw patterns from Big Data, it could also be analogous to ensuring right data in the right format at right intervals. In fact, DaaS providers have themselves used this democracy to empower it further.

Source:https://www.promptcloud.com/blog/big-data-democratization-using-web-scraping-2/

Why Businesses Need Data Scraping Service?

With the ever-increasing popularity of internet technology there is an abundance of knowledge processing information that can be used as gold if used in a structured format. We all know the importance of information. It has indeed become a valuable commodity and most sought after product for businesses. With widespread competition in businesses there is always a need to strive for better performances.

Taking this into consideration web data scraping service has become an inevitable component of businesses as it is highly useful in getting relevant information which is accurate. In the initial periods data scraping process included copying and pasting data information which was not relevant because it required intensive labor and was very costly. But now with the help of new data scraping tools like Mozenda, it is possible to extract data from websites easily. You can also take the help of data scrapers and data mining experts that scrape the data and automatically keep record of it.

How Professional Data Scraping Companies and Data Mining Experts Device a Solution?

Data Scraping Plan and Solutions

ImageCredit:http://www.loginworks.com/images/newscapingpage/data-as-service-plan.png

Why Data Scraping is Highly Essential for Businesses?

Data scraping is highly essential for every industry especially Hospitality, eCommerce, Research and Development, Healthcare, Financial and data scraping can be useful in marketing industry, real estate industry by scraping properties, agents, sites etc., travel and tourism industry etc. The reason for that is it is one of those industries where there is cut-throat competition and with the help of data scraping tools it is possible to extract useful information pertaining to preferences of customers, their preferred location, strategies of your competitors etc.

It is very important in today’s dynamic business world to understand the requirements of your customers and their preferences. This is because customers are the king of the market they determine the demand. Web data scraping process will help you in getting this vital information. It will help you in making crucial decisions which are highly critical for the success of business. With the help of data scraping tools you can automate the data scraping process which can result in increased productivity and accuracy.

Reasons Why Businesses Opt. For Website Data Scraping Solutions:

Website Scraping
Demand For New Data:

There is an overflowing demand for new data for businesses across the globe. This is due to increase in competition. The more information you have about your products, competitors, market etc. the better are your chances of expanding and persisting in competitive business environment. The manner in which data extraction process is followed is also very important; as mere data collection is useless. Today there is a need for a process through which you can utilize the information for the betterment of the business. This is where data scraping process and data scraping tools come into picture.

ImageCredit:3idatascraping.com
Capitalize On Hot Updates:

Today simple data collection is not enough to sustain in the business world. There is a need for getting up to date information. There are times when you will have the information pertaining to the trends in the market for your business but they would not be updated. During such times you will lose out on critical information. Hence; today in businesses it is a must to have recent information at your disposal.

The more recent update you have pertaining to the services of your business the better it is for your growth and sustenance. We are already seeing lot of innovation happening in the field of businesses hence; it is very important to be on your toes and collect relevant information with the help of data scrapers. With the help of data scrapping tools you can stay abreast with the latest developments in your business albeit; by spending extra money but it is necessary tradeoff in order to grow in your business or be left behind like a laggard.

Analyzing Future Demands:

Foreknowledge about the various major and minor issues of your industry will help you in assessing the future demand of your product / service. With the help of data scraping process; data scrapers can gather information pertaining to possibilities in business or venture you are involved in. You can also remain alert for changes, adjustments, and analysis of all aspects of your products and services.

Appraising Business:

It is very important to regularly analyze and evaluate your businesses. For that you need to evaluate whether the business goals have been met or not. It is important for businesses to know about your own performance. For example; for your businesses if the world market decides to lower the prices in order to grow their customer base you need to be prepared whether you can remain in the industry despite lowering the price. This can be done only with the help of data scraping process and data scraping tools.

Source:http://www.habiledata.com/blog/why-businesses-need-data-scraping-service

How to scrape Amazon with WebDriver in Java

Here is a real-world example of using Selenium WebDriver for scraping.
This short program is written in Java and scrapes book title and author from the Amazon webstore.
This code scrapes only one page, but you can easily make it scraping all the pages by adding a couple of lines.

You can download the souce here.

import java.io.*;
import java.util.*;
import java.util.regex.*;

import org.openqa.selenium.*;
import org.openqa.selenium.firefox.FirefoxDriver;

public class FetchAllBooks {

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

        WebDriver driver = new FirefoxDriver();
      
driver.navigate().to("http://www.amazon.com/tag/center%20right?ref_=tag_dpp_cust_itdp_s_t&sto

re=1");

        List<WebElement> allAuthors =  driver.findElements(By.className("tgProductAuthor"));
        List<WebElement> allTitles =  driver.findElements(By.className("tgProductTitleText"));
        int i=0;
        String fileText = "";

        for (WebElement author : allAuthors){
            String authorName = author.getText();
            String Url = (String)((JavascriptExecutor)driver).executeScript("return

arguments[0].innerHTML;", allTitles.get(i++));
            final Pattern pattern = Pattern.compile("title=(.+?)>");
            final Matcher matcher = pattern.matcher(Url);
            matcher.find();
            String title = matcher.group(1);
            fileText = fileText+authorName+","+title+"\n";
        }

        Writer writer = new BufferedWriter(new OutputStreamWriter(new

FileOutputStream("books.csv"), "utf-8"));
        writer.write(fileText);
        writer.close();

        driver.close();
    }
}

Source: http://scraping.pro/scraping-amazon-webdriver-java/