Why Hand-Scraped Flooring?

So many types of flooring possibilities exist on the market, so why hand-scraped hardwood and why now? Trends for hardwoods come and go. In recent years, the demand for exotic species has grown, and even more closer to the present, requests for hand-scraped flooring are also increasing. As a result, nearly all species are available hand-scraped, but walnut, hickory, cherry, and oak are the most popular.

In the past, parquet was a popular style of flooring, and while seldom seen in the present, parquet was characterized by an angular style and contrasting woods. Not relying on color, hand-scraped flooring instead goes for texture. The wood is typically scraped by hand, creating a rustic and unique look for every plank. But rather than be exclusively rough, some hand-scraped products have a smoother sculpted look, such as hand-sculpted hardwood, and this flooring is often considered "classic."

Texture, as well, makes the flooring have additional visual and tactile dimensions. Those walking on the floor may just want to run their hands over the surface to feel the knots, scraping, and sculpted portions. However, tastes for hand-scraped flooring vary by region. According to top hardwood manufacturer Armstrong, the sculpted look is more requested in California, while a rustic appearance of knots, mineral streaks, and graining is more common in the Southwest. The Northeast, on the other hand, is just catching onto this trend.

There's no one look for hand-scraped flooring. Rather, hardwood is altered through scraping or brushing, finishing, or aging; a combination of such techniques may also be used.

Scraped or brushed hardwoods are sold under names "wire brushed," which has accented grain and no sapwood; "hand-sculpted," which indicates a smoother distressed appearance; and "hand hewn and rough sawn," which describes the roughest product available.

Aged hand-scraped products go by "time worn aged" or "antique." For both of these, the wood is aged, and then the appearance is accented through dark-colored staining, highlighting the grain, or contouring. A lower grade of hardwood is used for antique.

A darker stain tends to bring out the look of hand-scraped flooring. For woods that have specifically been stained, "French bleed" is the most common. Such a product has deeper beveled edges, and joints are emphasized with a darker color stain.

No matter the look for hand-scraped flooring, the hardwood is altered by hand, generally by a trained craftsman, such as an Amish woodworker. As a result, every plank looks unique. However, "hand-scraped" and "distressed" are often used interchangeably, but not all "distressed" products are altered by hand. Instead, the hardwood is distressed by machine, which presses a pattern into the surface of the wood.

Source:http://www.articlesbase.com/home-improvement-articles/why-hand-scraped-flooring-5488704.html

Damaged Or Affected Information Providers By Web Scraping Service

Data Scraping Services and computer hardware to grow. How is this possible? It's really simple. Computer systems installed and set in metal boxes and cabinets are a combination of electronic circuit cards. Conductive metal of choice because steel is very strong and affordable. Steel is often plated to prevent oxidation and corrosion.

Galvanizing material of choice because it is still relatively cheap, conductive, and provides a well finished appearance. Many computer enclosures are galvanized rack shelf supports, rails and other structural elements. Data Scraping Services are everywhere, they are not visible? Remember that Data Scraping Services thinner than a human hair and about You are looking for them to find them. Look for them to grow together.

Data Scraping Services exposed bridges and shorts of the circuit is still the potential to wreak havoc on a system. Remain important clues about what happens when the memory bus clock cycles during the installation of the latch is shorted? Maybe the data is corrupted. Perhaps the corruption will be detected and corrected by the error correction algorithms. Affect the data processor is actually an instruction

He logged on to various system disorders - are not logged in or track. If a reset clears the event, problem quickly annoying, but not - as significant is rejected. Often this is not the floor fixed management visibility. If the device must be set and they'll say: "Ask an IT manager ... No, why questions" Ask the operator to reset the equipment needs to be done and they will respond "... Of course, all the time why ask "

So if the Data Scraping Services are everywhere and are instruments to influence how it is not common knowledge? Most users of personal experience or get their information from reliable sources. If personal experience is unforgettable, it's human nature to discount and discard. If a jammed machine reset by filling a cup of coffee is memorable, it is not missed. Popping a diet is unusual and unforgettable. Clicking on the button is not. Data Scraping Services affected or influenced almost all providers.

If the  Services are plentiful, there are no problems?

Research has shown that Data Scraping Services to be reasonably attached to the host surface. Until a certain length, Data Scraping Services rub and rub until they are released by mechanical means such as related. After reaching a certain length, not only freedom from direct mechanical means is possible, but also as a more passive mode of vibration or air flow. Once expelled, Data Scraping Services are free to migrate within the environment.

Data Scraping Services need not be catastrophic failures. Bit errors, soft faults and other defects can be attributed to Data Scraping Services.

What is the treatment for Data Scraping Services?

In general, the accepted treatment to remove Data Scraping Services and is a pure version of the original source material. This tool is not suitable for every bad piece of the place, either a logistical or financial perspective. Does not mean that the problem should be ignored. . Will continue to grow Data Scraping Services. As they are today, they are potentially harmful.

Data Scraping Services through management training, all employees and visitors to the zinc whisker behavior are needed to sign the pledge. The promise Data Scraping Services staff and visitors are forced to treat seriously and will take no action that would aggravate the problem take. Their actions will reflect the best interests of users and reliable computing.

Conclusion

Data Scraping Services are more common than previously believed and accepted. At the same time we can keep up with Data Scraping Services can enjoy fairly reliable operation. But it is important to recognize and manage the situation - not ignore. Living with a chronic infectious disease is a useful model for operations.

Once a surface is the source of zinc whisker, it will always be a source of zinc whisker. Left alone, reliable operation can continue. When the need to interact with the surface, the material does not reveal the need for zinc whisker position.

Source:http://www.articlesbase.com/outsourcing-articles/damaged-or-affected-information-providers-by-web-scraping-service-5549982.html

Data Mining for Dollars

The more you know, the more you're aware you could be saving. And the deeper you dig, the richer the reward.

That's today's data mining capsulation of your realization: awareness of cost-saving options amid logistical obligations.

According to global trade group Association for Information and Image Management (AIIM), fewer than 25% of organizations in North America and Europe are currently utilizing captured data as part of their business process. With high ease and low cost associated with utilization of their information, this unawareness is shocking. And costly.

Shippers - you're in prime position to benefit the most by data mining and assessing your electronically-captured billing records, by utilizing a freight bill processing provider, to realize and receive significant savings.

Whatever your volume, the more you know about your transportation options, throughout all modes, the easier it is to ship smarter and save. A freight bill processor is able to offer insight capable of saving you 5% - 15% annually on your transportation expenditures.

The University of California - Los Angeles states that data mining is the process of analyzing data from different perspectives and summarizing it into useful information - knowledge that can be used to increase revenue, cuts costs, or both. Data mining software is an analytical tool that allows investigation of data from many different dimensions, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations among dozens of fields in large relational databases. Practically, it leads you to noticeable shipping savings.

Data mining and subsequent reporting of shipping activity will yield discovery of timely, actionable information that empowers you to make the best logistics decisions based on carrier options, along with associated routes, rates and fees. This function also provides a deeper understanding of trends, opportunities, weaknesses and threats. Exploration of pertinent data, in any combination over any time period, enables you the operational and financial view of your functional flow, ultimately providing you significant cost savings.

With data mining, you can create a report based on a radius from a ship point, or identify opportunities for service or modal shifts, providing insight regarding carrier usage by lane, volume, average cost per pound, shipment size and service type. Performance can be measured based on overall shipping expenditures, variances from trends in costs, volumes and accessorial charges.

The easiest way to get into data mining of your transportation information is to form an alliance with a freight bill processor that provides this independent analytical tool, and utilize their unbiased technologies and related abilities to make shipping decisions that'll enable you to ship smarter and save.

Source:http://ezinearticles.com/?Data-Mining-for-Dollars&id=7061178

Scrape Web data using R

Plenty of people have been scraping data from the web using R for a while now, but I just completed my first project and I wanted to share the code with you.  It was a little hard to work through some of the “issues”, but I had some great help from @DataJunkie on twitter.

As an aside, if you are learning R and coming from another package like SPSS or SAS, I highly advise that you follow the hashtag #rstats on Twitter to be amazed by the kinds of data analysis that are going on right now.

One note.  When I read in my table, it contained a wierd set of characters.  I suspect that it is some sort of encoding, but luckily, I was able to get around it by recoding the data from a character factor to a number by using the stringr package and some basic regex expressions.

Bring on fantasy football!

################################################################

## Help from the followingn sources:

## @DataJunkie on twitter

## http://www.regular-expressions.info/reference.html

## http://stackoverflow.com/questions/1395528/scraping-html-tables-into-r-data-frames-using-the-xml-package

## http://stackoverflow.com/questions/1395528/scraping-html-tables-into-r-data-frames-using-the-xml-package

## http://stackoverflow.com/questions/2443127/how-can-i-use-r-rcurl-xml-packages-to-scrape-this-webpage

################################################################

library(XML)

library(stringr)

# build the URL

url <- paste("http://sports.yahoo.com/nfl/stats/byposition?pos=QB",

        "&conference=NFL&year=season_2009",
        "&timeframe=Week1", sep="")

# read the tables and select the one that has the most rows

tables <- readHTMLTable(url)

n.rows <- unlist(lapply(tables, function(t) dim(t)[1]))

tables[[which.max(n.rows)]]

# select the table we need - read as a dataframe

my.table <- tables[[7]]

# delete extra columns and keep data rows

View(head(my.table, n=20))

my.table <- my.table[3:nrow(my.table), c(1:3, 5:12, 14:18, 20:21, 23:24) ]

# rename every column

c.names <- c("Name", "Team", "G", "QBRat", "P_Comp", "P_Att", "P_Yds", "P_YpA", "P_Lng", "P_Int", "P_TD", "R_Att",

        "R_Yds", "R_YpA", "R_Lng", "R_TD", "S_Sack", "S_SackYa", "F_Fum", "F_FumL")

names(my.table) <- c.names

# data get read in with wierd symbols - need to remove - initially stored as character factors

# for the loops, I am manually telling the code which regex to use - assumes constant behavior

# depending on where the wierd characters are -- is this an encoding?

front <- c(1)

back <- c(4:ncol(my.table))

for(f in front) {

    test.front <- as.character(my.table[, f])

    tt.front <- str_sub(test.front, start=3)

    my.table[,f] <- tt.front

}

for(b in back) {

    test <- as.character(my.table[ ,b])

    tt.back <- as.numeric(str_match(test, "\-*\d{1,3}[\.]*[0-9]*"))

    my.table[, b] <- tt.back
}

str(my.table)

View(my.table)

# clear memory and quit R

rm(list=ls())

q()

n

Source: http://www.r-bloggers.com/scrape-web-data-using-r/

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

Web Data Extraction Services and Data Collection Form Website Pages

For any business market research and surveys plays crucial role in strategic decision making. Web scrapping and data extraction techniques help you find relevant information and data for your business or personal use. Most of the time professionals manually copy-paste data from web pages or download a whole website resulting in waste of time and efforts.

Instead, consider using web scraping techniques that crawls through thousands of website pages to extract specific information and simultaneously save this information into a database, CSV file, XML file or any other custom format for future reference.

Examples of web data extraction process include:

• Spider a government portal, extracting names of citizens for a survey
• Crawl competitor websites for product pricing and feature data
• Use web scraping to download images from a stock photography site for website design

Automated Data Collection

Web scraping also allows you to monitor website data changes over stipulated period and collect these data on a scheduled basis automatically. Automated data collection helps you discover market trends, determine user behavior and predict how data will change in near future.

Examples of automated data collection include:

• Monitor price information for select stocks on hourly basis
• Collect mortgage rates from various financial firms on daily basis
• Check whether reports on constant basis as and when required

Using web data extraction services you can mine any data related to your business objective, download them into a spreadsheet so that they can be analyzed and compared with ease.

In this way you get accurate and quicker results saving hundreds of man-hours and money!

With web data extraction services you can easily fetch product pricing information, sales leads, mailing database, competitors data, profile data and many more on a consistent basis.

Should you have any queries regarding Web Data extraction services, please feel free to contact us. We would strive to answer each of your queries in detail.

Source:http://ezinearticles.com/?Web-Data-Extraction-Services-and-Data-Collection-Form-Website-Pages&id=4860417

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/

Autoscraping casts a wider net

We have recently started letting more users into the private beta for our Autoscraping service. We’re receiving a lot of applications following the shutdown of Needlebase and we’re increasing our capacity to accommodate these users.

Natalia made a screencast to help our new users get started:

It’s also a great introduction to what this service can do.

We released slybot as an open source integration of the scrapely extraction library and the scrapy framework. This is the core technology behind the autoscraping service and we will make it easy to export autoscraping spiders from Scrapinghub  and run them completely with slybot – allowing our users to have the flexibility and freedom provided by open source.

Source:http://blog.scrapinghub.com/2012/02/27/autoscraping-casts-a-wider-net/

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/

Seven tools for web scraping – To use for data journalism & creating insightful content

I’ve been creating a lot of (data driven) creative content lately and one of the things I like to do is gathering as much data as I can from public sources. I even have some cases it is costing to much time to create and run database queries and my personal build PHP scraper is faster so I just wanted to share some tools that could be helpful. Just a short disclaimer: use these tools on your own risk! Scraping websites could generate high numbers of pageviews and with that, using bandwidth from the website you are scraping.

1. Scraper (Chrome plugin)

    Scraper is a simple data mining extension for Google Chrome™ that is useful for online research when you need to quickly analyze data in spreadsheet form.

You can select a specific data point, a price, a rating etc and then use your browser menu: click Scrape Similar and you will get multiple options to export or copy your data to Excel or Google Docs. This plugin is really basic but does the job it is build for: fast and easy screen scraping.

2. Simple PHP Scraper

PHP has a DOMXpath function. I’m not going to explain how this function works, but with the script below you can easily scrape a list of URLs. Since it is PHP, use a cronjob to hourly, daily or weekly scrape the desired data. If you are not used to creating Xpath references, use the Scraper for Chrome plugin by selecting the data point and see the Xpath reference directly.

scraper-example

– Click here to download the example script.

3. Kimono Labs
Kimono has two easy ways to scrape specific URLs: just paste the URL into their website or use their bookmark. Once you have pointed out the data you need, you can set how often and when you want the data to be collected. The data is saved in their database. I like the facts that their learning curve is not that steep and it doesn’t look like you need a PHD in engineering to use their software. The disadvantage of this tool is the fact you can’t upload multiple URLs at once.

4. Import.io

Import.io is a browser based web scraping tool. By following their easy step-by-step plan you select the data you want to scrape and the tool does the rest. It is a more sophisticated tool compared to Kimono. I like it because of the fact it shows a clear overview of all the scrapers you have active and you can scrape multiple URLs at once.

5. Outwit Hub

I will start with the two biggest differences compared to the previous tool: it is a softwarepackage to use on your PC or laptop and to use its full potential it will cost you 75 USD. The free version can only scrape 100 rows of data. What I do like is the number of preprogrammed options to scrape which makes it easy to start and learn about web scraping.

6. ScraperWiki

This tool is really for people wanting to scrape on a massive scale. You can code your own scrapers (in PHP, Ruby & Python) and pricing is really cheap looking to what you can get: 29USD / month for 100 datasets. You are completely free in using libraries and timers. And if your programming skills are not good enough, they can help you out (paid service though). Compared to other tools, this is the most advanced tool that offers the basics of web scraping.

7. Fminer.com

This tool made it possible to finally scrape all the data inside Google Webmaster Tools since it can deal with JavaScript and AJAX interfaces. Read my extensive review on this page: Scraping Webmaster Tools with FMiner!

But on the end, building your individual project scrapers will always be more effective than using predefined scrapers. Am I missing any tools in this sum up of tools?

Source: http://www.notprovided.eu/7-tools-web-scraping-use-data-journalism-creating-insightful-content/

Multiple Listing Service Gets Favorable Appellate Ruling in Scraping Lawsuit

This is a follow-up to our massive post on anti-scraping lawsuits in the real estate industry from New Year’s Eve 2012 (Note: the portion on MRIS is about halfway through the post, labeled “Same Writ, Different Plaintiff”).

AHRN is a California real estate broker that owns and operates NeighborCity.com. The site gets its data in part by scraping from MLS databases–in this case, MRIS. As part of the scraping, however, AHRN had collected and displayed copyrighted photographs among the bits and pieces of general textual information about the properties. MRIS sent a cease and desist letter to AHRN, and filed suit alleging various copyright claims after the parties failed to agree on a license to use the photographs. Ultimately, a district court in Maryland granted a motion made by MRIS for a preliminary injunction.

When we last left off, the district court had revised its preliminary injunction order to enjoin only AHRN’s use of MRIS’s photographs–not the compilation itself or any textual elements that may be considered a part of it. Since then, AHRN appealed the injunction. On July 18th, the Fourth Circuit Court of Appeals affirmed.

Background

shutterstock_108008486.jpgAHRN argued that MRIS failed to show a likelihood of success on its copyright infringement claim because MRIS: (1) failed to register its copyright in the individual photographs when it registered the database, and (2) did not have a copyright interest in the photographs because the subscribers’ electronic agreement to MRIS’s terms of use failed to transfer those rights.

 MRIS Did Not Fail to Register Its Interest in the Photographs

This first question revolved around the scope of MRIS’s registrations. AHRN argued that MRIS’s collective work registrations did not cover the individual photographs because MRIS did not identify the names of the authors and titles of those works. MRIS argued that 17 U.S.C. §409 did not require any such identification when applied to collective works, and that its general description of the pre-existing photographs’ inclusion sufficed.

The court began its discussion by noting the “ambiguous” nature of §409’s language and its varying judicial interpretations. Some courts have barred infringement suits because the collective work registrant failed to list the authors, while others have allowed infringement suits where the registrant owns the rights to the component works as well as the collective work.

In this case, the court agreed with MRIS and found that the latter approach was more consistent with the relevant statutes and regulations:

    Adding impediments to automated database authors’ attempts to register their own component works conflicts with the general purpose of Section 409 to encourage prompt registration . . . and thwarts the specific goal embodied in Section 408 of easing the burden on group registrations[.]

As part of its decision, the court looked favorably upon the 3Taps case, in which Craigslist sued 3Taps and Padmapper for scraping and repackaging its online classified ads. In that case, the court reasoned that it would be “inefficient” to require registrants to list each author of an extremely large number of component works to which the registrant already had obtained an exclusive license.

Having found that MRIS’s general description satisfied § 409’s pre-suit registration requirement, the court moved on to the merits of MRIS’s infringement claim–more specifically, the question of whether MRIS’s Terms of Use actually transferred a copyright interest to its subscribers’ photographs.

E-SIGN Applies to Assignments of Copyrights and Overrides § 204

AHRN challenged MRIS’s ownership of the photographs by arguing that an MLS subscriber’s electronic agreement to MRIS’s Terms of Use does not operate as an assignment of rights under § 204, which requires a signed “writing.”

In a bad sign for AHRN, the court began its discussion by volunteering an argument that MRIS did not even bring up:

    [I]n situations where “the copyright [author] appears to have no dispute with its [assignee] on this matter, it would be anomalous to permit a third party infringer to invoke [Section 204(a)’s signed writing requirement] against the [assignee].”

With that in mind, the court went on to discuss the E-SIGN act’s impact on the conveyance of copyrights. After establishing the meaning of “e-signature,” the court focused on whether the act was limited from covering this type of situation.

    The Act provides that it “does not . . . limit, alter, or otherwise affect any requirement imposed by a statute, regulation, or rule of law . . . other than a requirement that contracts or other records be written, signed, or in nonelectric form[.]”

The court emphasized the phrase “other than,” reasoning that a plain reading of the E-SIGN language showed that Congress intended the provisions to limit § 204. It also noted that Congress did not list copyright assignments among the various agreements to which E-SIGN did not apply–nor was there a catchall that included such assignments.

The court then turned to the Hermosilla case, in which a district court in Florida upheld the validity of a copyright conveyance via e-mail. It emphasized the Hermosilla court’s reliance on the purpose of § 204–“to resolve disputes between copyright owners and transferees and to protect copyright holders from persons mistakenly or fraudulently claiming oral licenses or copyright ownership.” The appellate court agreed with the Hermosilla court that allowing assignment via e-mail actually helped cut down on these types of disputes.

    To invalidate copyright transfer agreements solely because they were made electronically would thwart the clear congressional intent embodied in the E-Sign Act.

All in all, the court basically said “we don’t see why E-SIGN shouldn’t apply.” Note that it did not pass judgment specifically on whether MRIS’s Terms of Use constituted a valid contract. It simply mentioned that AHRN waived that argument by not bringing it up sooner.

Source: http://blog.ericgoldman.org/archives/2013/07/multiple_listin_1.htm

The Roots of Web Scraping and the Wisdom behind It

You may be wondering how data mining came into existence. This effective and innovative trend in business and research is indeed something commendable and the genius behind it is worth great reward. To have a clear view of the origin of web scraping, the following important factors that contribute to the creation of this phenomenon called data collection or web scraping are considered.

Foundations

Unlike any other innovation, no specific date can be clearly pointed out as the birthdate of data mining. It has come into existence as a result of several problem solving processes in major data gathering and handling situations. It appears that cyber technology has opened a Pandora box of “anything can happen” experiences. Moreover, the shift from physical to virtual data collection has resulted in a bulk of database that needed to be organized, analyzed and utilized.

Source: http://www.loginworks.com/blogs/web-scraping-blogs/roots-web-scraping-wisdom-behind/

Scraping Online Communities for your Outreach Campaigns

Online communities offer a wealth of intelligence for blog owners and business owners alike.

Exploring the data within popular communities will help you to understand who the major influencers are, what content is popular and who are the key content aggregators within your niche.

This is all fair and well to talk about, but is it feasible to be manually sorting through online communities to find this information out? Probably not.

This is where data scraping comes in.
What is Scraping and What Can it do?

I’m not going to go into great detail on what data scraping actually means, but to simplify this, here’s a definition from the Wikipedia page:

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

Let me explain this with a little example…

Imagine a huge community full of individuals within your industry. Each person within the community has a personal profile page that contains information about their interests, contact details, social profiles, etc.

If you were tasked with gathering all of this data on all of the individuals then you might start to hyperventilating at the thought of all the copy and pasting you’d need to do.

Well, an alternative is to scrape all of this content so that you can automate all of this process and easily export all of this information into a manageable, more consumable format in a matter of seconds. It’d be pretty awesome, right?
Luckily for you, I’m going to show you how to do just that!
The Example of Inbound.org

Recently, I wanted to gather a list of digital marketers that were fairly active on social media and shared a lot of content online within communities. These people were going to be some of my core targets to get content from the blog in front of.

To do this, I first found some active communities online where these types of individuals hang out. Being a digital marketer myself, this process was fairly easy and I chose Inbound.org as my starting place.

Scoping out Data Requirements
Each community is different and you’ll be able to gather varying information within each.

The place to look for this information is within the individual user profile pages. This is usually where the contact information or links to social media accounts are likely to be displayed.

For this particular exercise, I wanted to gather the following information:

    Full name
    Job title
    Company name and URL
    Location
    Personal website URL
    Twitter URL, handle and follower/following stats
    Google+ URL, follower count and list of contributor URLs
    Profile image URL
    Facebook URL
    LinkedIn URL

With all of this information I’ll be able to get a huge amount of intelligence about the community members. I’ll also have a list of social media accounts to add and engage with.
On top of this, with all the information on their websites and sites that they write for, I’ll have a wealth of potential link building prospects to work on.

Inbound.org Profiles

You’ll see in the above screenshot that a few of the pieces of data are available to see on the Inbound.org user profiles. We’ll need to get the other bits of information from the likes of Twitter and Google+, but this will all stem from the scraping of Inbound.org.

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Scraping the Data

The idea behind this is that we can set up a template based on one of the user profiles and then automate the data gathering across the rest of the profiles on the site.

This is where you’ll need to install the SEO Tools plugin for Excel (it’s free). If you’ve not used this plugin before, don’t worry – I’ve put together a full tutorial here.

Once you’ve installed the plugin, you’re good to go on the actual scraping side of things…
Quick Note: Don’t worry if you don’t have a good knowledge of coding – you don’t need it. All you’ll need is a very basic understanding of reading some code and some basic Excel skills.

To begin with, you’ll need to do a little Excel admin. Simply add in some column titles based around the data that you’re gathering. For example, with my example of Inbound.org, I had, ‘Name’, ‘Position’, ‘Company’, ‘Company URL’, etc. which you can see in the screenshot below. You’ll also want to add in a sample profile URL to work on building the template around.

spreadsheet admin
Now it’s time to start getting hands on with XPath.
How to Use XPathOnURL()

This handy little formula is made possible within Excel by the SEO Tools plugin. Now, I’m going to keep this very basic because there are loads of XPath tutorials available online that can go into the very advanced queries that are possible to use.

For this, I’m simply going to show you how to get the data we want and you can have a play around yourself afterwards (you can download the full template at the end of this post).

Here’s an example of an XPath query that gathers the name of the person within the profile that we’re scraping:

=XPathOnUrl(A2, "//*[@id='user-profile']/h2")

A2 is simply referencing the cell that contains the URL that we’re scraping. You’ll see in the screenshot above that this is Jason Acidre’s profile page.

The next part of the formula is the XPath.

What this essentially says is to scrape through the HTML to find a tag that has ‘user-profile’ id attached to it. This could be a div, span, a or whatever.

Once it’s found this tag, it then needs to look at the first h2 tag within this area and grab the text within it. This is Jason’s name, which you’ll see in the screenshot below of the code:

website code

Don’t be put off at this stage because you don’t need to go manually trawling through code to build these queries, there’s a much simpler way.

The easiest way to do this is by right-clicking on the element you want to scrape on the webpage (within Chrome); for example, on Inbound.org, this would be the profile name. Now click ‘Inspect element’.

inspect element

The developer tools window should now appear at the bottom of your browser (or in a separate window). Within that, you should see the element that you’ve drilled down on.

All you need to do now is right-click on it and press ‘Copy XPath’.
copy XPath

This will now copy the XPath code for your Excel formula to the clipboard. You’ll just need to add in the first part of the query, i.e. =XPathOnUrl(A2,

You can then paste in the copied XPath after this and add a closing bracket.

Note: When you use ‘Copy XPath’ it will wrap some parts of the code in double apostrophes (“) which you’ll need to change to single apostrophes. You’ll also need to wrap the copied XPath in double apostrophes.

Your finished code will look like this:
=XPathOnUrl(A2, "//*[@id='user-profile']/h2")

You can then apply this formula against any Inbound.org profile and it will automatically grab the user’s full name. Pretty good, right?

Check out the full video tutorial below that I’ve put together that talks you through this whole process:

[sws_blue_box box_size=””] Want more useful video tutorials? Subscribe to my YouTube channel now![/sws_blue_box]

XPath Examples for Grabbing Other Data

As you’re probably starting to see, this technique could be scaled across any website online. This makes big data much more attainable and gives you the kind of results that an expensive paid tool would offer without any of the cost – bonus!

Here’s a few more examples of XPath that you can use in conjunction with the SEO Tools plugin within Excel to get some handy information.

Twitter Follower Count

If you want to grab the number of followers for a Twitter user then you can use the following formula. Simply replace A2 with the Twitter profile URL of the user you want data on. Just a quick word of warning with this one; it looks like it’s really long and complicated, but really I’ve just used another Excel formula to snip of the text ‘followers’ from the end.

=RIGHT(XPathOnUrl(D57,"//li[@class='ProfileNav-item ProfileNav-item--followers']"),LEN(XPathOnUrl(D57,"//li[@class='ProfileNav-item ProfileNav-item--followers']"))-10)

Google+ Follower Count

Like with the Twitter follower formula, you’ll need to replace A2 with the full Google+ profile URL of the user you want this data for.

=XPathOnUrl(H67,"//span[@class='BOfSxb']")

List of ‘Contributor to’ URLs

I don’t think I need to tell you the value of pulling in a list of websites that someone contributes content to. If you do want to know then check out this post that I wrote.

This formula is a little more complex than the rest. This is because I’m pulling in a list of URLs as opposed to just one entity. This requires me to use the StringJoin function to separate all of the outputs with a comma (or whatever character you’d like).

Also, you may notice that there is an additional section to the XPath query, “href”. This pulls in the link within the specific code block instead of the text.

As you’ll see in the full Inbound.org scraper template that I’ve made, this is how I pull in the Twitter, Google+, Facebook and LinkedIn profile links.

You’ll want to replace A2 with the Google+ profile URL of the person you wish to gather data on.

=StringJoin(", ",XPathOnUrl(A2,"//a[@rel='contributor-to nofollow']","href"))

Twitter Profile Image URL
If you want to get a large version of someone’s Twitter profile image then I’ve got just the thing for you.
Again, you’ll just need to substitute A2 with their Twitter profile URL.
=XPathOnUrl(A2,"//*[@class='profile-picture media-thumbnail js-tooltip']","data-resolved-url-large")


Some Findings from the Data I’ve Gathered

With all big data sets will come some interesting findings. Here’s a few little things that I’ve found from the top 100 influential users on Inbound.org.

average followers chart

The chart above maps out the average number of followers that the top 100 users have on both Twitter (12,959) and Google+ (9,601). As well as this, it shows the average number of users that they follow on Twitter (1,363).

The next thing that I’ve looked at is the job titles of the top 100 users. You can see the most common occurrences of terms within the tag cloud below:

Job titlesFinally, I had a look through all of the domains listed within each of the top 100 Inbound.org users’ Google+ ‘contributor to’ sections and mapped out the most frequently mentioned sites.

Here’s the spread of domains that were the most popular to be contributed to:

domain frequency
It Doesn’t Stop There

As you’ve probably gathered, this can be scaled out across pretty much any community/forum/directory/website online.

With this kind of intelligence in your armoury, you’ll be able to gather more intelligence on your targets and increase the effectiveness of your outreach campaigns dramatically.

Also, as promised, you can download my full Inbound.org scraper template below:

[sdfile url=”http://www.matthewbarby.com/goodies/MatthewBarby-Inbound-Scraper.xlsx” redirect=”http://www.matthewbarby.com/thanks-downloading-inbound-scraper/”]

TL;DR

    Online communities hold valuable data on your target audiences – use it!
    Scale out your intelligence gathering by brushing up on your XPath.
    Download my Inbound.org scraper template and let it work its magic.

Source: http://www.matthewbarby.com/scraping-communities-with-xpath/

Web Data Extraction: driving data your way

Most businesses rely on the web to gather data such as product specifications, pricing information, market trends, competitor information, and regulatory details. More often than not, companies collect this data manually—a process that not only takes a significant amount of time, but also has the potential to introduce costly errors.

By automating data extraction, you're able to free yourself (and your pointer finger) from hours of copy/pasting, eliminate human errors, and focus on the parts of your job that make you feel great.

Web data extraction: What it is, why it's used, and how to get it right on an ongoing basis

Web data extraction, screen scraping, web harvesting—while these terms may have different connotations they all essentially point to the same thing: plucking data from the web and populating it in an organized way in another place for further analysis or more focused use. In an era where “big data” has become a commonplace concept, the appeal of web data extraction has grown; it’s an extremely efficient alternative to web browsing, and culls very specific data for a focused purpose.

How it's used

While each company’s needs vary, data extraction is often used for:

    Competitive intelligence, including web popularity, social perception, other sites linking to them, and placement of competitor advertisements

    Gathering financial data including stock market movement, product pricing, and more

    Creating continuity between price sheets and online websites, catalogs, or inventory databases

    Capturing product specifications like dimensions, color, and materials

    Pulling tabular data from multiple sources for in-depth analysis

Interestingly, some people even find that web data extraction can aid them in their leisure time as well, pulling data from blogs and websites that pertain to their hobbies or interests, and creating their own library of organized information on a topic. Perhaps, for instance, you want a list of all the designers that George Clooney wears (hey- we won’t question what you do in your free time). By using web scraping tools, you could automatically extract this type of data from, say, a fashion blogger who follows celebrity style, and create your own up-to-date shopping list of items.

How it's done

When you think of gathering data from the web, you should mentally juxtapose two different images: one of gathering a bucket of sand one grain at a time, and one of filling a bucket with a shovel that has the perfect scoop size to fill it completely in one sitting. While clearly the second method makes the most sense, the majority of web data extraction happens much like the first process--manually, and slowly.

Let’s take a look at a few different ways organizations extract data today.

The least productive way: manually

While this method is the least efficient, it’s also the most widespread. On the plus side, you need to learn absolutely nothing except “Ctrl+C/V” to use this method, which explains why it is the generally preferred method, despite the hours of time it can take. Imagine, for instance, managing a sales spreadsheet that keeps inventory up to date so that the information can be properly disseminated to a global sales team. Not only does it take a significant amount of time to update the spreadsheet with information from, say, your internal database and manufacturer’s website, but information may change rapidly, leaving sales reps with inaccurate information regardless.

Finding someone in the organization with a talent for programming languages like Python

Generally, automating a task without dedicated automation software requires programming, and therefore an internal resource with a solid familiarity with programming languages to create the task and corresponding script. While most organizations do, in fact, have a resource in IT or engineering with this type of ability, it often doesn’t seem like a worthy time investment for that person to derail the initiatives he or she is working on to automate web data extraction. Additionally, if companies do choose to automate using in-house resources, that person will find himself beholden to a continuing obligation, since he or she will need to adjust scripting if web objects and attributes change, disabling the task.

Outsourcing via Elance or oDesk

Unless there is a dedicated resource ready to automate and maintain data extraction processes (and most organizations wouldn’t necessarily choose to use their in-house employee time this way), companies might turn to outsourcing companies such as Elance or oDesk to hire contract help. While this is an effective way to automate a task using a resource that has a level of acumen in automation, it represents an additional cost--be it one time or on a regular basis as data extraction requirements change or increase.

Using Excel web queries

Since more often than not, data extracted from the web is often populated into an Excel spreadsheet, it’s no wonder that Excel includes web query tools expressly for that purpose. These tools are particularly useful in pulling tabular data from a website (such as product specifications, legal codes, stock prices, and a host of other information) and automatically pushing the data into a spreadsheet. Excel queries do have limitations and a learning curve, however, particularly when creating dynamic web queries. And clearly, if you’re hoping to populate the information in other sources, such as external databases, there is yet another level of difficulty to navigate.

How automation simplifies web data extraction

Culling web data quickly

Using automation is the simplest way to extract web data. As you execute the steps necessary to perform the task one time, a macro recorder captures each action, automatically generates an easily-editable script, and lets you specify how often you would like to repeat the task, and at what speed.

Maintaining the highest level of accuracy

With humans copy/pasting data, or comparing between multiple screens and entering data manually into a spreadsheet, you’re likely to run into accuracy issues (sometimes directly proportionate to the amount of time spent on the task and amount of coffee in the office!) Automation software ensures that “what you see is what you get,” and that data is picked up from the web and put back down where you want it without a hitch.

Storing web data in your preferred format

Not only can you accurately transfer data with automation software, you can also ensure that it’s populated into spreadsheets or databases in the format you prefer. Rather than simply dumping the data into a spreadsheet, you can ensure that the right information is put into the proper column, row, field, and style (think, for instance, of the difference between writing a birth date as “03/13/1912” and “12/3/13”).

Simplifying data analysis

Automation software allows you to aggregate data from disparate sources or enormous stockpiles of structured or unstructured data in a way that makes sense for your business analysis needs. This way, the majority of employees in an organization can perform some level of analysis on their own, making it easier to surface information that informs business decisions.

Reacting to changes without a hitch

Because automation software is built to recognize icons, images, symbols, and other objects regardless of their position on a screen, it can automate processes in a self-perpetuating manner. For example, let’s say you automate data retrieval from a certain chart on a retailer’s website without automation software. If the retailer decides to move that object to another area of the screen, your task would no longer produce accurate results (or work at all), leaving you to make changes to the script (or find someone who can), or re-record the task altogether. With image recognition capabilities, however, the system “memorizes” the object itself, not merely its coordinates, so that the task can continue to run irrespective of changes.

The wide sweeping appeal of automation software

Companies often pick a comprehensive automation solution not only because of its ability to effectively automate any web data extraction task, but also because it goes beyond data extraction. Automation software can permeate into other areas of the business as well, making tasks such as application integration, data migration, IT processes, Excel automation, testing, and routine tasks such as launching applications or formatting files faster and more accurate. Because it requires no programming experience to use, adoption rates are higher and businesses get more “bang for their buck.”

Almost any organization can benefit from using automation software, particularly as they grow and scale. If you are looking to quit “moving grains of sand” and start claiming back time in your day, there are a few steps you can take:

 Watch a short video that shows how web data extraction is done with automation software

 Download a free trial and start reaping the benefits of downloading even just a couple of tasks today.

 See how tasks are automated with our short, step-by-step how-to-sheets (and then give it a try yourself!)

Source: https://www.automationanywhere.com/web-data-extraction

Outsourcing Data Mining is a Wise Business Decision

Most businesses nowadays have a large volume of raw data that is never processed, because of the lack of time or resources. If your business is facing a similar situation, then you are missing out on valuable information. Without the right information, your company will be unable to make accurate business decisions.

The right information can play a key role in promoting the growth of your business. When unprocessed data is entered, filtered, classified and converted into a workable format, it can be used to maximize your profits, ameliorate your risks and run a seamless workflow.

Over the years, data mining has proved to be extremely useful in various industries, be it, healthcare, direct marketing, e-commerce, finance, customer relationship management or telecommunications. With the right information, companies have been able to make fast and effective business decisions.

Why outsource data mining?

Data mining requires the expertise of professional business and financial analysts who understand how to acquire important information from vast amounts of data. If data mining is done in-house, it can become expensive and time consuming. It can also shift your focus away from core business activities. Outsourcing data mining on the other hand is more fast, cost-effective and can give you access to professional services.

4 commonly outsourced data mining functions

Most companies outsource one or more of the following data mining functions to India:

1. Data congregation: Data is extracted from various web pages and websites, by using methods like web and screen scraping. The collected data is then entered into a database.

2. Contact data collection: Different websites are searched and information concerning contacts is collected.

3. E-commerce data: Data about varied online stores are collected, taking into account information about prices, discounts and products.

4. Data about competitors: Data about business competitors are collected to help a company gauge itself against its competition. With such valuable data, you can effectively re-design your marketing strategy and pricing matrix.

8 advantages of outsourcing data mining to India

With data mining out of your hands, your business can make huge savings in terms of time, money and infrastructure. The following are some of the benefits that you can leverage by outsourcing data mining to India:

    Get qualified and highly skilled data mining experts to work for you at an extremely affordable cost

    Be assured of the quality of information, as Indian data entry companies only extract information from reliable websites and databases

    Save on the cost of investing on the latest data mining software and technology, as your Indian service provider will be making these investments

    Get your data processed within a short turnaround time of 3,6 or 12 hours as Indian data mining companies can provide efficient data mining within a few hours

    When compared to in-house data mining, outsourcing data mining can be a lot cheaper and also bring you better results

    Stay assured about the complete privacy, security and confidentiality of your valuable data as Indian data mining companies use the latest technology to ensure 100% safety

    Get access to data with a wide market coverage as your Indian data mining provider will be serving many business with varied data mining needs

    Improve your overall productivity and generate more profits by making informed decisions about your business

Have you outsourced data mining before? If yes, which data mining service did you outsource? Did you find outsourcing more advantageous that in-house data mining. Let us know.

Source: http://blog.flatworldsolutions.com/outsourcing-data-mining-is-a-wise-business-decision/

Online Data Entry & Web Scraping Services

To operate any type of organization smoothly, it is essential to have precise data that is accurate and reliable. When your business expands, data entry on an ongoing basis is a tedious job. It’s a very time consuming task that can often distract employees focusing on core business areas.

Webpop offers all forms of online data entry services that are quick and accurate. We provide data entry services across all verticals that can be completely customized to your business requirements.

Database Population Services

Database population involves content collection from various database sources. This requires a lot of attention to detail, dedication and awareness and can prove a formidable task, especially for websites that largeley depend on it.

Webpop offer a quick and efficient database population service that helps relieve the stress from an extremely laborius task and leaves you more time to focus on more important aspects of your business. By investing just a fraction of the cost, you can outsource your database population tasks to us.

Web Scraping Services

Webpop have been assisting clients in searching, extracting and collecting data from the web for the past 5 years using the latest techniques in web scraping techology. We can scrape all types of information from a variety of sources such as websites, blogs, online directories, e-commerce websites and podcasts to name a few. We use a varied selection of automated and manual web scraping technologies to extract, gather and collect all of the required data you require from any chosen website(s) on the World Wide Web.

We can simplify the whole process from collection to population, converting your scraped data in to structured formats that are applicable to your website. This can be offered as a one time service or an ongoing basis that will assist you in constantly keeping your website’s content fresh and up to date. We can crawl competitors websites, gather sales leads, product details, pricing methodologies and also creat custom campaigns to suit your project’s requirements.

Over the years Webpop has grown from strength-to-strength by providing all types of data entry, database population and web scraping services. All of our data entry services are performed with care, due dilligence and attention to detail. We enjoy a challenge and pride ourselves on delivering results whilst working on precarious projects that require precision and total commitment.

Source:http://www.webpopdesign.com/services/data-entry/

Kimono Is A Smarter Web Scraper That Lets You “API-ify” The Web, No Code Required

A new Y Combinator-backed startup called Kimono wants to make it easier to access data from the unstructured web with a point-and-click tool that can extract information from webpages that don’t have an API available. And for non-developers, Kimono plans to eventually allow anyone track data without needing to understand APIs at all.

This sort of smarter “web scraper” idea has been tried before, and has always struggled to find more than a niche audience. Previous attempts with similar services like Dapper or Needlebase, for example, folded. Yahoo Pipes still chugs along, but it’s fair to say that the service has long since been a priority for its parent company.

But Kimono’s founders believe that the issue at hand is largely timing.

“Companies more and more are realizing there’s a lot of value in opening up some of their data sets via APIs to allow developers to build these ecosystems of interesting apps and visualizations that people will share and drive up awareness of the company,” says Kimono co-founder Pratap Ranade. (He also delves into this subject deeper in a Forbes piece here). But often, companies don’t know how to begin in terms of what data to open up, or how. Kimono could inform them.

Plus, adds Ranade, Kimono is materially different from earlier efforts like Dapper or Needlebase, because it’s outputting to APIs and is starting off by focusing on the developer user base, with an expansion to non-technical users planned for the future. (Meanwhile, older competitors were often the other way around).

The company itself is only a month old, and was built by former Columbia grad school companions Ranade and Ryan Rowe. Both left grad school to work elsewhere, with Rowe off to Frog Design and Ranade at McKinsey. But over the nearly half-dozen or so years they continued their careers paths separately, the two stayed in touch and worked on various small projects together.

One of those was Airpapa.com, a website that told you which movies were showing on your flights. This ended up giving them the idea for Kimono, as it turned out. To get the data they needed for the site, they had to scrape data from several publicly available websites.

“The whole process of cleaning that [data] up, extracting it on a schedule…it was kind of a painful process,” explains Rowe. “We spent most of our time doing that, and very little time building the website itself,” he says. At the same time, while Rowe was at Frog, he realized that the company had a lot of non-technical designers who needed access to data to make interesting design decisions, but who weren’t equipped to go out and get the data for themselves.

With Kimono, the end goal is to simplify data extraction so that anyone can manage it. After signing up, you install a bookmarklet in your browser, which, when clicked, puts the website into a special state that allows you to point to the items you want to track. For example, if you were trying to track movie times, you might click on the movie titles and showtimes. Then Kimono’s learning algorithm will build a data model involving the items you’ve selected.

Screen Shot 2014-02-18 at 4.29.05 PM

Screen Shot 2014-02-18 at 4.29.27 PM

That data can be tracked in real time and extracted in a variety of ways, including to Excel as a .CSV file, to RSS in the form of email alerts, or for developers as a RESTful API that returns JSON. Kimono also offers “Kimonoblocks,” which lets you drop the data as an embed on a webpage, and it offers a simple mobile app builder, which lets you turn the data into a mobile web application.

Screen Shot 2014-02-18 at 4.29.50 PM

For developer users, the company is currently working on an API editor, which would allow you to combine multiple APIs into one.

So far, the team says, they’ve been “very pleasantly surprised” by the number of sign-ups, which have reached ten thousand*. And even though only a month old, they’ve seen active users in the thousands.

Initially, they’ve found traction with hardware hackers who have done fun things like making an airhorn blow every time someone funds their Kickstarter campaign, for instance, as well as with those who have used Kimono for visualization purposes, or monitoring the exchange rates of various cryptocurrencies like Bitcoin and dogecoin. Others still are monitoring data that’s later spit back out as a Twitter bot.

Kimono APIs are now making over 100,000 calls every week, and usage is growing by over 50 percent per week. The company also put out an unofficial “Sochi Olympics API” to showcase what the platform can do.

The current business model is freemium based, with pricing that kicks in for higher-frequency usage at scale.

The Mountain View-based company is a team of just the two founders for now, and has initial investment from YC, YC VC and SV Angel.

Source:http://techcrunch.com/2014/02/18/kimono-is-a-smarter-web-scraper-that-lets-you-api-ify-the-web-no-code-required/

A Web Scraper’s Guide to Kimono

Being a frequent reader of Hacker News, I noticed an item on the front page earlier this year which read, “Kimono – Never write a web scraper again.” Although it got a great number of upvotes, the tech junta was quick to note issues, especially if you are a developer who knows how to write scrapers. The biggest concern was a non-intuitive UX, followed by the inability of the first beta version to extract data items from websites as smoothly as the demo video suggested.

I decided to give it a few months before I tested it out, and I finally got the chance to do so recently.

Kimono is a Y-Combinator backed startup trying to do something in a field where others have failed. Kimono is focused on creating APIs for websites which don’t have one, another term would be web scraping. Imagine you have a website which shows some data you would like to dynamically process in your website or application. If the website doesn’t have an API, you can create one using Kimono by extracting the data items from the website.

Is it Legal?

Kimono provides an FAQ section, which says that web scraping from public websites “is 100% legal” as long as you check the robots.txt file to see which URL patterns they have disallowed. However, I would advise you to proceed with caution because some websites can pose a problem.

A robots.txt is a file that gives directions to crawlers (usually of search engines) visiting the website. If a webmaster wants a page to be available on search engines like Google, he would not disallow robots in the robots.txt file. If they’d prefer no one scrapes their content, they’d specifically mention it in their Terms of Service. You should always look at the terms before creating an API through Kimono.

An example of this is Medium. Their robots.txt file doesn’t mention anything about their public posts, but the following quote from their TOS page shows you shouldn’t scrape them (since it involves extracting data from their HTML/CSS).

    For the remainder of the site, you may not duplicate, copy, or reuse any portion of the HTML/CSS, JavaScipt, logos, or visual design elements without express written permission from Medium unless otherwise permitted by law.

If you check the #BuiltWithKimono section of their website, you’d notice a few straightforward applications. For instance, there is a price comparison API, which is built by extracting the prices from product pages on different websites.

Let us move on and see how we can use this service.

What are we about to do?

Let’s try to accomplish a task, while exploring Kimono. The Blog Bowl is a blog directory where you can share and discover blogs. The posts that have been shared by users are available on the feeds page. Let us try to get a list of blog posts from the page.

The simple thought process when scraping the data is parsing the HTML (or searching through it, in simpler terms) and extracting the information we require. In this case, let’s try to get the title of the post, its link, and the blogger’s name and profile page.

Source: http://www.sitepoint.com/web-scrapers-guide-kimono/

Future of Web Scraping

The Internet is large, complex and ever-evolving. Nearly 90% of all the data in the world has been generated over the last two years. In this vast ocean of data, how does one get to the relevant piece of information? This is where web scraping takes over.

Web scrapers attach themselves, like a leech, to this beast and ride the waves by extracting information form websites at will. Granted “scraping” doesn’t have a lot of positive connotations, yet it happens to be the only way to access data or content from a web site without RSS or an open API.

Future of Web Scraping

Web scraping faces testing times ahead. We outline why there may be some serious challenges to its future.

With rise in data, redundancies in web scraping are rising. No more is web scraping a domain of the coders; in fact, companies now offer customized scraping tools to clients which they can use to get the data they want. The outcome of everyone equipped to crawl, scrape, and extract, is unnecessary waste of precious man-power. Collaborative scraping could well heal this hurt. Here, where one web crawler does a broad scraping, the others scrape data off an API. An extension of the problem is that text retrieval attracts more attention than multimedia; and with websites becoming more complex, this enforces limited scraping capacity.

Easily, the biggest challenge to web scraping technology is Privacy concerns. With data freely available (most of it voluntary, much of it involuntary), the call for stricter legislation rings loudest. Unintended users can easily target a company and take advantage of the business using web scraping. The disdain with which “do not scrape” policies are treated and terms of usage violated, tells us that even legal restrictions are not enough. This begs to ask an age-old question: is scraping legal?

Is Crawling Legal? from PromptCloud

The flipside to this argument is that if technological barriers replace legal clauses, then web scraping will see a steady, and sure, decline. This is a distinct possibility since the only way scraping activity thrives is on the grid, and if the very means are taken away and programs no longer have access to website information, then web scraping by itself will be wiped out.

Building the Future

On the same thought is the growing trend of accepting “open data”. The open data policy, while long mused hasn’t been used at the scale it should be. The old way was to believe that closed data is the edge over competitors. But that mindset is changing. Increasingly, websites are beginning to offer APIs and embracing open data. But what’s the advantage of doing so?

Selling APIs not only brings in the money, but also is useful in driving back traffic to the sites! APIs are also a more controlled, cleaner way of turning sites into services. Steadily many successful sites like Twitter, LinkedIn etc. are offering access to their APIs with paid services and actively blocking scraper and bots.

Yet, beyond these obvious challenges, there’s a glimmer of hope for web scraping. And this is based on a singular factor: the growing need for data!

With Internet & web technology spreading, massive amounts of data will be accessible on the web. Particularly with increased adoption of mobile internet. According to one report, by 2020, the number of mobile internet users will hit 3.8 billion, or around half of the world’s population!

Since ‘big data’ can be both, structured & unstructured; web scraping tools will only get sharper and incisive. There is fierce competition between those who provide web scraping solutions. With the rise of open source languages like Python, R & Ruby, Customized scraping tools will only flourish bringing in a new wave of data collection and aggregation methods.

Source: https://www.promptcloud.com/blog/Future-of-Web-Scraping

3 Reasons to Up Your Web Scraping Game

If you aren’t using a machine-learning-driven intelligent Web scraping solution yet, here are three reasons why you might want to abandon that entry-level Web-scraping software or cut your high-cost script-writing approach.

    You need to keep an eye on a large number of web sources that get updated frequently.
    Understanding what’s changed is at least as critical as the data itself.
    You don’t want maintenance and scheduling to drag you down.

Here’s what an intelligent Web-scraping solution can deliver – and why:

1. Better data monitoring of an ever-shifting Web

If you need to keep a watch over hundreds, thousands or even tens of thousands of sites, an intelligent Web scraper is a must, because:

    It can scale – easily adding new websites, coordinating extraction routines, and automating the normalization of data across different websites.

    It can navigate and extract data from websites efficiently. Script-based approaches typically only can view a Web page in isolation, making it difficult to optimize navigation across unique pages of a targeted site. More intelligent approaches can be trained to bypass unnecessary links and leave a lighter footprint on the sites you need to access. And, they can monitor millions of precise Web data points quickly. This means you can monitor more pages on more sites with more frequent updates.

2. Critical alerts to Web data changes

A key sales executive suddenly drops off of the management page of your main competitor. That can mean big shakeup in the entire organization, which your sales team can jump on.

An intelligent Web scraper can alert you to this personnel shift because it can be set to monitor for just the changes; less powerful technologies or script-based approaches can’t. Whether you’re tracking price shifts, people moves, or product changes (or more) intelligent Web scraping delivers more profound insights.

3. Maintenance may become your biggest nightmare

You’ve purchased an entry-level tool and built out scrapers for a few hundred sites.  At first, everything seems fine. But, within weeks you begin to notice that your data is incomplete and not being updated as you’d expected. Why did your data deliveries disappear?

Reality is that these low-cost tools are simply not designed for mission-critical business applications – on the surface they look helpful and easy to use, but underneath the surface they are script-based and highly dependent upon the HTML of a website. But websites change, and entry-level web scraping tools are simply not engineered to adapt to those changes.

And, most of these tools are simply not designed for enterprise use. They have limited reporting, if any, so the only way to know whether they’re successfully completing their tasks is by finding gaps in the data – often when it’s too late.

An intelligent web scraping approach doesn’t rely upon the HTML of a web page. It uses machine learning algorithms which view the web the same way a user might. A typical reader doesn’t get confused when a font or color is changed on a website, and neither do these algorithms. But simple approaches to web scraping are highly dependent on the specific HTML to help it understand the content of a page. So, when websites have design changes (on average once every 18 months), the software fails.

While entry-level web scraping software can be an easy solution for simple, one-time web scraping projects, the scripts they generate are fragile and the resources required for tracking and maintenance can become overwhelming when you need to regularly extract data from multiple sites.

Case in point: Shopzilla assimilates data five times faster than outsourced Web scrapers

To demonstrate the power of intelligent Web scraping, here’s a real-life example from Shopzilla.  Shopzilla manages a premier portfolio of online shopping brands in the United States and Europe, connecting more than 40 million shoppers each month with millions of products from retailers worldwide. With the explosive growth of retail data on the Web, Shopzilla’s outsourced, custom-built approach, based on scripting, could not add the product lines of new retailers to its site in a timely fashion. It was taking up to two weeks to write the scripts needed to make a single site accessible.

By deploying Connotate’s intelligent web scraping platform on site, Shopzilla gained the ability to harness Web data’s rapid growth and keep up to date. Today, new sources are added in days, not weeks.  The platform continually monitors Web content from thousands of sites, delivering high volumes of data every day in a structured format. The result: 500 percent more data from new retailers. An added bonus: the company has reduced IT maintenance costs and its dependence on outsourced development timetables. Case in point: Deep competitor intelligence in two languages

A global manufacturer needed to monitor competitors’ technology improvements in a field where market leadership hinges on an ability to quickly leverage these advances. That meant accessing scholarly journals and niche sites in multiple languages. Using the Connotate solution, it was able to access highly-targeted, keyword-driven university and industry research journals and blogs in German and English that are hard to reach because they do not support RSS feeds. Our solution also incorporated semantic analysis to tag and categorize data and help identify new technologies and products not currently in the keyword list. The firm enhanced its competitive edge with the up-to-the-minute, precise data it needed.

Is your Web scraping intelligent enough?

See what intelligent agents through an automated Web data extraction and monitoring solution can bring to your business. Contact us and speak with one of experts.

Source:http://www.connotate.com/3-reasons-web-scraping-game-6579#.VGMjH2f4EuQ

Data Scraping vs. Data Crawling

One of our favorite quotes has been- ‘If a problem changes by an order, it becomes a totally different problem’ and in this lies the answer to- what’s the difference between scraping and crawling?

Crawling usually refers to dealing with large data-sets where you develop your own crawlers (or bots) which crawl to the deepest of the web pages. Data scraping on the other hand refers to retrieving information from any source (not necessarily the web). It’s more often the case that irrespective of the approaches involved, we refer to extracting data from the web as scraping (or harvesting) and that’s a serious misconception.

=>Below are some differences in our opinion- both evident and subtle

1.    Scraping data does not necessarily involve the web. Data scraping could refer to extracting information from a local machine, a database, or even if it is from the internet, a mere “Save as” link on the page is also a subset of the data scraping universe. Crawling on the other hand differs immensely in scale as well as in range. Firstly, crawling = web crawling which means on the web, we can only “crawl” data. Programs that perform this incredible job are called crawl agents or bots or spiders (please leave the other spider in spiderman’s world). Some web spiders are algorithmically designed to reach the maximum depth of a page and crawl them iteratively (did we ever say scrape?).

2.    Web is an open world and the quintessential practising platform of our right to freedom. Thus a lot of content gets created and then duplicated. For instance, the same blog might be posted on different pages and our spiders don’t understand that. Hence, data de-duplication (affectionately dedup) is an integral part of data crawling. This is done to achieve two things- keep our clients happy by not flooding their machines with the same data more than once, and saving our own servers some space. However, dedup is not necessarily a part of data scraping.

3.    One of the most challenging things in the web crawling space is to deal with coordination of successive crawls. Our spiders have to be polite with the servers that they hit so that they don’t piss them off and this creates an interesting situation to handle. Over a period of time, our intelligent spiders have to get more intelligent (and not crazy!) and learn to know when and how much to hit a server in order to crawl data on its web pages while complying with its politeness policies.

4.    Finally, different crawl agents are used to crawl different websites and hence you need to ensure they don’t conflict with each other in the process. This situation never arises when you intend to just scrape data.

On a concluding note, scraping represents a very superficial node of crawling which we call extraction and that again requires few algorithms and some automation in place.

Source:https://www.promptcloud.com/blog/data-scraping-vs-data-crawling/

Web Scraping the Solution to Data Harvesting

The internet is the number one information provider in the world and it is of course the largest in the same course. Web scraping is meant to extract and harvest useful information from the internet. It can be regarded as a multidisciplinary process that involves statistics, databases, data harvesting and data retrieval.

There has been noted a rapid expansion of the web and therefore causing an enormous growth of information. This has led to increased difficulty in the extraction of useful and potential information. Web scraping therefore confronts this problem by harvesting explicit information from a number of websites for knowledge discovery and easy access. It is important to realize that query interfaces of web databases are prone to sharing of same building blocks. It is therefore important to realize that the web offers unprecedented challenge and opportunity to data harvesting.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/web-scraping-solution-data-harvesting/

Application of Web Data Mining in CRM

The process of improvising the customer relations and interactions and making them more amicable may be termed as Customer relationship management (CRM). Since web data mining is used in the utilization of the various modeling and data analysis methods in detecting given patterns and relationships in the data, it can be used as an effective tool in CRM. By the effectively using web data mining you are able to understand what your customers what.

It is important to note that web data mining can be used effectively in searching for the right and potential customers to be offered the right products at the right time. The result of this in any business is the increase in the revenue generated. This is made possible as you are able to respond to each customer in an effective and efficient way. The method further utilizes very few resources and can be therefore termed as an economical method.

In the next paragraphs we discuss the basic process of customer relationship management and its integration with web data mining service. The following are the basic process that should be used in understanding what your customers need, sending them the right offers and products, and reducing the resources used in managing your customers.

Defining the business objective. Web data mining can be used to define and inform your customers your business objective. By doing research you can be able to determine whether your business objective is communicated well to your customers and clients. Does your business objective take interest in the customers? Your business goal must be clearly outlined in your business CRM. By having a more precise and defined goal is the possible way of ensuring success in the customer relationship management.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/application-web-data-mining-crm/

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

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

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

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

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

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

Thanks for the help! Here is the code:

##POSTAL CODE POPULATION SCRAPER##

import requests

import re

import datetime

def zip_population_scrape():

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

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


    for postal_code in range(1001,5000):

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

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

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

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

            #if valid webpage:
            if http == 200:

                #save html as text
                html = response.text

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

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

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

                    #add to # scraped counter
                    total_scraped +=1

                    postal_code_data.append([postal_code_string,found])

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

                    total_scraped +=1

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

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

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

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

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

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

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


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



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