Web Data Scraping Services Have Various Method Of Business

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

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

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

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

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

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

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

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

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

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

What Kind of Legal Problems Can Web Scraping Cause

Web scraping software is readily available and has been used by many for legitimate purposes. It has also been used for illegal purposes. A website that engages in this practice should know the legal dangers of the activity.

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Black Hat SEO Popular Techniques

General Knowledge- VII

The idea of web scraping is not new. Search engines have used this type of software to determine which results appear when someone conducts a search. They use special software software to extract data from a website and this data is then used to calculate the rankings of the website. Websites work very hard to improve their ranking and their chance of being found by anyone making a search. This use of this practice is understood and is considered to be a legitimate use for the software. However, there are services that provide web scraping and screen scraping prevention services and help the webmaster to remain safe from the attack of bad bots.

The problem with duplicacy is that it is often used for less than legitimate reasons. Since the software responsible can collect all sorts of data from websites and store the information that is collected, it represents a danger to anyone who might be affected by it. The information that can be collected can be used for many practices that are not so legitimate and may even be illegal. Anyone who is involved in this practice of content duplicacy should be aware of the legal issues implicated with this practice. It may be wise for anyone who has a website to find ways to prevent a site from being scraped or to use professional services to block site scraping.

Legal problems

The first thing to worry about, if you have a website or are using web scraping software, is when you might run into legal problems. Some of the issues that web scraping can cause include:

•    Access. If the software is used to access sites it does not have the right to access and takes information that it is not entitled to, the owner of the web scarping software may find themselves in legal trouble.

•    Re-use. The software can collect and reuse information. If that information is copyrighted, that might be a legal problem. Any information that is reused without permission may create legal issues for anyone who uses it.

•    Robots. Some states have enacted laws that are designed to keep people from using scraping robots. These automatically search out information on websites and using them may be illegal in some states. It is up to the user of the web scraping software to comply with any laws in the state in which they are operating.

Who is Responsible

The laws and regulations surrounding this practice are not always clear. There are many grey areas that allow this practice to occur. The question is, who is responsible for determining whether the use of web scraping software is legal?

Websites collect the information, but they may not be the entity using the web scraping software. If they are using this type of software, it is not always enough to inform the website's visitors that this practice is occurring. Putting this information into the user agreement may or may not protect the website from legal problems.

It is also partly the responsibility of a site owner to prevent a site from being scraped. There is software that can be used that will do this for a website and will keep any information that is collected safe and secure. A website may or may not be held legally responsible for any web scraper that is able to collect information they have. It will depend on why the data was collected, how it was used, who collected it, and whether precautions were taken.

What to expect

The issue of content copying and the legal issues surrounding it will continue to evolve. As more courts take on this issue, the lines between legal and illegal web scraping will become clearer. Many of the cases that have been brought to court have occurred in civil court, although there are some that have been taken up in a criminal court. There will be times when such practice may actually be a felony.

Before you use spying software, you need to realize that the laws surrounding its use are not clear. If you operate a website, you need to know the legal issues that you may face if scraping software is used on your website. The best step is to use the software available to protect your website and stop web scraping and be honest on your site if web scraping is used.

Source: http://www.articlesbase.com/technology-articles/what-kind-of-legal-problems-can-web-scraping-cause-6780486.html

Central Qld Coal: Mining for Needed Investments

The Central Qld Coal Project is situated in the Galilee Coal Basin, Central Queensland with the purpose of establishing a mine to service international export markets for thermal coal. An estimated cost to such a project would be around $ 7.5 billion - the amount proves that the mining industry is one serious business to begin with.

In addition to the mine, the Central Qld Coal Project also proposes to construct a railway, potentially in excess of 400km depending on the final option: Either to transport processed coal to an expanded facility at Abbot Point or new export terminal to be established at Dudgeon Point. However, this would require new major water and power supply infrastructure to service the mine and port - hence, the extremely high cost. Because mining areas usually involve desolate areas where there is no direct risk to developed regions where the populace thrives, setting up new major water and power supplies would simply demand costs as high as the estimated cost - but this is not the only major percent of the whole budget of the Central Qld Coal Project.

The location for the Central Qld Coal Project is situated 40km northwest of Alpha, approximately 450 km west of Rockhampton and contains an amount of more than three billion tons. The proposed open-cut mine of the Central Qld Coal Project is expected to be developed in stages. It shall have an initial export capacity of 30 million tons per annum with a mine life expectancy of 30 years.

In terms of employment regarding Central Qld Coal Project, there will be around a total of 2,500 people to be employed during the construction and 1,600 permanent positions shall be employed in the operation stage of the Central Qld Coal Project.

Australia is a major coal exporter - the largest exporter of coal and fourth largest producer of coal. Australia is also the second largest producer of gold, second only to China. As for Opal, Australia is responsible for 95% of its production, thereby making her the largest producer worldwide. Australia would not also lose in terms of commercially viable diamond deposits - being third next after Russia and Botswana. This pretty much explains the significance of the mining industry to Australia. It is like the backbone of its economy; an industry focused on claiming the blessings the earth has giver her lands. The Central Qld Coal Project was made to further the exports and improve the trade. However, the Central Qld Coal Project requires quite a large sum for its project. It is only through the financial support of investments, both local and international, can it achieve its goals and begin reaping the fruits of the land.

Source: http://ezinearticles.com/?Central-Qld-Coal:-Mining-for-Needed-Investments&id=6314576

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/

Affordable Tooth Extractions

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

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

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

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

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

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

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

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

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 R-bloggers with Python – Part 2

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

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

from BeautifulSoup import BeautifulSoup

import os
import re

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

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

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

listing = os.listdir(path)

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

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

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

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

os.chdir(path)
data = {}

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

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

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

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

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

data.update({key:{}})

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

keys = data.keys()

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

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

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

output.close()

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

from BeautifulSoup import BeautifulSoup

import os
import re

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

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

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

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

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

Data Mining and Frequent Datasets

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

My question is

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

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

    (i) Find all frequent itemsets.

Here is how I worked it out:

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

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

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

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

data mining

Nanor

3 Answers

There are two ways to solve the problem:

    using Apriori algorithm
    Using FP counting

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

The algorithm is simple:

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

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

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

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

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

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

141

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

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

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

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

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

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

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

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

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

1

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

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

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

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

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

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

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

4 Data Mining Tips to Scrap Real Estate Data; Innovative Way to Give Realty Business a boost!

Internet has become a huge source of data – in fact; it has turned into a goldmine for the marketers, from where they can easily dig the useful data!

    Web scraping has become a norm in today’s competitive era, where one with maximum and relevant information wins the race!

Real Estate Data Extraction and Scraping Service

It has helped many industries to carve a niche in the market; especially real estate – Scraping real estate data has been of great help for professionals to reach out to a large number of people and gather reliable property data. However, there are some people for whom web scraping is still an alien concept; most probably because most of its advantages are not discussed.

    There are institutions, companies and organizations, entrepreneurs, as well as just normal citizens generating an extraordinary amount of information every day. Property information extraction can be effectively used to get an idea about the customer psyche and even generate valuable lead to further the business.

In addition to this, data mining has also some of following uses making it an indispensable part of marketing.

Gather Properties Details from Different Geographical Locations

You are an estate agent and want to expand your business to the neighboring city or state. But, then you are short of information. You are completely aware of the properties in the vicinity and in your town; however, with data mining services will help you to get an idea about the properties in the other state. You can also approach probable clients and increase your database to offer extensive services.

Online Offers and Discounts are just a Click Away

Now, it is tough to deal with the clients, show them the property of their choice and again act as a mediator between the buyer and seller. In all this, it becomes almost difficult to take a look at some special discounts or offers. With the data mining services, you can get an insight into these amazing offers. Thus, you can plan a move or even provide your client an amazing deal.

What people are talking about – Easy Monitoring of your Online Reputation

Internet has become a melting pot where different people come together. In fact, it provides a huge platform where people discuss about their likes and dislikes. When you dig into such online forums, you can get an idea of reputation that you or your firm holds. You can know what people think about you and where you require to buck up and where you need to slow down.

A Chance to Know your Competitors Better!

Last, but not the least, you can keep an eye on the competitor.  Real Estate is getting more competitive; and therefore, it is important to have knowledge about your competitors to get an upper hand. It will help you to plan your moves and strategize with more ease. Moreover, you also know what is that “something” that your competitor does not have and you have, with can be subtly highlighted.

Property information extraction can prove to be the most fruitful method to get a cutting edge in the industry.

Source: http://www.hitechbposervices.com/blog/4-data-mining-tips-to-scrap-real-estate-data-innovative-way-to-give-realty-business-a-boost/

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/

Big Data Democratization via Web Scraping

Big Data Democratization via Web Scraping

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

Big-data-crawling

Big Data web Crawling

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

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

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

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

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

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

Steps in Data Acquisition Pipeline

crawl-extract-norm

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

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

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

Why Businesses Need Data Scraping Service?

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

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

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

Data Scraping Plan and Solutions

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

Why Data Scraping is Highly Essential for Businesses?

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

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

Reasons Why Businesses Opt. For Website Data Scraping Solutions:

Website Scraping
Demand For New Data:

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

ImageCredit:3idatascraping.com
Capitalize On Hot Updates:

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

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

Analyzing Future Demands:

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

Appraising Business:

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

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

Example of Scraping with Selenium WebDriver in C#

In this article I will show you how it is easy to scrape a web site using Selenium WebDriver. I will guide you through a sample project which is written in C# and uses WebDriver in conjunction with the Chrome browser to login on the testing page and scrape the text from the private area of the website.

Downloading the WebDriver

First of all we need to get the latest version of Selenium Client & WebDriver Language Bindings and the Chrome Driver. Of course, you can download WebDriver bindings for any language (Java, C#, Python, Ruby), but within the scope of this sample project I will use the C# binding only. In the same manner, you can use any browser driver, but here I will use Chrome.

After downloading the libraries and the browser driver we need to include them in our Visual

Studio solution:

VS Solution

Creating the scraping program

In order to use the WebDriver in our program we need to add its namespaces:

using OpenQA.Selenium;
using OpenQA.Selenium.Chrome;
using OpenQA.Selenium.Support.UI;

Then, in the main function, we need to initialize the Chrome Driver:

using (var driver = new ChromeDriver())

{

 This piece of code searches for the chromedriver.exe file. If this file is located in a directory different from the directory where our program is executed, then we need to specify explicitly its path in the ChromeDriver constructor.

When an instance of ChromeDriver is created, a new Chrome browser will be started. Now we can control this browser via the driver variable. Let’s navigate to the target URL first:

driver.Navigate().GoToUrl("http://testing-ground.scraping.pro/login");

Then we can find the web page elements needed for us to login in the private area of the website:

var userNameField = driver.FindElementById("usr");
var userPasswordField = driver.FindElementById("pwd");
var loginButton = driver.FindElementByXPath("//input[@value='Login']");

Here we search for user name and password fields and the login button and put them into the corresponding variables. After we have found them, we can type in the user name and the password  and press the login button:

userNameField.SendKeys("admin");
userPasswordField.SendKeys("12345");
loginButton.Click();

At this point the new page will be loaded into the browser, and after it’s done we can scrape the text we need and save it into the file:

var result = driver.FindElementByXPath("//div[@id='case_login']/h3").Text;

File.WriteAllText("result.txt", result);

That’s it! At the end, I’d like to give you a bonus – saving a screenshot of the current page into a

file:

driver.GetScreenshot().SaveAsFile(@"screen.png", ImageFormat.Png);

The complete program listing

using System.IO;
using System.Text;
using OpenQA.Selenium;
using OpenQA.Selenium.Chrome;
using OpenQA.Selenium.Support.UI;

namespace WebDriverTest
{
    class Program
    {
        static void Main(string[] args)
        {
            // Initialize the Chrome Driver
            using (var driver = new ChromeDriver())
            {
                // Go to the home page
                driver.Navigate().GoToUrl("http://testing-ground.scraping.pro/login");

                // Get the page elements
                var userNameField = driver.FindElementById("usr");
                var userPasswordField = driver.FindElementById("pwd");
                var loginButton = driver.FindElementByXPath("//input[@value='Login']");

                // Type user name and password
                userNameField.SendKeys("admin");
                userPasswordField.SendKeys("12345");

                // and click the login button
                loginButton.Click();

                // Extract the text and save it into result.txt
                var result = driver.FindElementByXPath("//div[@id='case_login']/h3").Text;
                File.WriteAllText("result.txt", result);

                // Take a screenshot and save it into screen.png
                driver.GetScreenshot().SaveAsFile(@"screen.png", ImageFormat.Png);
            }
        }
    }
}

Also you can download a ready project here.

Conclusion

I hope you are impressed with how easy it is to scrape web pages using the WebDriver. You can naturally press keys and click buttons as you would in working with the browser. You don’t even need to understand what kind of HTTP requests are sent and what cookies are stored; the browser does all this for you. This makes the WebDriver a wonderful tool in the hands of a web scraping specialist.

Source:http://scraping.pro/example-of-scraping-with-selenium-webdriver-in-csharp/

Web Scraping: Business Intelligence

Web scraping is simply getting of information that is both hidden and unhidden from the internet. Web scraping is one of the latest technologies used in harvesting data from WebPages. It has been used to extract useful information for practical and beneficial applications and its interpretation has been tested in decision making. Web scraping is a new term that overshadows the traditional data harvesting technique that was used before. It has been regarded as knowledge discovery in databases for research and even marketing monitoring.

This article explores the various business intelligence ways in which web scraping can be used to be of importance.

Web scraping services has been used by many companies that have a strong customer focus. These companies range from sectors like retail, financial services, and marketing and communication organizations. It quite important to realize that web scraping has great signifies and impact in the varied commercial applications for the better understanding and prediction of the critical data. The data may range from stocks to consumer behaviors. The consumer behaviors are better shown in trends like customer profiles, purchasing and industry analysis among others.

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

Web Scraping Popularity Soars

The world is stirred because of the ever-growing web scraping success in almost all of its services. Success stories pertaining to the benefits of online data collection in business, research, politics, health, and almost all aspects of human life are endless. With this popularity surge, it has become a hot issue and many are questioning its legality and reliability.

Looking back, this simple harvesting of pertinent data from competitors and the global market in general like anything else started as a non-threatening and advanced form of web research. Eventually, when the benefits begin to manifest and the system improves, many are lured into it that it has become one of the strongest and fastest growing business in the world.

Simple Beginnings

As naturally as a law of life that great things come from small beginnings, data mining was conceived as a process in gaining information, mostly in research. This act of collecting information through the internet was never imagined to be what it has become nowadays.

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

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

A good web data extraction/screen scraper program?

I need to capture product data from a site on a regular basis and wondered if any one knows of a good software program? I've trialed Mozenda but its a monthly subscription and pricey in the long term. Obviously something thats free would be best but I don't mind paying either. Just need a decent program thats reliable and doesn't require much programming knowledge.

You can try ScraperWiki.com if you know python.

I've experimented with Screen-Scraper and found it easy to use. The application comes in multiple versions: basic (which is free), professional, and enterprise. Also, multiple platforms are supported.

Hire a programmer to do it so that there is only a one off cost. I often see similar projects on freelancing websites like Elance and oDesk.

I really like iMacros. You can give it a test drive to see if it meets your needs with the totally free Firefox extension (there's also IE versions), but there are also more full featured application and "server" versions that have more features and ability to do thing in an unattended manner.

Here are some other alternatives to consider:

    License the data from the provider. Call em up and ask 'em.

    Use Amazon Mechanical Turk to get humans to copy and paste and format it for ya. They are cheap.

    For automation, it depends on how complicated the HTML is and how often it changes. You could use Excel's Web Data Import if it's really simple.


You can use irobot from IRobotSoft, which is totally free, and provides more functionalityies than other paid software. Watch demos here http://irobotsoft.com/help/ for how simple it is.

Questions on their forum were answered very quickly.


Source: http://stackoverflow.com/questions/2334164/a-good-web-data-extraction-screen-scraper-program

How to login to website and extract data using PHP [closed]

I have installed the tiny tiny rss on to my computer (Windows) and also have Xampp installed (localhost).

I want to be able to use PHP to extract data from the Tiny tiny RSS webpage.

I have tried this it which just opens the front page:

<?php
$homepage = file_get_contents('my install tiny tiny rss url');
echo $homepage;
?>

But how do I login and extract the data.

You can use cURL to send post data and headers. To login you need to replicate the exact data exchange between the client and the server.


SOurce: http://stackoverflow.com/questions/20611918/how-to-login-to-website-and-extract-data-using-php