Data Scraping Services with Proxy Data Scraping

Have you ever heard of "data scraping? Data Scraping is the process of gathering relevant information in the public domain on the internet (private areas even if the conditions are met) and stored in databases or spreadsheets for later use in various applications. Scraping data technology is not new and a successful businessman his fortune by using data scraping technology.

Sometimes owners of sites that are not derived much pleasure from the automated harvesting of their data. Webmasters have learned to deny access to web scrapers their websites using tools or methods that some IP addresses to block the content of the site here. scrapers data is left to either target a different site, or the script to move the harvest of a computer using a different IP address each time and get as much information as possible to "all computers finally blocked the nozzle.

Fortunately, there is a modern solution to this problem. Proxy data scraping technology solves the problem by using a proxy IP addresses. When your data scraping program performs an extraction of a website, the site thinks that it comes from a different IP address. For site owner, proxies just like scratching a short period of increased traffic around the world. They have very limited resources and tedious to block such a scenario, but more importantly - for the most part, they simply do not know they are scraped.

Now you can ask. "Where can I proxy data scraping technology for my project" The "do-it-yourself solution is free, unfortunately, not easy at all Creation of a database scraping proxy network takes time and requires you to either a group of IP addresses and servers can be used in place yet, the computer guru you need to call to get everything configured. You may consider hiring proxy servers hosting providers to select, but this option is usually quite expensive, but probably better than the alternative: dangerous and unreliable servers (but free) public proxy.

There are literally thousands of free proxy servers located all over the world are fairly easy to use. The trick is to find them. Hundreds of sites, list servers, but by placing a functioning, open and supports standard protocols that you need to a lesson in perseverance, trial and error will be. However, if you manage to find a working public representatives, there are dangers inherent in their use. First, you do not know who owns the server or activities taking place elsewhere on the server. Send applications or sensitive data via an open proxy is a bad idea. It's easy enough for a proxy server to keep all information you send or send it back to you to catch. If you choose the method of replacing the public, make sure you never a transaction through which you or anyone else would jeopardize the case of unsavory types are made aware of the data to send.

A less risky scenario for data scraping proxy is to hire a proxy connection that runs through the rotation of a large number of private IP addresses. There are a number of these companies available that claim to remove all Web logs, which you harvest anonymously on the web with a minimal threat of retaliation. Companies such as enterprise solutions offer a large http://www.Anonymizer.com anonymous proxy, but often carry significant costs of installing enough for you to continue.

The other advantage is that companies that own such networks can often help design and implement a set of proxy data scraping custom program instead of trying to work with a generic bone scraping. After performing a simple Google search, I quickly found a company (www.ScrapeGoat.com) that an anonymous proxy server provides for data scraping purposes. Or, according to their website, if you want to make life even easier, scrap goat can retrieve data for you and a variety of different formats to deliver, often before you could finish up your plate from the scraping program.

Whatever path you choose for your data scraping proxy need not let a few simple tips to thwart access to all the wonderful information that is stored on the World Wide Web!

Source:http://www.articlesbase.com/small-business-articles/data-scraping-services-with-proxy-data-scraping-4697825.html

How to scrape address from Google Maps

If you want to build a new online directory based website and want it to be popular with latest web contents, then you need the help of web scraping services from iWeb scraping. If you want to scrape address from maps.google.com, there is a specialized web scraping tool developed by iWeb scraping which can do the job for you. There are plenty of benefits with web scraping which includes market research, gathering customer information, managing product catalogs, compare prices, gather real estate data, gather job posting information etc. Web scraping technology is very popular nowadays and it saves lot of time and effort involved in manual extraction of data from websites.

The web scraping tools developed iWeb Scraping is very user-friendly and can extract specific information from targeted websites. It converts data from HTML web pages to useful formats like Excel spread sheets or Access database. Whatever web scraping requirements you have, you can contact iWeb Scraping as they have more than 3.5 years of web data extraction experience and offer the best prices in the industry. Also their services are available in 24x7 basis and free pilot projects will be done based on request.

Companies which require specific web data and look for an application which can automate the process and export the HTML data in structured format could benefit greatly from web scraping applications of iWeb scraping. You can easily extract data from multiple target websites, parse and re-assemble the information in HTML format to database or spread sheets as you wish. The application has simple point-and-click user-interface and any beginner can use it scrape address from Google Maps. If you want to gather address of people in particular region from Google maps, you can do it with help of web scraping application developed by iWebscraping.

Web Scraping is a technology that able to digest target website databases that are visible only as HTML web pages, and create a local, identical replica of those databases as a information or result. With our web scraping & web data extraction service we can capture web pages, then pin-point specific pieces of data/information you'd like to extract from web pages. What is needed in this process is much more than a Website crawler and set of Website wrappers. The time required to do web data extraction goes down in comparison to manually data copying and pasting job.

Source:http://www.articlesbase.com/information-technology-articles/how-to-scrape-address-from-google-maps-4683906.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.

Related Articles

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

Scraping Fantasy Football Projections from the Web

In this post, I show how to download fantasy football projections from the web using R.  In prior posts, I showed how to scrape projections from ESPN, CBS, NFL.com, and FantasyPros.  In this post, I compile the R scripts for scraping projections from these sites, in addition to the following sites: Accuscore, Fantasy Football Nerd, FantasySharks, FFtoday, Footballguys, FOX Sports, WalterFootball, and Yahoo.

Why Scrape Projections?

Scraping projections from multiple sources on the web allows us to automate importing the projections with a simple script.  Automation makes importing more efficient so we don’t have to manually download the projections whenever they’re updated.  Once we import all of the projections, there’s a lot we can do with them, like:

•    Determine who has the most accurate projections
•    Calculate projections for your league
•    Calculate players’ risk levels
•    Calculate players’ value over replacement
•    Identify sleepers
•    Calculate the highest value you should bid on a player in an auction draft
•    Draft the best starting lineup
•    Win your auction draft
•    Win your snake draft

The R Scripts

To scrape the projections from the websites, I use the readHTMLTable function from the XML package in R.  Here’s an example of how to scrape projections from FantasyPros:

1 2 3 4 5 6 7 8    

#Load libraries

library("XML")

#Download fantasy football projections from FantasyPros.com

qb_fp <- readHTMLTable("http://www.fantasypros.com/nfl/projections/qb.php", stringsAsFactors = FALSE)$data

rb_fp <- readHTMLTable("http://www.fantasypros.com/nfl/projections/rb.php", stringsAsFactors = FALSE)$data

wr_fp <- readHTMLTable("http://www.fantasypros.com/nfl/projections/wr.php", stringsAsFactors = FALSE)$data

te_fp <- readHTMLTable("http://www.fantasypros.com/nfl/projections/te.php", stringsAsFactors = FALSE)$data

view raw FantasyPros projections hosted with ? by GitHub

The R Scripts for scraping the different sources are located below:

1.    Accuscore
2.    CBS - Jamey Eisenberg
3.    CBS – Dave Richard
4.    CBS – Average
5.    ESPN
6.    Fantasy Football Nerd
7.    FantasyPros
8.    FantasySharks
9.    FFtoday
10.    Footballguys – David Dodds
11.    Footballguys – Bob Henry
12.    Footballguys – Maurile Tremblay
13.    Footballguys – Jason Wood
14.    FOX Sports
15.    NFL.com
16.    WalterFootball
17.    Yahoo

Density Plot

Below is a density plot of the projections from the different sources:Calculate projections

Conclusion

Scraping projections from the web is fast, easy, and automated with R.  Once you’ve downloaded the projections, there’s so much you can do with the data to help you win your league!  Let me know in the comments if there are other sources you want included (please provide a link).

Source:http://fantasyfootballanalytics.net/2014/06/scraping-fantasy-football-projections.html

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

Benefits of Predictive Analytics and Data Mining Services

Predictive Analytics is the process of dealing with variety of data and apply various mathematical formulas to discover the best decision for a given situation. Predictive analytics gives your company a competitive edge and can be used to improve ROI substantially. It is the decision science that removes guesswork out of the decision-making process and applies proven scientific guidelines to find right solution in the shortest time possible.

Predictive analytics can be helpful in answering questions like:

•    Who are most likely to respond to your offer?
•    Who are most likely to ignore?
•    Who are most likely to discontinue your service?
•    How much a consumer will spend on your product?
•    Which transaction is a fraud?
•    Which insurance claim is a fraudulent?
•    What resource should I dedicate at a given time?

Benefits of Data mining include:

•    Better understanding of customer behavior propels better decision
•    Profitable customers can be spotted fast and served accordingly
•    Generate more business by reaching hidden markets
•    Target your Marketing message more effectively
•    Helps in minimizing risk and improves ROI.
•    Improve profitability by detecting abnormal patterns in sales, claims, transactions etc
•    Improved customer service and confidence
•    Significant reduction in Direct Marketing expenses

Basic steps of Predictive Analytics are as follows:

•    Spot the business problem or goal
•    Explore various data sources such as transaction history, user demography, catalog details, etc)
•    Extract different data patterns from the above data
•    Build a sample model based on data & problem
•    Classify data, find valuable factors, generate new variables
•    Construct a Predictive model using sample
•    Validate and Deploy this Model

Standard techniques used for it are:

•    Decision Tree
•    Multi-purpose Scaling
•    Linear Regressions
•    Logistic Regressions
•    Factor Analytics
•    Genetic Algorithms
•    Cluster Analytics
•    Product Association

Should you have any queries regarding Data Mining or Predictive Analytics applications, please feel free to contact us. We would be pleased to answer each of your queries in detail.

Source:http://ezinearticles.com/?Benefits-of-Predictive-Analytics-and-Data-Mining-Services&id=4766989

Do blog scraping sites violate the blog owner's copyright?

I noticed that my blog has been posted on one of these website scraping sites. This is the kind of site that has no original content, but just repeats or scrapes content others have written and does it to get some small amount of ad income from ads on the scraping site. In essence the scraping site is taking advantage of the content of the originating site in order to make a few dollars from people who go to the site looking for something else. Some of these websites prey on misspelling. If you accidentally misspell the name of an original site, you just may end up with one of these patently commercial scraping sites.

Google defines scraping as follows:

•    Sites that copy and republish content from other sites without adding any original content or value
•    Sites that copy content from other sites, modify it slightly (for example, by substituting synonyms or using automated techniques), and republish it
•    Sites that reproduce content feeds from other sites without providing some type of unique organization or benefit to the user

My question, as set out in the title to this post, is whether or not scraping is a violation of copyright. It turns out that the answer is likely very complicated.  You have to look at the definition of a scraping site very carefully. Let me give you some hypotheticals to show what I mean.

Let's suppose that I write a blog and put a link in my blog post to your blog. Does that link violate your copyright? I can't imagine that anyone would think that there was problem with linking to another website on the Web. In this case, there is no content from the originating site, just a link.

But let's carry the hypothetical a little further. What if I put a link to your site and quote some of your content? Does this violate copyright law? If you are acquainted with any of the terminology of copyright law; think fair use. The issue here is whether or not the "quoted" material is a substantial reproduction of the entire original content? I would have the opinion that duplicating an entire blog post either with or without attribution would be a violation of the originator's copyright.

So is the scraping website protected by the "fair use" doctrine? Does the fact that the motivation for listing the original websites is to make money have anything to do with how you would decide if there was or was not a violation of the originator's copyright? By the way, the copyright does not make a distinction between a commercial and non-commercial use of the original constituting or not constituting a violation of copyright. The fact that the reproducing (scraping) party does not make money from the reproduction is not a factor in the issue of violation, although it may ultimately be an issue as to the amount of damages assessed.

Does the fact that the actions of the scraper annoy me, make any difference? I would answer, not in the least. Whether or not you are annoyed by the violation of the copyright makes no difference as to whether or not there is a violation. Likewise, you have no independent claims for your wounded feelings because of the copied content. Copyright is a statutory action (i.e. based on statutory law) and unless the cause of action is recognized by the law, there is no cause of action. Now, in an outrageous case, you may have  some kind of tort (personal injury) claim, but that is way outside of my hypothetical situation.

So what is the answer? Does scraping violate the originator's copyright? If only a small portion of the blog is copied (scraped) then I would have to have the opinion that it is not. Essentially, no matter what the motivation of the scrapper, there is not enough content copied to violate the fair use doctrine. Now, that is my opinion. Your's might differ. That is what makes lawsuits.

Do I think there are other reasons why scraping websites are objectionable? Certainly, but those reasons have nothing to do with copyright and they are probably the subject of another different blog post. So, if you are reading this from scraping website, bear in mind that there may be a serious problem with that type of website.

Source:http://genealogysstar.blogspot.in/2013/05/do-blog-scraping-sites-violate-blog.html

Handling exceptions in scrapers

When requesting and parsing data from a source with unknown properties and random behavior (in other words, scraping), I expect all kinds of bizarrities to occur. Managing exceptions is particularly helpful in such cases.

Here is some ways that an exception might be raised.
[][0] #The list has no zeroth element, so this raises an IndexError
{}['foo'] #The dictionary has no foo element, so this raises a KeyError

Catching the exception is sometimes cleaner than preventing it from happening in the first place. Here are some examples handling bizarre exceptions in scrapers.

Example 1: Inconsistant date formats

Let’s say we’re parsing dates.
import datetime
This doesn’t raise an error.
datetime.datetime.strptime('2012-04-19', '%Y-%m-%d')
But this does.
datetime.datetime.strptime('April 19, 2012', '%Y-%m-%d')

It raises a ValueError because the date formats don’t match. So what do we do if we’re scraping a data source with multiple date formats?

Ignoring unexpected date formats

A simple thing is to ignore the date formats that we didn’t expect.

import lxml.html
import datetime
def parse_date1(source):
    rawdate = lxml.html.fromstring(source).get_element_by_id('date').text
    try:
         cleandate = datetime.datetime.strptime(rawdate, '%Y-%m-%d')
    except ValueError:
         cleandate = None
    return cleandate

print parse_date1('<div id="date">2012-04-19</div>')

If we make a clean date column in a database and put this in there, we’ll have some rows with dates and some rows with nulls. If there are only a few nulls, we might just parse those by hand.

Trying multiple date formats

Maybe we have determined that this particular data source uses three different date formats. We can try all three.

import lxml.html
import datetime

def parse_date2(source):

    rawdate = lxml.html.fromstring(source).get_element_by_id('date').text

    for date_format in ['%Y-%m-%d', '%B %d, %Y', '%d %B, %Y']:

        try:
             cleandate = datetime.datetime.strptime(rawdate, date_format)
             return cleandate
        except ValueError:
             pass
    return None

print parse_date2('<div id="date">19 April, 2012</div>')

This loops through three different date formats and returns the first one that doesn’t raise the error.

Example 2: Unreliable HTTP connection

If you’re scraping an unreliable website or you are behind an unreliable internet connection, you may sometimes get HTTPErrors or URLErrors for valid URLs. Trying again later might help.

import urllib2
def load(url):
    retries = 3
    for i in range(retries):
        try:
            handle = urllib2.urlopen(url)
            return handle.read()
        except urllib2.URLError:
            if i + 1 == retries:
                raise
            else:
                time.sleep(42)
    # never get here

print load('http://thomaslevine.com')

This function tries to download the page thee times. On the first two fails, it waits 42 seconds and tries again. On the third failure, it raises the error. On a success, it returs the content of the page.

Example 3: Logging errors rather than raising them

For more complicated parses, you might find loads of errors popping up in weird places, so you might want to go through all of the documents before deciding which to fix first or whether to do some of them manually.

import scraperwiki
for document_name in document_names:
    try:
        parse_document(document_name)
    except Exception as e:
        scraperwiki.sqlite.save([], {
            'documentName': document_name,
            'exceptionType': str(type(e)),
            'exceptionMessage': str(e)
        }, 'errors')

This catches any exception raised by a particular document, stores it in the database and then continues with the next document. Looking at the database afterwards, you might notice some trends in the errors that you can easily fix and some others where you might hard-code the correct parse.

Example 4: Exiting gracefully

When I’m scraping over 9000 pages and my script fails on page 8765, I like to be able to resume where I left off. I can often figure out where I left off based on the previous row that I saved to a database or file, but sometimes I can’t, particularly when I don’t have a unique index.


for bar in bars:
    try:
        foo(bar)
    except:
        print('Failure at bar = "%s"' % bar)
        raise

This will tell me which bar I left off on. It’s fancier if I save the information to the database, so here is how I might do that with ScraperWiki.

import scraperwiki
resume_index = scraperwiki.sqlite.get_var('resume_index', 0)
for i, bar in enumerate(bars[resume_index:]):
    try:
        foo(bar)
    except:
        scraperwiki.sqlite.save_var('resume_index', i)
        raise
scraperwiki.sqlite.save_var('resume_index', 0)

ScraperWiki has a limit on CPU time, so an error that often concerns me is the scraperwiki.CPUTimeExceededError. This error is raised after the script has used 80 seconds of CPU time; if you catch the exception, you have two CPU seconds to clean up. You might want to handle this error differently from other errors.

import scraperwiki
resume_index = scraperwiki.sqlite.get_var('resume_index', 0)
for i, bar in enumerate(bars[resume_index:]):
    try:
        foo(bar)
    except scraperwiki.CPUTimeExceededError:
        scraperwiki.sqlite.save_var('resume_index', i)
    except Exception as e:
        scraperwiki.sqlite.save_var('resume_index', i)
        scraperwiki.sqlite.save([], {
            'bar': bar,
            'exceptionType': str(type(e)),
            'exceptionMessage': str(e)
        }, 'errors')
scraperwiki.sqlite.save_var('resume_index', 0)

tl;dr

Expect exceptions to occur when you are scraping a randomly unreliable website with randomly inconsistent content, and consider handling them in ways that allow the script to keep running when one document of interest is bizarrely formatted or not available.

Source: https://blog.scraperwiki.com/2012/05/handling-exceptions-in-scrapers

A quick guide on web scraping: Why and how

Web scraping, which is the collection and cleaning of online data, is the first step in any
data-driven project. Here’s a short video that explains what scraping is, and how to create
automated scraping jobs using a digital tool.

This is a 15-minute video created by an instructor at Ohio State University. In the first six
minutes, the instructor talks about why we need web scraping; he then shows how to use a
scraping tool, OutWit Hub, to collect data scattered in a large database.

FYI: read reviews by Reporters’ Lab of OutWit Hub and other web scraping tools.

Source: http://www.mulinblog.com/quick-guide-web-scraping/

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

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

Some of the tools i use to do this are:

    Google Webmaster Tools
    Google Alerts

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

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

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

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

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

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

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

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

Google Alerts

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

Link Notifications

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

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

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

RSS Feed Links

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

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

Web Scraping’s 2013 Review – part 1

Here we are, almost having ended another year and having the chance to analyze the aspects of the Web scraping market over the last twelve months. First of all i want to underline all the buzzwords on the tech field as published in the Yahoo’s year in review article . According to Yahoo, the most searched items wore

•     iPhone (including 4, 5, 5s, 5c, and 6)
•     Samsung (including Galaxy, S4, S3, Note)
•     Siri
•     iPad Cases
•     Snapchat
•     Google Glass
•     Apple iPad
•     BlackBerry Z10
•     Cloud Computing

It’s easy to see that none of this terms regards in any way with the field of data mining, and they rather focus on the gadgets and apps industry, which is just one of the ways technology can evolve to. Regarding actual data mining industry there were a lot of talks about it in this year’s MIT’s Engaging Data 2013 Conference. One of the speakers Noam Chomsky gave an acid speech relating data extraction and its connection to the Big Data phenomena that is also on everyone’s lips this year. He defined a good way to see if Big Data works by following a series of few simple factors: 1. It’s the analysis, not the raw data, that counts. 2. A picture is worth a thousand words 3. Make a big data portal (Not sure if Facebook is planning on dominating in cloud services some day) 4. Use a hybrid organizational model (We’re asleep already, soon)  let’s move 5. Train employees Other interesting declaration  was given by EETimes saying, “Data science will do more for medicine in the next 10 years than biological science.” which says a lot about the volume of required extracted data.

Because we want to cover as many as possible events about data mining this article will be a two parter, so don’t forget to check our blog tomorrow when the second part of this article will come up!

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

Scraping XML Tables with R

A couple of my good friends also recently started a sports analytics blog. We’ve decided to collaborate on a couple of studies revolving around NBA data found at www.basketball-reference.com. This will be the first part of that project!

Data scientists need data. The internet has lots of data. How can I get that data into R? Scrape it!

People have been scraping websites for as long as there have been websites. It’s gotten pretty easy using R/Python/whatever other tool you want to use. This post shows how to use R to scrape the demographic information for all NBA and ABA players listed at www.basketball-reference.com.

Here’s the code:

###### Settings

library(XML)

 ###### URLs

url<-paste0("http://www.basketball-reference.com/players/",letters,"/")

len<-length(url)

 ###### Reading data

tbl<-readHTMLTable(url[1])[[1]]

 for (i in 2:len)

    {tbl<-rbind(tbl,readHTMLTable(url[i])[[1]])}

 ###### Formatting data

colnames(tbl)<-c("Name","StartYear","EndYear","Position","Height","Weight","BirthDate","College")

tbl$BirthDate<-as.Date(tbl$BirthDate[1],format="%B %d, %Y")

Created by Pretty R at inside-R.org

And here’s the result:Result

Source: http://www.r-bloggers.com/scraping-xml-tables-with-r/

Web Scraping Tools for Non-developers

I recently spoke with a resource-limited organization that is investigating government corruption and wants to access various public datasets to monitor politicians and law firms. They don’t have developers in-house, but feel pretty comfortable analyzing datasets in CSV form. While many public datasources are available in structured form, some sources are hidden in what us data folks call the deep web. Amazon is a nice example of a deep website, where you have to enter text into a search box, click on a few buttons to narrow down your results, and finally access relatively structured data (prices, model numbers, etc.) embedded in HTML. Amazon has a structured database of their products somewhere, but all you get to see is a bunch of webpages trapped behind some forms.

A developer usually isn’t hindered by the deep web. If we want the data on a webpage, we can automate form submissions and key presses, and we can parse some ugly HTML before emitting reasonably structured CSVs or JSON. But what can one accomplish without writing code?

This turns out to be a hard problem. Lots of companies have tried, to varying degrees of success, to build a programmer-free interface for structured web data extraction. I had the pleasure of working on one such project, called Needlebase at ITA before Google acquired it and closed things down. David Huynh, my wonderful colleague from grad school, prototyped a tool called Sifter that did most of what one would need, but like all good research from 2006, the lasting impact is his paper rather than his software artifact.

Below, I’ve compiled a list of some available tools. The list comes from memory, the advice of some friends that have done this before, and, most productively, a question on Twitter that Hilary Mason was nice enough to retweet.

The bad news is that none of the tools I tested would work out of the box for the specific use case I was testing. To understand why, I’ll break down the steps required for a working web scraper, and then use those steps to explain where various solutions broke down.

The anatomy of a web scraper

There are three steps to a structured extraction pipeline:

    Authenticate yourself. This might require logging in to a website or filling out a CAPTCHA to prove you’re not…a web scraper. Because the source I wanted to scrape required filling out a CAPTCHA, all of the automated tools I’ll review below failed step 1. It suggests that as a low bar, good scrapers should facilitate a human in the loop: automate the things machines are good at automating, and fall back to a human to perform authentication tasks the machines can’t do on their own.

    Navigate to the pages with the data. This might require entering some text into a search box (e.g., searching for a product on Amazon), or it might require clicking “next” through all of the pages that results are split over (often called pagination). Some of the tools I looked at allowed entering text into search boxes, but none of them correctly handled pagination across multiple pages of results.

    Extract the data. On any page you’d like to extract content from, the scraper has to help you identify the data you’d like to extract. The cleanest example of this that I’ve seen is captured in a video for one of the tools below: the interface lets you click on some text you want to pluck out of a website, asks you to label it, and then allows you to correct mistakes it learns how to extract the other examples on the page.

As you’ll see in a moment, the steps at the top of this list are hardest to automate.

What are the tools?

Here are some of the tools that came highly recommended, and my experience with them. None of those passed the CAPTCHA test, so I’ll focus on their handling of navigation and extraction.

    Web Scraper is a Chrome plugin that allows you to build navigable site maps and extract elements from those site maps. It would have done everything necessary in this scenario, except the source I was trying to scrape captured click events on links (I KNOW!), which tripped things up. You should give it a shot if you’d like to scrape a simpler site, and the youtube video that comes with it helps get around the slightly confusing user interface.

    import.io looks like a clean webpage-to-api story. The service views any webpage as a potential data source to generate an API from. If the page you’re looking at has been scraped before, you can access an API or download some of its data. If the page hasn’t been processed before, import.io walks you through the process of building connectors (for navigation) or extractors (to pull out the data) for the site. Once at the page with the data you want, you can annotate a screenshot of the page with the fields you’d like to extract. After you submit your request, it appears to get queued for extraction. I’m still waiting for the data 24 hours after submitting a request, so I can’t vouch for the quality, but the delay suggests that import.io uses crowd workers to turn your instructions into some sort of semi-automated extraction process, which likely helps improve extraction quality. The site I tried to scrape requires an arcane combination of javascript/POST requests that threw import.io’s connectors for a lo
op, and ultimately made it impossible to tell import.io how to navigate the site. Despite the complications, import.io seems like one of the more polished website-to-data efforts on this list.

    Kimono was one of the most popular suggestions I got, and is quite polished. After installing the Kimono bookmarklet in your browser, you can select elements of the page you wish to extract, and provide some positive/negative examples to train the extractor. This means that unlike import.io, you don’t have to wait to get access to the extracted data. After labeling the data, you can quickly export it as CSV/JSON/a web endpoint. The tool worked seamlessly to extract a feed from the Hackernews front page, but I’d imagine that failures in the automated approach would make me wish I had access to import.io’s crowd workers. The tool would be high on my list except that navigation/pagination is coming soon, and will ultimately cost money.

    Dapper, which is now owned by Yahoo!, provides about the same level of scraping capabilities as Kimono. You can extract content, but like Kimono it’s unclear how to navigate/paginate.

    Google Docs was an unexpected contender. If the data you’re extracting is in an HTML table/RSS Feed/CSV file/XML document on a single webpage with no navigation/authentication, you can use one of the Import* functions in Google Docs. The IMPORTHTML macro worked as advertised in a quick test.

    iMacros is a tool that I could imagine solves all of the tasks I wanted, but costs more than I was willing to pay to write this blog post. Interestingly, the free version handles the steps that the other tools on this list don’t do as well: navigation. Through your browser, iMacros lets you automate filling out forms, clicking on “next” links, etc. To perform extraction, you have to pay at least $495.

    A friend has used Screen-scraper in the past with good outcomes. It handles navigation as well as extraction, but costs money and requires a small amount of programming/tokenization skills.

    Winautomation seems cool, but it’s only available for Windows, which was a dead end for me.

So that’s it? Nothing works?

Not quite. None of these tools solved the problem I had on a very challenging website: the site clearly didn’t want to be crawled given the CAPTCHA, and the javascript-submitted POST requests threw most of the tools that expected navigation through links for a loop. Still, most of the tools I reviewed have snazzy demos, and I was able to use some of them for extracting content from sites that were less challenging than the one I initially intended to scrape.

All hope is not lost, however. Where pure automation fails, a human can step in. Several proposals suggested paying people on oDesk, Mechanical Turk, or CrowdFlower to extract the content with a human touch. This would certainly get us past the CAPTCHA and hard-to-automate navigation. It might get pretty expensive to have humans copy/paste the data for extraction, however. Given that the tools above are good at extracting content from any single page, I suspect there’s room for a human-in-the-loop scraping tool to steal the show: humans can navigate and train the extraction step, and the machine can perform the extraction. I suspect that’s what import.io is up to, and I’m hopeful they keep the tool available to folks like the ones I initially tried to help.

While we’re on the topic of human-powered solutions, it might make sense to hire a developer on oDesk to just implement the scraper for the site this organization was looking at. While a lot of the developer-free tools I mentioned above look promising, there are clearly cases where paying someone for a few hours of script-building just makes sense.

Source: http://blog.marcua.net/post/74655674340

Web Scraping for SEO with these Open-Source Scrapers

When conducting Search Engine Optimization (SEO), we’re required to scrape websites for data, our campaigns, and reports for our clients. At the lowest level we utilize scraping to keep track of rankings on search engines like Google, Bing, and Yahoo, even keep a track of links on websites to know when it’s completed its lifespan. Then we’ve used them to help us aggregate data from APIs, RSS feeds, and websites to conduct some of our data mining to find patterns to help us become more competitive. 

So scraping is a function majority of companies (SEOmoz, Raventools, and Google) have to do to either save money, protect intellectual property, track trends, etc… Businesses can find infinite uses with scraping tools, it just depends if you’re an printed circuit board manufacturer looking for ideas on your e-mail marketing campaign or a Orange County based business trying to keep an eye out on the competition. which is why we’ve created a comprehensive list of open source scrapers out there to help all the businesses out there. Just keep in mind we haven’t used all of them!

Words of caution, web scrapers require knowledge specific to the language such as PHP & cURL. Take into considerations issues like cookie management, fault tolerance, organizing the data properly, not crashing the website being scraped, and making sure the website doesn’t prohibit scraping.

If you’re ready, here’s the list…

Erlang

    eBot

Java

    Heritrix
    Nutch
    Piggy Bank
    WebSPHINX
    WebHarvest

PHP

    PHPCrawl
    Snoopy
    SpiderMonkey

Python

    BeautifulSoap
    HarvestMan
    Scrape.py
    Scrapemark
    Scrapy **
    Mechanize

Ruby

    Anemone
    scRUBYt

We’ll come back and update this list as we encounter more! If you would like to submit a solution we missed, feel free. Also we’re looking for guides related to each of these, so if you know of any or would be interested in guesting blogging about one, let us know!

Source:http://www.annexcore.com/blog/web-scraping-for-seo-with-these-open-source-scrapers/

Is Web Scraping Legal?

Web scraping might be one of the best ways to aggregate content from across the internet, but it comes with a caveat: It’s also one of the hardest tools to parse from a legal standpoint.

For the uninitiated, web scraping is a process whereby an automated piece of software extracts data from a website by “scraping” through the site’s many pages. While search engines like Google and Bing do a similar task when they index web pages, scraping engines take the process a step further and convert the information into a format which can be easily transferred over to a database or spreadsheet.

It’s also important to note that a web scraper is not the same as an API. While a company might provide an API to allow other systems to interact with its data, the quality and quantity of data available through APIs is typically lower than what is made available through web scraping. In addition, web scrapers provide more up-to-date information than APIs and are much easier to customize from a structural standpoint.

The applications of this “scraped” information are widespread. A journalist like Nate Silver might use scrapers to monitor baseball statistics and create numerical evidence for a new sports story he’s working on. Similarly, an eCommerce business might bulk scrape product titles, prices, and SKUs from other sites in order to further analyze them.

Legality of Web ScrapingWhile web scraping is an undoubtedly powerful tool, it’s still undergoing growing pains when it comes to legal matters. Because the scraping process appropriates pre-existing content from across the web, there are all kinds of ethical and legal quandaries that confront businesses who hope to do leverage scrapers for their own processes.

In this “wild west” environment, where the legal implications of web scraping are in a constant state of flux, it helps to get a foothold on where the legal needle currently falls. The following timeline outlines some of the biggest cases involving web scrapers in the United States, and allows us to achieve a greater understanding on the precedents that surround the court rulings.

Terms of Use Tug-of-War—2000-2009

For years after they first came into use, web scrapers went largely unchallenged from a legal standpoint. In 2000, however, the use of scrapers came under heavy and consistent fire when eBay fired the first shot against an auction data aggregator called Bidder’s Edge. In this very early case, eBay argued that Bidder’s Edge was using scrapers in a way that violated Trespass to Chattels doctrine. While the lawsuit was settled out of court, the judge upheld eBay’s original injunction, stating that heavy bot traffic could very well disrupt eBay’s service.

Then in 2003’s Intel Corp. v. Hamidi, the California Supreme court overturned the basis of eBay v. Bidder’s Edge, ruling that Trespass to Chattels could not extend to the context of computers if no actual damage to personal property occurred.

So in terms of legal action against web scraping, Tresspass to Chattels no longer applied, and things were back to square one. This began a period in which the courts consistently rejected Terms of Service as a valid means of prohibiting scrapers, including cases like Perfect 10 v. Google, and Cvent v. Eventbrite.

The Takeaway: The earliest cases against scrapers hinged on Trespass to Chattels law, and were successful. However, that doctrine is no longer a valid approach.

Facebook Web Scraping2009—Facebook Steps In

In 2009, Facebook turned the tides of the web scraping war when Power.com, a site which aggregated multiple social networks into one centralized site, included Facebook in their service. Because Power.com was scraping Facebook’s content instead of adhering to their established standards, Facebook sued Power on grounds of copyright infringement.

In denying Power.com’s motion to dismiss the case, the Judge ruled that scraping can constitute copying, however momentary that copying may be. And because Facebook’s Terms of Service don’t allow for scraping, that act of copying constituted an infringement on Facebook’s copyright. With this decision, the waters regarding the legality of web scrapers began to shift in favor of the content creators.

The Takeaway: Even if a web scraper ignores infringing content on its way to freely-usable content, it might qualify as copyright infringement by virtue of having technically “copied” the infringing content first.

2011-2014— U.S. v Auernheimer

In 2010, hacker Andrew “Weev” Auernheimer found a security flaw in AT&T’s website, which would display the email addresses of users who visited the site via their iPads. By exploiting the flaw using some simple scripts and a scraper, Auernheimer was able to gather thousands of emails from the AT&T site.

Although these email addresses were publicly available, Auernheimer’s exploit led to his 2012 conviction, where he was charged with identity fraud and conspiracy to access a computer without authorization.

Data ScrapingEarlier this year, the court vacated Auernheimer’s conviction, ruling that the trial’s New Jersey venue was improper. But even though the case turned out to be mostly inconclusive, the court noted the fact that there was no evidence to show that “any password gate or code-based barrier was breached.” This seems to leave room for the web scraping of publicly-available personal information, although it’s still very much open to interpretation and not set in stone.

The Takeaway: Using a web scraper to aggregate sensitive personal information can lead to a conviction, even if that information was technically available to the public. While there is hope in the court’s observation that no passwords or barriers were broken to retrieve this information, the waters here are still very volatile.

2013—Associated Press vs. Meltwater

Meltwater is a software company whose “Global Media Monitoring” product uses scrapers to aggregate news stories for paying clients. The Associated Press took issue with Meltwater’s scraping of their original stories, some of which had been copyrighted. In 2012, AP filed suit against Meltwater for copy infringement and hot news misappropriation.

While it’s already been established that facts cannot be copyrighted, the court decided that the AP’s copyrighted articles—and more specifically, the way in which the facts within those articles were arranged—were not fair game for copying. On top of this, Meltwater’s use of the articles failed to meet the established fair use standards, and could not be defended on that front either.

The Takeaway: Fair use is limited when it comes to web scrapers, and copyrighted content is not always open to be scraped.

~~

By closely observing the outcomes of previous rulings, you’ll find that there are a few guidelines that a scraper should attempt to adhere to:

    Content being scraped is not copyright protected
    The act of scraping does not burden the services of the site being scraped
    The scraper does not violate the Terms of Use of the site being scraped
    The scraper does not gather sensitive user information
    The scraped content adheres to fair use standards

While all of these guidelines are important to understand before using scrapers, there are other ways to acclimate to the legal nuances. In many cases, you’ll find that a simple conversation with a business software developer or consultant will lead to some satisfying conclusions: Odds are, they’ve used scrapers in the past and can shed light on any snags they’ve hit in the process. And of course, talking with a lawyer is always an ideal course of action when treading into questionable legal territory.

Source:http://blog.icreon.us/2014/09/12/web-scraping-and-you-a-legal-primer-for-one-of-its-most-useful-tools/

The PromptCloud Advantage- Web Scraping with an Edge

The global market is now more aware of its data scraping needs. And so with the demand, the list of suppliers has grown too. This post is dedicated to bringing out the PromptCloud Advantage among such providers.

PromptCloud-Winning-The Race

1. The know-how- Crawling the web, as mundane as it may sound, is a fairly complex task. No one is to be blamed for overlooking the complexity as these things surface only after you’ve tried it yourself and delved into the nitty-gritty. The design decisions you take sit at the core of what you build and eventually monetize. And the long-term effects of such architectural choices are as pleasing if you’ve done it right as disturbing they might turn out if you’re not far-sighted.

Although the expertise of building the tech stack for such large-scale data acquisition, distributing your clusters (and putting thoughts into their geographical locations), maintaining queues, databases and backups, does come from ‘been there done that’, we have been lucky to have the tech advantage imbibed into us since inception. Not that we got it right the first time, but our systems have evolved with technologies, improving each day. Now that we have been there in this business for the last 56 months, it does feel like a long journey for our stack and yes, we do know better :)

2. SLAs- SLAs are what bolsters the data itself. PromptCloud’s key SLAs are scale and quality; while not compromising the data coverage or the politeness policies on your sources. Since we perform focused crawls, there’s no dilution of data and you can consume it all or ask us to index it in order to search using logical combinations in queries. For your reference, here’s a list of all SLAs to visit while picking your data service provider.

changing_place_changing_time_changing_thouts_changing_future.

3. The Experience- There are many scraping tools and crawling services in the market which might just serve the need. What PromptCloud provides is a data acquisition experience; and we go as many number of extra miles as you’d like us to go for it. By leveraging our DaaS platform, we make sure you get what you need from the time you start your research for a data provider through importing the data feeds into your database. We hear your requirements in detail, make sure we’ve got it right by sharing samples and going multiple iterations of reprocessing the data to match your needs while you battle internally on freezing your requirements. But what’s more magical is the way all these feeds get delivered to you, at the intervals you requested; programatically.

It might be evident for the SLAs and the know-how fusing to provide the experience, but it’s that additional human touch that actually aids in sustaining it. We make sure you’re at peace while our systems handle the roadblocks and sort out the messiness on the web.

Source:https://www.promptcloud.com/blog/the-promptcloud-advantage-web-scraping/

Web scraping services-importance of scraped data

Web scraping services are provided by computer software which extracts the required facts from the website. Web scraping services mainly aims at converting unstructured data collected from the websites into structured data which can be stockpiled and scrutinized in a centralized databank. Therefore, web scraping services have a direct influence on the outcome of the reason as to why the data collected in necessary.

It is not very easy to scrap data from different websites due to the terms of service in place. So, the there are some legalities that have been improvised to protect altering the personal information on different websites. These ‘rules’ must be followed to the letter and to some extent have limited web scraping services.

Owing to the high demand for web scraping, various firms have been set up to provide the efficient and reliable guidelines on web scraping services so that the information acquired is correct and conforms to the security requirements. The firms have also improvised different software that makes web scraping services much easier.

Importance of web scraping services

Definitely, web scraping services have gone a long way in provision of very useful information to various organizations. But business companies are the ones that benefit more from web scraping services. Some of the benefits associated with web scraping services are:

    Helps the firms to easily send notifications to their customers including price changes, promotions, introduction of a new product into the market. Etc.
    It enables firms to compare their product prices with those of their competitors
    It helps the meteorologists to monitor weather changes thus being able to focus weather conditions more efficiently
    It also assists researchers with extensive information about peoples’ habits among many others.
    It has also promoted e-commerce and e-banking services where the rates of stock exchange, banks’ interest rates, etc. are updated automatically on the customer’s catalog.

Advantages of web scraping services

The following are some of the advantages of using web scraping services

    Automation of the data

    Web scraping can retrieve both static and dynamic web pages

    Page contents of various websites can be transformed

    It allows formulation of vertical aggregation platforms thus even complicated data can still be extracted from different websites.

    Web scraping programs recognize semantic annotation

    All the required data can be retrieved from their websites

    The data collected is accurate and reliable

Web scraping services mainly aims at collecting, storing and analyzing data. The data analysis is facilitated by various web scrapers that can extract any information and transform it into useful and easy forms to interpret.

Challenges facing web scraping

    High volume of web scraping can cause regulatory damage to the pages

    Scale of measure; the scales of the web scraper can differ with the units of measure of the source file thus making it somewhat hard for the interpretation of the data

    Level of source complexity; if the information being extracted is very complicated, web scraping will also be paralyzed.

It is clear that besides web scraping providing useful data and information, it experiences a number of challenges. The good thing is that the web scraping services providers are always improvising techniques to ensure that the information gathered is accurate, timely, reliable and treated with the highest levels of confidentiality.

Source: http://www.loginworks.com/blogs/web-scraping-blogs/191-web-scraping-services-importance-of-scraped-data/

Review: import.io’s New Scraping Process and Features

Web scraping Data platform import.io, announced last week that they have secured $3M in funding from investors that include the founders of Yahoo! and MySQL.

They also released a new beta version of the tool that is essentially a better version of their extraction tool, with some new features and a much cleaner and faster user experience.

First Impression

I’ve used the tool for a week and can say it is an improvement over the old version – which was a bit bulky and awkward. While still not exactly the most intuitive process, the development team at import.io has managed to slim down what was a relatively button heavy process, without sacrificing any of the functionality – they made the new workflow both simpler and more complicated at the same.

The new version features a simple tool bar across the top as opposed to the space hogging table and wizard from before, which is a large improvement on the pink and white of the previous version.

True, the loss of the wizard means there isn’t as much guidance as before (the pop-up help only appears on the first use), but the undo button means you don’t really need it. You can click around and experiment a bit with the different extraction options before settling down to do some real work.

Data Extraction

Once you’ve figured out how it works, the new version requires far fewer mouse clicks to get from the page to a table of data/API as shown in their homepage video.

All you need to do now is navigate to a website, click a single piece of data on the page – such as price, image, or URL – and their app will find all the other examples of similar data on the website, immediately creating a structured table of data.

download2

This latest version of the extractor also includes a important new feature labeled “Suggest Data”. Its important because it lets you extract all the data from a page, instantly creating a table of data that can be published as an API. This makes import.io very exciting and quick, I spent a long time playing with this and it worked on the majority of sites.

Advanced Features

Most non-programmer web scrapers struggle with complex sites that use JavaScript or iFrames, but import.io also now deals with this. In the basic mode you can toggle JavaScript and CSS on and off to help you see your data better.

If that doesn’t work, you can switch into an ‘advanced mode’ where import allows you to write your own XPath and RegExp. They’ve also added a source code view, though without the ability to click on the site and inspect element (like in Chrome) this feature isn’t particularly useful.

API Integration

Once you’ve created your scraper, there are a number of options for what you can do with it.

If you’ want you can just copy and paste the data into a spreadsheet or Download as CSV. You can also push your data directly Google Sheets, with import.io’s self generated formula.

For the rest of us, they have surfaced both the POST and GET requests for you and given you a JSON view which allows you to see how the data is returned, which is handy.

All this functionality is nice, and it’s clear they’re trying to cater to all technical levels, but it has made the API page somewhat messy and potentially confusing for newer or less technical users, but they should be able to get what they need.

Good with lots of Potential

Their new tool certainly isn’t perfect. There are still a few sites where manual row training is required and you can’t access the authentication feature (though you can still do this in the old version) or pagination.

Even if it’s not quite there yet, if import.io continue like this, they are well on its way to becoming the best data scraping platform on the market. Especially when you consider the “free for life” price tag.

Source:http://scraping.pro/review-import-ios-new-scraping-tools-features/

Web Scraping and Copyright

There are rigorous debates on the topic of copyright issues of a scraped data. Whether it is legal to use web scraping to extract data from a website or it’s just an illegal act that will lead you to a troublesome situation. There are certain implementations when we talk of web scraping. There are certain websites that will provide you with RSS feeds.

Just grab the piece use it and get the credit. However there are sites that do not allow such kind of cooperation (as we will call this!) and thus there is no other way to extract reliable data. Another way is to hold the “ctrl” plus “c” keys and wait for a while for the data to be copied to your computer.

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

Why People Hesitate To Try Data Mining

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

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

The Causes

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

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

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

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

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

The thing is, unless you are a yahoo developer, you probably don't have access to the source code you're trying to scrape. That is to say, you don't know exactly how the site is built and how your requests to it as a user are being processed on the server-side. You can, however, investigate the client-side and try to emulate it. I like using Chrome Developer Tools for this, but you can use others such as FF firebug.

So first off we need to figure out what's going on. So the way it works, is you click on the 'show comments' it loads the first ten, then you need to keep clicking for the next ten comments each time. Notice, however, that all this clicking isn't taking you to a different link, but lively fetches the comments, which is a very neat UI but for our case requires a bit more work. I can tell two things right away:

    They're using javascript to load the comments (because I'm staying on the same page).
    They load them dynamically with AJAX calls each time you click (meaning instead of loading the comments with the page and just showing them to you, with each click it does another request to the database).

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

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

By looking at that we still don't know how that's generated or what it does when clicked. Fine. Now, keeping the devtools window open, let's click on it. This opened up the first ten. But in fact, a request was being made for us to fetch them. A request that chrome devtools recorded. We look in the network tab of the devtools and see a lot of confusing data. Wait, here's one that makes sense:

http://news.yahoo.com/_xhr/contentcomments/get_comments/?content_id=42f7f6e0-7bae-33d3-aa1d-3dfc7fb5cdfc&_device=full&count=10&sortBy=highestRated&isNext=true&offset=20&pageNumber=2&_media.modules.content_comments.switches._enable_view_others=1&_media.modules.content_comments.switches._enable_mutecommenter=1&enable_collapsed_comment=1

See? _xhr and then get_comments. That makes a lot of sense. Going to that link in the browser gave me a JSON object (looks like a python dictionary) containing all the ten comments which that request fetched. Now that's the request you need to emulate, because that's the one that gives you what you want. First let's translate this to some normal reqest that a human can read:

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

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

    Obviously the count is what decides how many comments you're getting. I tried changing it to 100 to see what happens and got a bad request. And it was nice enough to tell me why - "Offset should be multiple of total rows". So now we understand how to use offset

    The content_id is probably something that identifies the article you are reading. Meaning you need to fetch that from the original page somehow. Try digging around a little, you'll find it.

    Also, you obviously don't want to fetch 10 comments at a time, so it's probably a good idea to find a way to fetch the number of total comments somehow (either find out how the page gets it, or just fetch it from within the article itself)

    Using the devtools you have access to all client-side scripts. So by digging you can find that that link to /get_comments/ is kept within a javascript object named YUI. You can then try to understand how it is making the request, and try to emulate that (though you can probably figure it out yourself)

    You might need to overcome some security measures. For example, you might need a session-key from the original article before you can access the comments. This is used to prevent direct access to some parts of the sites. I won't trouble you with the details, because it doesn't seem like a problem in this case, but you do need to be aware of it in case it shows up.

    Finally, you'll have to parse the JSON object (python has excellent built-in tools for that) and then parse the html comments you are getting (for which you might want to check out BeautifulSoup).

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

So don't panic.

It's just a matter of digging and digging until you find gold (also, having some basic WEB knowledge doesn't hurt). Then, if you face a roadblock and really can't go any further, come back here to SO, and ask again. Someone will help you.


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

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

Is it ok to scrape data from Google results?

I'd like to fetch results from Google using curl to detect potential duplicate content. Is there a high risk of being banned by Google?

Google will eventually block your IP when you exceed a certain amount of requests.



Google disallows automated access in their TOS, so if you accept their terms you would break them.

That said, I know of no lawsuit from Google against a scraper. Even Microsoft scraped Google, they powered their search engine Bing with it. They got caught in 2011 red handed :)

There are two options to scrape Google results:

1) Use their API

    You can issue around 40 requests per hour You are limited to what they give you, it's not really useful if you want to track ranking positions or what a real user would see. That's something you are not allowed to gather.

    If you want a higher amount of API requests you need to pay.
    60 requests per hour cost 2000 USD per year, more queries require a custom deal.

2) Scrape the normal result pages

    Here comes the tricky part. It is possible to scrape the normal result pages. Google does not allow it.
    If you scrape at a rate higher than 15 keyword requests per hour you risk detection, higher than 20/h will get you blocked from my experience.
    By using multiple IPs you can up the rate, so with 100 IP addresses you can scrape up to 2000 requests per hour. (50k a day)
    There is an open source search engine scraper written in PHP at http://scraping.compunect.com It allows to reliable scrape Google, parses the results properly and manages IP addresses, delays, etc. So if you can use PHP it's a nice kickstart, otherwise the code will still be useful to learn how it is done.


Source: http://stackoverflow.com/questions/22657548/is-it-ok-to-scrape-data-from-google-results

Excel VBA Data Mining Real-Time Data from a Web Page that Refreshes Data

I want to capture real-time data that updates into a table on a webpage; I prefer capturing it into excel using VBA, but I will write it in .NET C# or VB if I that is easier.

the data updates about 1 or 2 seconds, and I want to just grab the latest data quotes and log it into my spreadsheet; the table names are the same, only the data refreshes, and it does so automatically on the web page.

I've done a lot of Excel VBA and I know how to download a URL to a file--this is NOT what I want; I want to gain access to my webpage that is active and grab the data updates after I've logged into my site and selected a webpage that I like.

Is there a simple way to access this data on the webpage from Excel or .Net? Because it refreshes no more than once every 1 or 2 seconds, it is easy to just keep checking it for updates, and I can compare the latest data to see if it actually refreshed.


In Excel 2003, use Data/Import External Data/New Web Query
Browse to your page and select the table you want to import.
After that you can either do a manual Refresh, or use a timer procedure to do something like:

Source: http://stackoverflow.com/questions/9855794/excel-vba-data-mining-real-time-data-from-a-web-page-that-refreshes-data

How to extract data from web 2.0 graphs using a scraper

I have recently come across a web page containing a graph object that displays the (x, y) values on the object as the mouse is rolled across it. Is there any way to automate the extraction of this data?

How is the graph data loaded? If embedded in the page source then you can extract it with xpath or regex. Else use Firebug to see how it is loaded.

You will need a solution that works inside the web browser, so the AJAX/Javascript is properly rendered.

I have used iMacros with good success for web scraping in the past. There are free/open-source and "PRO" paid editions (comparison table here).

Another option is always to custom code something with the Microsoft webbrowser control.


Source: http://stackoverflow.com/questions/3980774/how-to-extract-data-from-web-2-0-graphs-using-a-scraper

Extract data from Web Scraping C#

I am MVC ASP.NET developer.

I have received the contents from any url, i.e. http, https etc. using WebRequest class.

I have received all the content of that particular url. (for now I took http://google.com)

My next step is to extract buttons, header, footer, colors, text etc.

Here is my code for now:

public ActionResult GetContent(UrlModel model) //model having a string URL
which is entered in a text box and method hits using submit button.
{
    //WebRequest request = WebRequest.Create(model.URL);

    WebRequest request = WebRequest.Create(model.URL);

    request.Credentials = CredentialCache.DefaultCredentials;

    WebResponse response = request.GetResponse();

    Stream dataStream = response.GetResponseStream();

    StreamReader reader = new StreamReader(dataStream);

    string responseFromServer = reader.ReadToEnd();
    ViewBag.Response = responseFromServer;

    reader.Close();
    response.Close();
    return View();
}

Can someone help me with writing the code ?

Also do suggest me with some techniques of data extraction in C#.



Source: http://stackoverflow.com/questions/21901162/extract-data-from-web-scraping-c-sharp

Scrapy, scraping price data from StubHub

I've been having a difficult time with this one.

I want to scrape all the prices listed for this Bruno Mars concert at the Hollywood Bowl so I can get the average

price.

http://www.stubhub.com/bruno-mars-tickets/bruno-mars-hollywood-hollywood-bowl-31-5-2014-4449604/

I've located the prices in the HTML and the xpath is pretty straightforward but I cannot get any values to return.

I think it has something to do with the content being generated via javascript or ajax but I can't figure out how to

send the correct request to get the code to work.

Here's what I have:

from scrapy.spider import BaseSpider
from scrapy.selector import Selector

from deeptix.items import DeeptixItem

class TicketSpider(BaseSpider):
    name = "deeptix"
    allowed_domains = ["stubhub.com"]
    start_urls = ["http://www.stubhub.com/bruno-mars-tickets/bruno-mars-hollywood-hollywood-bowl-31-5-2014-4449604/"]

def parse(self, response):
    sel = Selector(response)
    sites = sel.xpath('//div[contains(@class, "q_cont")]')
    items = []
    for site in sites:
        item = DeeptixItem()
        item['price'] = site.xpath('span[contains(@class, "q")]/text()').extract()
        items.append(item)
    return items

Any help would be greatly appreciated I've been struggling with this one for quite some time now. Thank you in advance!


Source: http://stackoverflow.com/questions/22770917/scrapy-scraping-price-data-from-stubhub

Anyone knows an online tool that can scrape a page and create a REST API for the scraped data?

I'm looking for a SaaS solution that is able to login to a platform, scrape data (reports) and then allow accessing the data through an API. I have some reporting platforms that provide web reporting and email reporting but with no API. Online reporting doesn't help and email reporting, although can be automated and scraped, isn't so reliable.

If you are willing to do the scraping through your own connection, have a look at Import IO. They have a desktop application that you use to teach the system how to scrape a page, and then you run the crawler from that application - and you can run it for as long as you like, as far as I can tell.

You may then upload your data to the Import cloud, from where it is available via an API on the import.io servers. Useful data can be made public to donate it "to the commons" if you wish.


I did some more digging, found iMacros as a possible solution. Its Windows based, which is a drawback in my case, but it does allow automation of the scraping and afterwards interaction via common web scripting languages like PHP and ASP.net.


If you are familiar with jQuery, I think you can use node.js and Cheerio module, then you can create a simple application to do auto scraping. Actually I have already built a site to do on line web scraping based on the above mentioned tech, the site is www.datafiddle.net, you can take a look at it.


Source: http://stackoverflow.com/questions/19646028/anyone-knows-an-online-tool-that-can-scrape-a-page-and-create-a-rest-api-for-the

How do you scrape AJAX pages?

Overview:

All screen scraping first requires manual review of the page you want to extract resources from. When dealing with AJAX you usually just need to analyze a bit more than just simply the HTML.

When dealing with AJAX this just means that the value you want is not in the initial HTML document that you requested, but that javascript will be exectued which asks the server for the extra information you want.

You can therefore usually simply analyze the javascript and see which request the javascript makes and just call this URL instead from the start.

Example:

Take this as an example, assume the page you want to scrape from has the following script:

<script type="text/javascript">
function ajaxFunction()
{
var xmlHttp;
try
  {
  // Firefox, Opera 8.0+, Safari
  xmlHttp=new XMLHttpRequest();
  }
catch (e)
  {
  // Internet Explorer
  try
    {
    xmlHttp=new ActiveXObject("Msxml2.XMLHTTP");
    }
  catch (e)
    {
    try
      {
      xmlHttp=new ActiveXObject("Microsoft.XMLHTTP");
      }
    catch (e)
      {
      alert("Your browser does not support AJAX!");
      return false;
      }
    }
  }
  xmlHttp.onreadystatechange=function()
    {
    if(xmlHttp.readyState==4)
      {
      document.myForm.time.value=xmlHttp.responseText;
      }
    }
  xmlHttp.open("GET","time.asp",true);
  xmlHttp.send(null);
  }
</script>

Then all you need to do is instead do an HTTP request to time.asp of the same server instead. Example from w3schools.


Sporce: http://stackoverflow.com/questions/260540/how-do-you-scrape-ajax-pages

PDF scraping using R

I have been using the XML package successfully for extracting HTML tables but want to extend to PDF's. From previous questions it does not appear that there is a simple R solution but wondered if there had been any recent developments

Failing that, is there some way in Python (in which I am a complete Novice) to obtain and manipulate pdfs so that I could finish the job off with the R XML package

Extracting text from PDFs is hard, and nearly always requires lots of care.

I'd start with the command line tools such as pdftotext and see what they spit out. The problem is that PDFs can store the text in any order, can use awkward font encodings, and can do things like use ligature characters (the joined up 'ff' and 'ij' that you see in proper typesetting) to throw you.

pdftotext is installable on any Linux system



Source: http://stackoverflow.com/questions/7918718/pdf-scraping-using-r

Php Scraping data from a website

I am very new to programming and need a little help with getting data from a website and passing it into my PHP script.

The website is http://www.birthdatabase.com/.

I would like to plug in a name (First and Last) and retrieve the result. I know you can query the site by passing the name in the URL, but I am having problems scraping the results.

http://www.birthdatabase.com/cgi-bin/query.pl?textfield=FIRST&textfield2=LAST&age=&affid=

I am using the file_get_contents($URL) function to get the page but need help after that. Specifically, I would like to scrape only the results from a certain state if there are multiple results for that name.



You need the awesome simple_html_dom class.

With this class you can query the webpage's DOM in a similar way to jQuery.

First include the class in your page, then get the page content with this snippet:

$html = file_get_html('http://www.birthdatabase.com/cgi-bin/query.pl?textfield=' . $first . '&textfield2=' . $last . '&age=&affid=');

Then you can use CSS selections to scrape your data (something like this):

$n = 0;
foreach($html->find('table tbody tr td div font b table tbody') as $element) {
    @$row[$n]['tr']  = $element->find('tr')->text;
    $n++;
}

// output your data
print_r($row);



Source: http://stackoverflow.com/questions/15601584/php-scraping-data-from-a-website