Web Scraping : To Scrape or Not to Scrape?

Web scraping is on the rise and its legality is being debated. The future of big data could hang in the balance.

I decided that I might want to stop writing about all these successful dot-com businesses and get into the act myself. I mean, how hard could it be, aside from the fact that I have no expertise in any particular vertical, no technological knowledge, and no money? That last was a bit of a problem because I was going to need big data, and big data doesn't come cheap.

So, one day I'm talking to a direct-tech wizard and he says, “Why don't you just find a business you want to be in, find the most successful company, go to their website, and scrape some of their data?”

Scrape? My dad was a house painter. I used to help him during summers. The only scraping I knew was done with a putty knife. But that's what God invented the Internet for. Google turned up endless Web scraping services and I went to one called Automation Anywhere.

Its homepage told me not to try scraping on my own, that I could pay them as little as $1,995 for a program that would have me scraping away in minutes with no programming expertise. A video showed me how. Suppose I wanted to locate all the assisted living facilities in Detroit? (Bedpans! There's a wide-open Web business!) Automation Anywhere showed me how I could request a data pattern—name, address, phone, and service area of targets—apply their program to a rehab facility listing, and minutes later be in possession of tidy customer list on a spreadsheet. A box popped up asking if I had any questions for a live account manager. I did.

“I'm interested in this, but is it totally legal?” I asked Shine, the account rep.

Shine was slow to respond and came back disappointingly noncommittal: “You need to install the software at your end. Hence, you will need to check at your end for legal documents for the website.”

Shine had obviously been reading the same European news sites that I had. Last year Irish airline Ryanair filed suit against PR Aviation, a Dutch airfare comparison site, charging it with copyright infringement and breach of contract for scraping flight data from its site. The Court of Justice of the European Union (ECJ) dismissed the suit, saying the scraping amounted to “normal use” of a website. However, the ECJ did potentially leave the door open for businesses with unprotected databases, such as Ryanair, to establish contractual limitations on use of their databases by third parties. That opening, should it be entered into by airlines, might have businesses such as Expedia, Orbitz, and Priceline reimagining their business plans.

And it could have any other businesses that load up third-party data files through scraping activities doing the same. The reality, however, is that that's a big “and.”

“The airlines have been halfway successful taking travel agents to court, but it can take five years and then they lose,” said Gus Cunningham, CEO of ScrapeSentry, one of only “three and a half” companies, in his words, that block scrapers from websites.

Professional scrapers are not only out-front and plentiful—as the Google search demonstrated—they're also nimble and expensive to chase away. Basically, Cunningham said, it's a matter of stopping scrapers at the website door among the airlines, e-coms, real estate sites, and online gambling companies that are scraped the most. Cunningham's company monitors inbound Web traffic and uses an analysis engine to block suspect visitors per parameters set down by clients. ScanSentry has a nine-year-old database that helps it identify bad actors, much like Interpol with its criminal database. Then a human element must enter the process.

Some of Cunningham's clients feed bogus information to competitors identified as scrapers to ferret them out. In many cases, though, they opt to turn their heads. “Airlines have some flights where they just want to get as many butts as they can in the seats, so they won't concentrate their blocking efforts on those. They'll concentrate on the routes that are always jammed,” Cunningham said.

Unlike botnets that steal money by, say, serving bogus websites to siphon off programmatic ad dollars, scraping is not overtly criminal. In the wide sphere of digital commerce, it's probably most common that the scrap-ee is also a scrap-er. How vigilant vulnerable industries become, and how protective courts and law enforcement agencies grow, will depend on how much scraping activity increases. Cunningham said it's growing fast. More than one fifth of visitors to client websites last year were scrapers, according to a ScanSentry study. Among travel companies, meanwhile, scrapers doubled from 15% in 2013 to 33% last year.

“And,” Cunningham noted, “It is stealing.”

Source: http://www.dmnews.com/direct-line-blog/to-scrape-or-not-to-scrape/article/422662/

Data Scraping - Hand Scraped Hardwood Flooring Gives Your Home That Exclusive Look

Today hand scraped hardwood flooring is becoming extremely popular in the more opulent homes as well as in some commercial properties. Although this type of flooring has only recently become fashionable it has been around for many centuries.

Certainly before the invention of modern sanding techniques all floors where hand scraped at the location where they were to be installed to ensure that the floor would be flat and even. However today this method is used instead to provide texture, richness as well as a unique look and feel to the flooring.

Although manufacturers have produced machines which can provide a scraped look to their flooring it looks cheap compared to the real thing. Unfortunately the main problem with using a machine to scrape the flooring is that it provides a uniform look to the pattern of the wood. Because of this it lacks the natural feel that you would see with a floor which has been scraped by hand.

When done by hand, scraping creates a truly unique look to the floor. However the actual look and feel of each floor will vary as it depends on the skills of the person actually carrying out the work. If there is no control in place whilst the work is being carried out this can result in disastrous look to the finished product.

Many manufacturers who actually provide hand scraped hardwood flooring will either just dent, scoop or rough the floor up. But others will use sanding techniques in order to create a worn and uneven look to the flooring. The more professional teams will scrape the entire surface of the wood in order to create the unique hand made look for their customers.

Many companies will allow their customers to choose what type of scraping takes place on their wood. They can choose between light, medium and heavy. The companies who are really good at hand scraping will be able give the hardwood floor a reclaimed look by including wormholes, splits and other naturally-occurring features within the wood.

If you do decide to choose hand scraped hardwood flooring you will need to factor the costs that are associated with it into your budget. Unfortunately this type of flooring does not come cheap and you can find yourself paying upwards of $15 per sq ft. But once it is installed it will give a room a unique and warm rich feel to it and is certainly going to wow your friends and family when they see it for the first time.

Source: http://ezinearticles.com/?Hand-Scraped-Hardwood-Flooring-Gives-Your-Home-That-Exclusive-Look&id=572577

Making data on the web useful: scraping

Introduction

Many times data is not easily accessible – although it does exist. As much as we wish everything was available in CSV or the format of our choice – most data is published in different forms on the web. What if you want to use the data to combine it with other datasets and explore it independently?

Scraping to the rescue!

Scraping describes the method to extract data hidden in documents – such as Web Pages and PDFs and make it useable for further processing. It is among the most useful skills if you set out to investigate data – and most of the time it’s not especially challenging. For the most simple ways of scraping you don’t even need to know how to write code.

This example relies heavily on Google Chrome for the first part. Some things work well with other browsers, however we will be using one specific browser extension only available on Chrome. If you can’t install Chrome, don’t worry the principles remain similar.

Code-free Scraping in 5 minutes using Google Spreadsheets & Google Chrome

Knowing the structure of a website is the first step towards extracting and using the data. Let’s get our data into a spreadsheet – so we can use it further. An easy way to do this is provided by a special formula in Google Spreadsheets.

Save yourselves hours of time in copy-paste agony with the ImportHTML command in Google Spreadsheets. It really is magic!

Recipes

In order to complete the next challenge, take a look in the Handbook at one of the following recipes:

    Extracting data from HTML tables.

    Scraping using the Scraper Extension for Chrome

Both methods are useful for:

    Extracting individual lists or tables from single webpages

The latter can do slightly more complex tasks, such as extracting nested information. Take a look at the recipe for more details.

Neither will work for:

    Extracting data spread across multiple webpages

Challenge

Task: Find a website with a table and scrape the information from it. Share your result on datahub.io (make sure to tag your dataset with schoolofdata.org)

Tip

Once you’ve got your table into the spreadsheet, you may want to move it around, or put it in another sheet. Right click the top left cell and select “paste special” – “paste values only”.

Scraping more than one webpage: Scraperwiki

Note: Before proceeding into full scraping mode, it’s helpful to understand the flesh and bones of what makes up a webpage. Read the Introduction to HTML recipe in the handbook.

Until now we’ve only scraped data from a single webpage. What if there are more? Or you want to scrape complex databases? You’ll need to learn how to program – at least a bit.

It’s beyond the scope of this course to teach how to scrape, our aim here is to help you understand whether it is worth investing your time to learn, and to point you at some useful resources to help you on your way!

Structure of a scraper

Scrapers are comprised of three core parts:

1.    A queue of pages to scrape
2.    An area for structured data to be stored, such as a database
3.    A downloader and parser that adds URLs to the queue and/or structured information to the database.

Fortunately for you there is a good website for programming scrapers: ScraperWiki.com

ScraperWiki has two main functions: You can write scrapers – which are optionally run regularly and the data is available to everyone visiting – or you can request them to write scrapers for you. The latter costs some money – however it helps to contact the Scraperwiki community (Google Group) someone might get excited about your project and help you!.

If you are interested in writing scrapers with Scraperwiki, check out this sample scraper – scraping some data about Parliament. Click View source to see the details. Also check out the Scraperwiki documentation: https://scraperwiki.com/docs/python/

When should I make the investment to learn how to scrape?

A few reasons (non-exhaustive list!):

1.    If you regularly have to extract data where there are numerous tables in one page.

2.    If your information is spread across numerous pages.

3.    If you want to run the scraper regularly (e.g. if information is released every week or month).

4.    If you want things like email alerts if information on a particular webpage changes.

…And you don’t want to pay someone else to do it for you!

Summary:

In this course we’ve covered Web scraping and how to extract data from websites. The main function of scraping is to convert data that is semi-structured into structured data and make it easily useable for further processing. While this is a relatively simple task with a bit of programming – for single webpages it is also feasible without any programming at all. We’ve introduced =importHTML and the Scraper extension for your scraping needs.

Further Reading

1.    Scraping for Journalism: A Guide for Collecting Data: ProPublica Guides

2.    Scraping for Journalists (ebook): Paul Bradshaw

3.    Scrape the Web: Strategies for programming websites that don’t expect it : Talk from PyCon

4.    An Introduction to Compassionate Screen Scraping: Will Larson

Any questions? Got stuck? Ask School of Data!

ScraperWiki has two main functions: You can write scrapers – which are optionally run regularly and the data is available to everyone visiting – or you can request them to write scrapers for you. The latter costs some money – however it helps to contact the Scraperwiki community (Google Group) someone might get excited about your project and help you!.

If you are interested in writing scrapers with Scraperwiki, check out this sample scraper – scraping some data about Parliament. Click View source to see the details. Also check out the Scraperwiki documentation: https://scraperwiki.com/docs/python/

When should I make the investment to learn how to scrape?

A few reasons (non-exhaustive list!):

1.    If you regularly have to extract data where there are numerous tables in one page.

2.    If your information is spread across numerous pages.

3.    If you want to run the scraper regularly (e.g. if information is released every week or month).

4.    If you want things like email alerts if information on a particular webpage changes.

…And you don’t want to pay someone else to do it for you!

Summary:

In this course we’ve covered Web scraping and how to extract data from websites. The main function of scraping is to convert data that is semi-structured into structured data and make it easily useable for further processing. While this is a relatively simple task with a bit of programming – for single webpages it is also feasible without any programming at all. We’ve introduced =importHTML and the Scraper extension for your scraping needs.

Source: http://schoolofdata.org/handbook/courses/scraping/

Web Scraping : Data Mining vs Screen-Scraping

Data mining isn't screen-scraping. I know that some people in the room may disagree with that statement, but they're actually two almost completely different concepts.

In a nutshell, you might state it this way: screen-scraping allows you to get information, where data mining allows you to analyze information. That's a pretty big simplification, so I'll elaborate a bit.

The term "screen-scraping" comes from the old mainframe terminal days where people worked on computers with green and black screens containing only text. Screen-scraping was used to extract characters from the screens so that they could be analyzed. Fast-forwarding to the web world of today, screen-scraping now most commonly refers to extracting information from web sites. That is, computer programs can "crawl" or "spider" through web sites, pulling out data. People often do this to build things like comparison shopping engines, archive web pages, or simply download text to a spreadsheet so that it can be filtered and analyzed.

Data mining, on the other hand, is defined by Wikipedia as the "practice of automatically searching large stores of data for patterns." In other words, you already have the data, and you're now analyzing it to learn useful things about it. Data mining often involves lots of complex algorithms based on statistical methods. It has nothing to do with how you got the data in the first place. In data mining you only care about analyzing what's already there.

The difficulty is that people who don't know the term "screen-scraping" will try Googling for anything that resembles it. We include a number of these terms on our web site to help such folks; for example, we created pages entitled Text Data Mining, Automated Data Collection, Web Site Data Extraction, and even Web Site Ripper (I suppose "scraping" is sort of like "ripping"). So it presents a bit of a problem-we don't necessarily want to perpetuate a misconception (i.e., screen-scraping = data mining), but we also have to use terminology that people will actually use.

Source: http://ezinearticles.com/?Data-Mining-vs-Screen-Scraping&id=146813

Twitter Scraper Python Library

I wanted to save the tweets from Transparency Camp. This prompted me to turn Anna‘s basic Twitter scraper into a library. Here’s how you use it.

Import it. (It only works on ScraperWiki, unfortunately.)

from scraperwiki import swimport

search = swimport('twitter_search').search

Then search for terms.

search(['picnic #tcamp12', 'from:TCampDC', '@TCampDC', '#tcamp12', '#viphack'])

A separate search will be run on each of these phrases. That’s it.

A more complete search

Searching for #tcamp12 and #viphack didn’t get me all of the tweets because I waited like a week to do this. In order to get a more complete list of the tweets, I looked at the tweets returned from that first search; I searched for tweets referencing the users who had tweeted those tweets.

from scraperwiki.sqlite import save, select

from time import sleep

# Search by user to get some more

users = [row['from_user'] + ' tcamp12' for row in \

select('distinct from_user from swdata where from_user where user > "%s"' \

% get_var('previous_from_user', ''))]

for user in users:

    search([user], num_pages = 2)

    save_var('previous_from_user', user)

    sleep(2)

By default, the search function retrieves 15 pages of results, which is the maximum. In order to save some time, I limited this second phase of searching to two pages, or 200 results; I doubted that there would be more than 200 relevant results mentioning a particular user.

The full script also counts how many tweets were made by each user.

Library

Remember, this is a library, so you can easily reuse it in your own scripts, like Max Richman did.

Source: https://scraperwiki.wordpress.com/2012/07/04/twitter-scraper-python-library/

Web Scraping Services - A trending technique in data science!!!

Web scraping as a market segment is trending to be an emerging technique in data science to become an integral part of many businesses – sometimes whole companies are formed based on web scraping. Web scraping and extraction of relevant data gives businesses an insight into market trends, competition, potential customers, business performance etc.  Now question is that “what is actually web scraping and where is it used???” Let us explore web scraping, web data extraction, web mining/data mining or screen scraping in details.

What is Web Scraping?

Web Data Scraping is a great technique of extracting unstructured data from the websites and transforming that data into structured data that can be stored and analyzed in a database. Web Scraping is also known as web data extraction, web data scraping, web harvesting or screen scraping.

What you can see on the web that can be extracted. Extracting targeted information from websites assists you to take effective decisions in your business.

Web scraping is a form of data mining. The overall goal of the web scraping process is to extract information from a websites and transform it into an understandable structure like spreadsheets, database or csv. Data like item pricing, stock pricing, different reports, market pricing, product details, business leads can be gathered via web scraping efforts.

There are countless uses and potential scenarios, either business oriented or non-profit. Public institutions, companies and organizations, entrepreneurs, professionals etc. generate an enormous amount of information/data every day.

Uses of Web Scraping:

The following are some of the uses of web scraping:

•    Collect data from real estate listing

•    Collecting retailer sites data on daily basis

•    Extracting offers and discounts from a website.

•    Scraping job posting.

•    Price monitoring with competitors.

•    Gathering leads from online business directories – directory scraping

•    Keywords research

•    Gathering targeted emails for email marketing – email scraping

•    And many more.

There are various techniques used for data gathering as listed below:

•    Human copy-and-paste – takes lot of time to finish when data is huge

•    Programming the Custom Web Scraper as per the needs.

•    Using Web Scraping Softwares available in market.

Are you in search of web data scraping expert or specialist. Then you are at right place. We are the team of web scraping experts who could easily extract data from website and further structure the unstructured useful data to uncover patterns, and help businesses for decision making that helps in increasing sales, cover a wide customer base and ultimately it leads to business towards growth and success.

We have got expertise in all the web scraping techniques, scraping data from ajax enabled complex websites, bypassing CAPTCHAs, forming anonymous http request etc in providing web scraping services.

The web scraping is legal since the data is publicly and freely available on the Web. Smart WebTech can probably help you to achieve your scraping-based project goals. We would be more than happy to hear from you.

Source: http://webdata-scraping.com/web-scraping-trending-technique-in-data-science/

Web Scraping Services : What are the ethics of web scraping?

Someone recently asked: "Is web scraping an ethical concept?" I believe that web scraping is absolutely an ethical concept. Web scraping (or screen scraping) is a mechanism to have a computer read a website. There is absolutely no technical difference between an automated computer viewing a website and a human-driven computer viewing a website. Furthermore, if done correctly, scraping can provide many benefits to all involved.

There are a bunch of great uses for web scraping. First, services like Instapaper, which allow saving content for reading on the go, use screen scraping to save a copy of the website to your phone. Second, services like Mint.com, an app which tells you where and how you are spending your money, uses screen scraping to access your bank's website (all with your permission). This is useful because banks do not provide many ways for programmers to access your financial data, even if you want them to. By getting access to your data, programmers can provide really interesting visualizations and insight into your spending habits, which can help you save money.

That said, web scraping can veer into unethical territory. This can take the form of reading websites much quicker than a human could, which can cause difficulty for the servers to handle it. This can cause degraded performance in the website. Malicious hackers use this tactic in what’s known as a "Denial of Service" attack.

Another aspect of unethical web scraping comes in what you do with that data. Some people will scrape the contents of a website and post it as their own, in effect stealing this content. This is a big no-no for the same reasons that taking someone else's book and putting your name on it is a bad idea. Intellectual property, copyright and trademark laws still apply on the internet and your legal recourse is much the same. People engaging in web scraping should make every effort to comply with the stated terms of service for a website. Even when in compliance with those terms, you should take special care in ensuring your activity doesn't affect other users of a website.

One of the downsides to screen scraping is it can be a brittle process. Minor changes to the backing website can often leave a scraper completely broken. Herein lies the mechanism for prevention: making changes to the structure of the code of your website can wreak havoc on a screen scraper's ability to extract information. Periodically making changes that are invisible to the user but affect the content of the code being returned is the most effective mechanism to thwart screen scrapers. That said, this is only a set-back. Authors of screen scrapers can always update them and, as there is no technical difference between a computer-backed browser and a human-backed browser, there's no way to 100% prevent access.

Going forward, I expect screen scraping to increase. One of the main reasons for screen scraping is that the underlying website doesn't have a way for programmers to get access to the data they want. As the number of programmers (and the need for programmers) increases over time, so too will the need for data sources. It is unreasonable to expect every company to dedicate the resources to build a programmer-friendly access point. Screen scraping puts the onus of data extraction on the programmer, not the company with the data, which can work out well for all involved.

Source: https://quickleft.com/blog/is-web-scraping-ethical/

What you need to know about web scraping: How to understand, identify, and sometimes stop

NB: This is a gust article by Rami Essaid, co-founder and CEO of Distil Networks.

Here’s the thing about web scraping in the travel industry: everyone knows it exists but few know the details.

Details like how does web scraping happen and how will I know? Is web scraping just part of doing business online, or can it be stopped? And lastly, if web scraping can be stopped, should it always be stopped?

These questions and the challenge of web scraping are relevant to every player in the travel industry. Travel suppliers, OTAs and meta search sites are all being scraped. We have the data to prove it; over 30% of travel industry website visitors are web scrapers.

Google Analytics, and most other analytics tools do not automatically remove web scraper traffic, also called “bot” traffic, from your reports – so how would you know this non-human and potentially harmful traffic exists? You have to look for it.

This is a good time to note that I am CEO of a bot-blocking company called Distil Networks, and we serve the travel industry as well as digital publishers and eCommerce sites to protect against web scraping and data theft – we’re on a mission to make the web more secure.

So I am admittedly biased, but will do my best to provide an educational account of what we’ve learned to be true about web scraping in travel – and why this is an issue every travel company should at the very least be knowledgeable about.

Overall, I see an alarming lack of awareness around the prevalence of web scraping and bots in travel, and I see confusion around what to do about it. As we talk this through I’ll explain what these “bots” are, how to find them and how to manage them to better protect and leverage your travel business.

What are bots, web scrapers and site indexers? Which are good and which are bad?

The jargon around web scraping is confusing – bots, web scrapers, data extractors, price scrapers, site indexers and more – what’s the difference? Allow me to quickly clarify.

–> Bots: This is a general term that refers to non-human traffic, or robot traffic that is computer generated. Bots are essentially a line of code or a program that is created to perform specific tasks on a large scale.  Bots can include web scrapers, site indexers and fraud bots. Bots can be good or bad.

–> Web Scraper: (web harvesting or web data extraction) is a computer software technique of extracting information from websites (source, Wikipedia). Web scrapers are usually bad.

If your travel website is being scraped, it is most likely your competitors are collecting competitive intelligence on your prices. Some companies are even built to scrape and report on competitive price as a service. This is difficult to prove, but based on a recent Distil Networks study, prices seem to be main target.You can see more details of the study and infographic here.

One case study is Ryanair. They have been particularly unhappy about web scraping and won a lawsuit against a German company in 2008, incorporated Captcha in 2011 to stop new scrapers, and when Captcha wasn’t totally effective and Cheaptickets was still scraping, they took to the courts once again.

So Ryanair is doing what seems to be a consistent job of fending off web scrapers – at least after the scraping is performed. Unfortunately, the amount of time and energy that goes into identifying and stopping web scraping after the fact is very high, and usually this means the damage has been done.

This type of web scraping is bad because:

    Your competition is likely collecting your price data for competitive intelligence.

    Other travel companies are collecting your flights for resale without your consent.

    Identifying this type of web scraping requires a lot of time and energy, and stopping them generally requires a lot more.

Web scrapers are sometimes good

Sometimes a web scraper is a potential partner in disguise.

Meta search sites like Hipmunk sometimes get their start by scraping travel site data. Once they have enough data and enough traffic to be valuable they go to suppliers and OTAs with a partnership agreement. I’m naming Hipmunk because the Company is one of th+e few to fess up to site scraping, and one of the few who claim to have quickly stopped scraping when asked.

I’d wager that Hipmunk and others use(d) web scraping because it’s easy, and getting a decision maker at a major travel supplier on the phone is not easy, and finding legitimate channels to acquire supplier data is most definitely not easy.

I’m not saying you should allow this type of site scraping – you shouldn’t. But you should acknowledge the opportunity and create a proper channel for data sharing. And when you send your cease and desist notices to tell scrapers to stop their dirty work, also consider including a note for potential partners and indicate proper channels to request data access.

–> Site Indexer: Good.

Google, Bing and other search sites send site indexer bots all over the web to scour and prioritize content. You want to ensure your strategy includes site indexer access. Bing has long indexed travel suppliers and provided inventory links directly in search results, and recently Google has followed suit.

–> Fraud Bot: Always bad.

Fraud bots look for vulnerabilities and take advantage of your systems; these are the pesky and expensive hackers that game websites by falsely filling in forms, clicking ads, and looking for other vulnerabilities on your site. Reviews sections are a common attack vector for these types of bots.

How to identify and block bad bots and web scrapers

Now that you know the difference between good and bad web scrapers and bots, how do you identify them and how do you stop the bad ones? The first thing to do is incorporate bot-identification into your website security program. There are a number of ways to do this.

In-house

When building an in house solution, it is important to understand that fighting off bots is an arms race. Every day web scraping technology evolves and new bots are written. To have an effective solution, you need a dynamic strategy that is always adapting.

When considering in-house solutions, here are a few common tactics:

    CAPTCHAs – Completely Automated Public Turing Tests to Tell Computers and Humans Apart (CAPTCHA), exist to ensure that user input has not been generated by a computer. This has been the most common method deployed because it is simple to integrate and can be effective, at least at first. The problem is that Captcha’s can be beaten with a little workand more importantly, they are a nuisance to end usersthat can lead to a loss of business.

    Rate Limiting- Advanced scraping utilities are very adept at mimicking normal browsing behavior but most hastily written scripts are not. Bots will follow links and make web requests at a much more frequent, and consistent, rate than normal human users. Limiting IP’s that make several requests per second would be able to catch basic bot behavior.

    IP Blacklists - Subscribing to lists of known botnets & anonymous proxies and uploading them to your firewall access control list will give you a baseline of protection. A good number of scrapers employ botnets and Tor nodes to hide their true location and identity. Always maintain an active blacklist that contains the IP addresses of known scrapers and botnets as well as Tor nodes.

    Add-on Modules – Many companies already own hardware that offers some layer of security. Now, many of those hardware providers are also offering additional modules to try and combat bot attacks. As many companies move more of their services off premise, leveraging cloud hosting and CDN providers, the market share for this type of solution is shrinking.

    It is also important to note that these types of solutions are a good baseline but should not be expected to stop all bots. After all, this is not the core competency of the hardware you are buying, but a mere plugin.

Some example providers are:

    Impreva SecureSphere- Imperva offers Web Application Firewalls, or WAF’s. This is an appliance that applies a set of rules to an HTTP connection. Generally, these rules cover common attacks such as Cross-site Scripting (XSS) and SQL Injection. By customizing the rules to your application, many attacks can be identified and blocked. The effort to perform this customization can be significant and needs to be maintained as the application is modified.

    F5 – ASM – F5 offers many modules on their BigIP load balancers, one of which is the ASM. This module adds WAF functionality directly into the load balancer. Additionally, F5 has added policy-based web application security protection.

Software-as-a-service

There are website security software options that include, and sometimes specialize in web scraping protection. This type of solution, from my perspective, is the most effective path.

The SaaS model allows someone else to manage the problem for you and respond with more efficiency even as new threats evolve.  Again, I’m admittedly biased as I co-founded Distil Networks.

When shopping for a SaaS solution to protect against web scraping, you should consider some of the following factors:

•    Does the provider update new threats and rules in real time?

•    How does the solution block suspected non-human visitors?

•    Which types of proactive blocking techniques, such as code injections, does the provider deploy?

•    Which of the reactive techniques, such as rate limiting, are used?

•    Does the solution look at all of your traffic or a snapshot?

•    Can the solution block bots before they reach your infrastructure – and your data?

•    What kind of latency does this solution introduce?

I hope you now have a clearer understanding of web scraping and why it has become so prevalent in travel, and even more important, what you should do to protect and leverage these occurrences.

Source: http://www.tnooz.com/article/what-you-need-to-know-about-web-scraping-how-to-understand-identify-and-sometimes-stop/

5 tips for scraping big websites

Scraping bigger websites can be a challenge if done the wrong way.

Bigger websites would have more data, more security and more pages. We’ve learned a lot from our years of crawling such large complex websites, and these tips could help solve some of your challenges

1. Cache pages visited for scraping

When scraping big websites, its always a good idea to cache the data you have already downloaded. So you don’t have to put load on the website again, in case you have start over again or that page is required again during scraping. Its effortless to cache to a key value store like redis.But Databases and filesystem caches are also good.

2. Don’t flood the website with large number of parallel requests , take it slow

Big websites posses algorithms to detect webscraping, large number of parallel requests from the same ip address will identify you as a Denial Of Service Attack on their website, and blacklist your IPs immediately. A better idea is to time your requests properly one after the other, giving it some human behavior. Oh!.. but scraping like that will take you ages. So balance requests using the average response time of the websites, and play around with the number of parallel requests to the website to get the right number.

3. Store the URLs that you have already fetched

You may want to keep a list of URLs you have already fetched, in a database or a key value store. What would you do if you scraper crashes after scraping 70% of the website. If you need to complete the remaining 30%, with out this list of URLs, you’ll waste a lot of time and bandwidth. Make sure you store this list of URLs

somewhere permanent, till you have all the required data. This could also be combined with the cache. This way, you’ll be able to resume scraping.

4. Split scraping into different phases

Its easier and safer if you split the scraping into multiple smaller phases. For example, you could split scraping a huge site into two. One for gathering links to the pages from which you need to extract data and another for downloading these pages to scrape content.

5. Take only whats required

Don’t grab or follow every link unless its required. You can define a proper navigation scheme to make the scraper visit only the pages required. Its always tempting to grab everything, but its just a waste of bandwidth, time and storage.

Source: http://learn.scrapehero.com/5-tips-for-scraping-big-websites/

How to Generate Sales Leads Using Web Scraping Services

The first stage of any selling process is what is popularly known as “lead generation”. This phase is what most businesses place at the apex of their sales concerns. It is a driving force that governs decision-making at its highest levels, and influences business strategy and planning. If you are about to embark on an outbound sales campaign and are in the process of looking for leads, you would acknowledge the fact that lead generation process is of extreme importance for any business.

Different lead generation techniques have been used over and over again by companies around the world to satiate this growing business need. Newer, more innovative methods have also emerged to help marketers in this process. One such method of lead generation that is fast catching on, and is poised to play a big role for businesses in the coming years, is web scraping. With web scraping, you can easily get access to multiple relevant and highly customized leads – a perfect starting point for any marketing, promotional or sales campaign.

The prominence of Web Scraping in overall marketing strategy

At present, levels of competition have risen sky high for most businesses. For success, lead generation and gaining insight about customer behavior and preferences is an essential business requirement. Web scraping is the process of scraping or mining the internet for information. Different tools and techniques can be used to harvest information from multiple internet sources based on relevance, and the structured and organized in a way that makes sense to your business. Companies that provide web scraping services essentially use web scrapers to generate a targeted lead database that your company can then integrate into its marketing and sales strategies and plans.

The actual process of web scraping involves creating scraping scripts or algorithms which crawl the web for information based on certain preset parameters and options. The scraping process can be customized and tuned towards finding the kind of data that your business needs. The script can extract data from websites automatically, collate and put together a meaningful collection of leads for business development.

Lead Generation Basics

At a very high level, any person who has the resources and the intent to purchase your product or service qualifies as a lead. In the present scenario, you need to go far deeper than that. Marketers need to observe behavior patterns and purchasing trends to ensure that a particular person qualifies as a lead. If you have a group of people you are targeting, you need to decide who the viable leads will be, acquire their contact information and store it in a database for further action.

List buying used to be a popular way to get leads, but their efficacy has dwindled over time. Web scraping is the fast coming up as a feasible lead generation technique, allowing you to find highly focused and targeted leads in short amounts of time. All you need is a service provider that would carry out the data mining necessary for lead generation, and you end up with a list of actionable leads that you can try selling to.

How Web Scraping makes a substantial difference

With web scraping, you can extract valuable predictive information from websites. Web scraping facilitates high quality data collection and allows you to structure marketing and sales campaigns better. To drive sales and maximize revenue, you need strong, viable leads. To facilitate this, you need critical data which encompasses customer behavior, contact details, buying patterns and trends, willingness and ability to spend resources, and a myriad of other aspects critical to ascertain the potential of an entity as a rewarding lead. Data mining through web scraping can be a great way to get to these factors and identifying the leads that would make a difference for your business.

Crawling through many different web locales using different techniques, web scraping services pick up a wealth of information. This highly relevant and specialized information instantly provides your business with actionable leads. Furthermore, this exercise allows you to fine-tune your data management processes, make more accurate and reliable predictions and projections, arrive at more effective, strategic and marketing decisions and customize your workflow and business development to better suit the current market.

The Process and the Tools

Lead generation, being one of the most important processes for any business, can prove to be an expensive proposition if not handled strategically. Companies spend large amounts of their resources acquiring viable leads they can sell to. With web scraping, you can dramatically cut down the costs involved in lead generation and take your business forward with speed and efficiency. Here are some of the time-tested web scraping tools which can come in handy for lead generation –

•    Website download software – Used to copy entire websites to local storage. All website pages are downloaded and the hierarchy of navigation and internal links preserve. The stored pages can then be viewed and scoured for information at any later time.     Web scraper – Tools that crawl through bulk information on the internet, extracting specific, relevant data using a set of pre-defined parameters.

•    Data grabber – Sifts through websites and databases fast and extracts all the information, which can be sorted and classified later.

•    Text extractor – Can be used to scrape multiple websites or locations for acquiring text content from websites and web documents. It can mine data from a variety of text file formats and platforms.

With these tools, web scraping services scrape websites for lead generation and provide your business with a set of strong, actionable leads that can make a difference.

Covering all Bases

The strength of web scraping and web crawling lies in the fact that it covers all the necessary bases when it comes to lead generation. Data is harvested, structured, categorized and organized in such a way that businesses can easily use the data provided for their sales leads. As discussed earlier, cold and detached lists no longer provide you with enough actionable leads. You need to look at various factors and consider them during your lead generation efforts –

•    Contact details of the prospect

•    Purchasing power and purchasing history of the prospect

•    Past purchasing trends, willingness to purchase and history of buying preferences of the prospect

•    Social markers that are indicative of behavioral patterns

•    Commercial and business markers that are indicative of behavioral patterns

•    Transactional details

•    Other factors including age, gender, demography, social circles, language and interests

All these factors need to be taken into account and considered in detail if you have to ensure whether a lead is viable and actionable, or not. With web scraping you can get enough data about every single prospect, connect all the data collected with the help of onboarding, and ascertain with conviction whether a particular prospect will be viable for your business.

Let us take a look at how web scraping addresses these different factors –

1. Scraping website’s

During the scraping process, all websites where a particular prospect has some participation are crawled for data. Seemingly disjointed data can be made into a sensible unit by the use of onboarding- linking user activities with their online entities with the help of user IDs. Documents can be scanned for participation. E-commerce portals can be scanned to find comments and ratings a prospect might have delivered to certain products. Service providers’ websites can be scraped to find if the prospect has given a testimonial to any particular service. All these details can then be accumulated into a meaningful data collection that is indicative of the purchasing power and intent of the prospect, along with important data about buying preferences and tastes.

2. Social scraping

According to a study, most internet users spend upwards of two hours every day on social networks. Therefore, scraping social networks is a great way to explore prospects in detail. Initially, you can get important identification markers like names, addresses, contact numbers and email addresses. Further, social networks can also supply information about age, gender, demography and language choices. From this basic starting point, further details can be added by scraping social activity over long periods of time and looking for activities which indicate purchasing preferences, trends and interests. This exercise provides highly relevant and targeted information about prospects can be constructively used while designing sales campaigns.

3. Transaction scraping

Through the scraping of transactions, you get a clear idea about the purchasing power of prospects. If you are looking for certain income groups or leads that invest in certain market sectors or during certain specific periods of time, transaction scraping is the best way to harvest meaningful information. This also helps you with competition analysis and provides you with pointers to fine-tune your marketing and sales strategies.

Using these varied lead generation techniques and finding the right balance and combination is key to securing the right leads for your business. Overall, signing up for web scraping services can be a make or break factor for your business going forward. With a steady supply of valuable leads, you can supercharge your sales, maximize returns and craft the perfect marketing maneuvers to take your business to an altogether new dimension.

Source: https://www.promptcloud.com/blog/how-to-generate-sales-leads-using-web-scraping-services/

Introducing ScrapeShield: Discover, Defend & Deter Content Scraping

If you're a publisher, whether an individual blogger or major media outlet, you've undoubtedly experienced content scraping. Searching the web for an article you've published or other original content you've created and you find it copied and republished on some other random website. Often the site will be full of ads. And, sometimes, it will even rank higher in search results than your original work.

While you may envision an army of individuals copying and pasting your content on their sites, the truth is content scraping is typically an automated process with bots that grab original content and then republish it without human intervention onto link farm sites. CloudFlare has blocked many of these bots automatically in the past, but we decided it was time to do something to more actively stop them.

Introducing ScrapeShield

ScrapeShield is an app created by the CloudFlare team. It incorporates several existing CloudFlare features like email obfuscation and hotlink protection that serve to protect from content scraping and adds a number of new features as well. Because we believe every publisher of original content should be able to understand and control how their work is used, we're providing ScrapeShield free for every CloudFlare user.

Detect, Defend & Deter

ScrapeShield has different elements to help you detect when your content is scraped, defend your site against content scrapers, and even deter content scrapers from targeting you in the first place. If you enable ScrapeShield, CloudFlare will automatically insert invisible tracking beacons in your content. When automated bots scrape your content, they pull the beacons along with them. CloudFlare detects these beacons when they ping from sites that aren't your own. You can access your ScrapeShield control panel to see where your content is being republished. Not only is this useful in showing scraping, but you can also see users who are reading your content through proxy services like Flipboard or Pulse.

The data from the content beacons is fed back into CloudFlare's protection system. As CloudFlare identifies content scraping bots, we automatically prevent them from accessing your site. Just like Project Honey Pot, the original inspiration for CloudFlare, used traps to detect when spammers were harvesting email addresses, CloudFlare now uses data from ScrapeShield to identify content scrapers and keep them off publishers' sites.

Maze

We didn't want to just stop scrapers from attacking sites on CloudFlare, we also wanted to tie up their resources so they couldn't harm the rest of the web. To do this, we created Maze. Maze routes known content scrapers who are visiting ScrapeShield-protected sites into a virtual labyrinth of gibirish and gobbledygook. We dynamically throttle the bandwidth and speed so instead of the pages loading as fast as possible, the connection is held open to the scrapers and their resources are tied up.

We use excess resources on the CloudFlare network to generate Maze, and it doesn't consume any of our publishers' resources or add any additional load to their sites. What's beautiful about the system is that the only way that content scrapers can be sure they're avoiding Maze is to avoid CloudFlare's IP addresses entirely. For any content scrapers who may be reading this, here's a helpful list of all of our IPs so you can make sure to stay away.

No Pinning

Finally, with the rise of sites like Pinterest, innocent content scraping may become even more prolific. While many sites welcome their images being pinned, we wanted to make it easy to opt out. ScrapeShield includes an option to add the no-pinning meta tag to your site to prevent your images from being pinned to the site. As other similar services include a mechanism to opt out, expect that we'll include an easy way for you to do so right from the ScrapeShield interface.

The health of the web depends on publishers creating original content getting credit for their creations. Cloud Flare is committed to building a better web and we're extremely excited about ScrapeShield as a new tool to help publishers do exactly that.

Source: https://blog.cloudflare.com/introducing-scrapeshield-discover-defend-dete/

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.

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.

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/

Scraping a website from a windows service

Question

Hi there.  I have a windows forms application that scrapes a website to retrieve some data.  I would like to implement the same functionality as a windows service.  The reason for this is to allow the program to run 24/7 without  having a user signed in. 

To that end, my current version of the program uses a web browser control (system.windows.forms.webbrowser) to navigate the pages, click the buttons, allow scripts to do their thing, etc.  I cannot figure out a way to do the same without the web browser control, but the web browser control cannot be instantiated in a windows service (because there is no user interface in a web service).

Does anyone have any brilliant ideas on how to get around this?

Thank you very much!

Answers

Hi Andy,

There is a tool which could let you manipulate anything you want on the website. This agile HTML parser builds a read/write DOM and supports plain XPATH or XSLT. It is a .NET code library that allows you to parse "out of the web" HTML files. The parser is very tolerant with "real world" malformed HTML. The object model is very similar to what proposes System.Xml, but for HTML documents (or streams). More information, please check:

http://htmlagilitypack.codeplex.com/

Have a nice day.

Best regards

All replies

You are not telling if you are using a .NET Express edition or not

You are not telling which Framework

You are not realy saying what data you are getting from the web site.

So

I made an example of service that work on any Studio edition (including the Express)

to install it, I supposed that you have at least the Framework2, so you will use something similar to:

    %SystemRoot%\Microsoft.NET\Framework\v2.0.50727\installutil /i C:\Test\MyWindowService\MyWindowService\bin\Release\MyWindowService.exe

In the example, I supposed that you are downloading some file from the site

You will need a reference to Windows.Form for the timer


Imports System.ServiceProcess

Imports System.Configuration.Install

Public Class WindowsService : Inherits ServiceBase

  Private Minute As Integer = 60000

  Private WithEvents Timer As New Timer With {.Interval = 30 * Minute, .Enabled = True}

  Public Sub New()

    Me.ServiceName = "MyService"

    Me.EventLog.Log = "Application"

    Me.CanHandlePowerEvent = True

    Me.CanHandleSessionChangeEvent = True

    Me.CanPauseAndContinue = True

    Me.CanShutdown = True

    Me.CanStop = True

  End Sub


  Private Sub Timer_Tick(ByVal sender As Object, ByVal e As System.EventArgs) Handles Timer.Tick

    If IO.File.Exists("C:\MyPath.Data") Then IO.File.Delete("C:\MyPath.Data")

    My.Computer.Network.DownloadFile("http://MyURL.com", "C:\MyPath.Data", "MyUserName", "MyPassword")

    'Do Something with the data downloaded

  End Sub

End Class

<Microsoft.VisualBasic.HideModuleName()> _

Module MainModule

  Public TheServiceName As String

  Public Sub main()

    Dim TheServiceApplication As New WindowsService

    TheServiceName = TheServiceApplication.ServiceName

    ServiceBase.Run(TheServiceApplication)

  End Sub

End Module

<System.ComponentModel.RunInstaller(True)> _

Public Class WindowsServiceInstaller : Inherits Installer

  Public Sub New()

    Dim serviceProcessInstaller As ServiceProcessInstaller = New ServiceProcessInstaller()

    Dim serviceInstaller As ServiceInstaller = New ServiceInstaller()

    serviceProcessInstaller.Account = ServiceAccount.LocalSystem

    serviceProcessInstaller.Username = Nothing

    serviceProcessInstaller.Password = Nothing

    serviceInstaller.DisplayName = "My Windows Service"

    serviceInstaller.StartType = ServiceStartMode.Automatic

    serviceInstaller.ServiceName = TheServiceName

    Me.Installers.Add(serviceProcessInstaller)

    Me.Installers.Add(serviceInstaller)

  End Sub

End Class



 Hello Andy,

Thanks for your post.

What do you want to scrape from the page? HttpWebRequest class ans WebClient class may be what you need. More information, please check:

The HttpWebRequest class provides support for the properties and methods defined in WebRequest and for additional properties and methods that enable the user to interact directly with servers using HTTP.

http://msdn.microsoft.com/en-us/library/system.net.httpwebrequest.aspx

The WebClient class provides common methods for sending data to or receiving data from any local, intranet, or Internet resource identified by a URI

http://msdn.microsoft.com/en-us/library/system.net.webclient.aspx

If you have any concenrs, please feel free to follow up.

Best regards



Hi Andy,

What about this problem on your side now? If you have any concerns, please feel free to follow up.

Have a nice day.

Best regards



Hi Andy,

When you come back, if you need further assistance about this issue, please feel free to let us know. We will continue to work with this issue.

Have a nice day.

Best regards



Thank you for the reply. Sorry it has taken me so long to respond.  I did not receive any notification that someone had replied!

I am using Visual Studio 2010 Ultimate Edition and the .NET framework 4.0.  Actually, I am upgrading some old code written in VB 6.0, but I can use the latest and greatest thats available.

The application uses a browser control to go to the page, fill in values, click on UI elements, read the HTML that returns, etc.  The purpose of the application is to collection useful information regularily/automatically.

I know how to create a web service, but using the web control in such a service is problematic because the web browser control was meant to be placed on a windows form.  I am not able to create a new instance of it in a project designated as a windows service.



Andy

Thank you for the reply. Sorry it has taken me so long to respond.  I did not receive any notification that someone had replied!

I thought a web request was for web services (retrieving information from them).  I am trying to retreive useful information from a website designed for interaction by a human, such as selecting items from lists and clicking buttons.   I currently use a web browser control to programmatically do what a person would do and get the pages back which in turn get parsed.

Andy



Hi Andy,

There is a tool which could let you manipulate anything you want on the website. This agile HTML parser builds a read/write DOM and supports plain XPATH or XSLT. It is a .NET code library that allows you to parse "out of the web" HTML files. The parser is very tolerant with "real world" malformed HTML. The object model is very similar to what proposes System.Xml, but for HTML documents (or streams). More information, please check:

http://htmlagilitypack.codeplex.com/

Have a nice day.

Best regards



Thanks for the suggestion.  I will go to that link and see if it will work.  I will update this post with what I find.

I am writing to check the status of the issue on your side. Would you mind letting us know the result of the suggestions? If you have any concerns, please feel free to follow up.

Have a nice day.



Best regards

Hi Liliane

Thank for the follow up reply.  I don't have an answer as of yet.  Implementing this is going to take time and I haven't been given the go-ahead by my boss to spend the time to pursue it.



Hi Andy,

Never minde. You could have a try when you feel free. If you have any further questions about this issue, please feel free to let us know. We will continue to work with you on this issue.

Have a nice day.

Best regards

Source: https://social.msdn.microsoft.com/Forums/vstudio/en-US/f5d565b1-236b-43c2-90c7-f5cc3b2c341b/scraping-a-website-from-a-windows-service

I Don’t Need No Stinking API: Web Scraping For Fun and Profit

If you’ve ever needed to pull data from a third party website, chances are you started by checking to see if they had an official API. But did you know that there’s a source of structured data that virtually every website on the internet supports automatically, by default?

scraper toolThat’s right, we’re talking about pulling our data straight out of HTML — otherwise known as web scraping. Here’s why web scraping is awesome:

Any content that can be viewed on a webpage can be scraped. Period.

If a website provides a way for a visitor’s browser to download content and render that content in a structured way, then almost by definition, that content can be accessed programmatically. In this article, I’ll show you how.

Over the past few years, I’ve scraped dozens of websites — from music blogs and fashion retailers to the USPTO and undocumented JSON endpoints I found by inspecting network traffic in my browser.

There are some tricks that site owners will use to thwart this type of access — which we’ll dive into later — but they almost all have simple work-arounds.

Why You Should Scrape

But first we’ll start with some great reasons why you should consider web scraping first, before you start looking for APIs or RSS feeds or other, more traditional forms of structured data.

Websites are More Important Than APIs

The biggest one is that site owners generally care way more about maintaining their public-facing visitor website than they do about their structured data feeds.

We’ve seen it very publicly with Twitter clamping down on their developer ecosystem, and I’ve seen it multiple times in my projects where APIs change or feeds move without warning.

Sometimes it’s deliberate, but most of the time these sorts of problems happen because no one at the organization really cares or maintains the structured data. If it goes offline or gets horribly mangled, no one really notices.

Whereas if the website goes down or is having issues, that’s a more of an in-your-face, drop-everything-until-this-is-fixed kind of problem, and gets dealt with quickly.

No Rate-Limiting

Another thing to think about is that the concept of rate-limiting is virtually non-existent for public websites.

Aside from the occasional captchas on sign up pages, most businesses generally don’t build a lot of defenses against automated access. I’ve scraped a single site for over 4 hours at a time and not seen any issues.

Unless you’re making concurrent requests, you probably won’t be viewed as a DDOS attack, you’ll just show up as a super-avid visitor in the logs, in case anyone’s looking.

Anonymous Access

There are also fewer ways for the website’s administrators to track your behavior, which can be useful if you want gather data more privately.

With APIs, you often have to register to get a key and then send along that key with every request. But with simple HTTP requests, you’re basically anonymous besides your IP address and cookies, which can be easily spoofed.

The Data’s Already in Your Face

Web scraping is also universally available, as I mentioned earlier. You don’t have to wait for a site to open up an API or even contact anyone at the organization. Just spend some time browsing the site until you find the data you need and figure out some basic access patterns — which we’ll talk about next.

Let’s Get to Scraping

So you’ve decided you want to dive in and start grabbing data like a true hacker. Awesome.

Just like reading API docs, it takes a bit of work up front to figure out how the data is structured and how you can access it. Unlike APIs however, there’s really no documentation so you have to be a little clever about it.

I’ll share some of the tips I’ve learned along the way.

Fetching the Data

So the first thing you’re going to need to do is fetch the data. You’ll need to start by finding your “endpoints” — the URL or URLs that return the data you need.

If you know you need your information organized in a certain way — or only need a specific subset of it — you can browse through the site using their navigation. Pay attention to the URLs and how they change as you click between sections and drill down into sub-sections.

The other option for getting started is to go straight to the site’s search functionality. Try typing in a few different terms and again, pay attention to the URL and how it changes depending on what you search for. You’ll probably see a GET parameter like q= that always changes based on you search term.

Try removing other unnecessary GET parameters from the URL, until you’re left with only the ones you need to load your data. Make sure that there’s always a beginning ? to start the query string and a & between each key/value pair.

Dealing with Pagination

At this point, you should be starting to see the data you want access to, but there’s usually some sort of pagination issue keeping you from seeing all of it at once. Most regular APIs do this as well, to keep single requests from slamming the database.

Usually, clicking to page 2 adds some sort of offset= parameter to the URL, which is usually either the page number or else the number of items displayed on the page. Try changing this to some really high number and see what response you get when you “fall off the end” of the data.

With this information, you can now iterate over every page of results, incrementing the offset parameter as necessary, until you hit that “end of data” condition.

The other thing you can try doing is changing the “Display X Per Page” which most pagination UIs now have. Again, look for a new GET parameter to be appended to the URL which indicates how many items are on the page.

Try setting this to some arbitrarily large number to see if the server will return all the information you need in a single request. Sometimes there’ll be some limits enforced server-side that you can’t get around by tampering with this, but it’s still worth a shot since it can cut down on the number of pages you must paginate through to get all the data you need.

AJAX Isn’t That Bad!

Sometimes people see web pages with URL fragments # and AJAX content loading and think a site can’t be scraped. On the contrary! If a site is using AJAX to load the data, that probably makes it even easier to pull the information you need.

The AJAX response is probably coming back in some nicely-structured way (probably JSON!) in order to be rendered on the page with Javscript.

All you have to do is pull up the network tab in Web Inspector or Firebug and look through the XHR requests for the ones that seem to be pulling in your data.

Once you find it, you can leave the crufty HTML behind and focus instead on this endpoint, which is essentially an undocumented API.

(Un)structured Data?

Now that you’ve figured out how to get the data you need from the server, the somewhat tricky part is getting the data you need out of the page’s markup.

Use CSS Hooks

In my experience, this is usually straightforward since most web designers litter the markup with tons of classes and ids to provide hooks for their CSS.

You can piggyback on these to jump to the parts of the markup that contain the data you need.

Just right click on a section of information you need and pull up the Web Inspector or Firebug to look at it. Zoom up and down through the DOM tree until you find the outermost <div> around the item you want.

This <div> should be the outer wrapper around a single item you want access to. It probably has some class attribute which you can use to easily pull out all of the other wrapper elements on the page. You can then iterate over these just as you would iterate over the items returned by an API response.

A note here though: the DOM tree that is presented by the inspector isn’t always the same as the DOM tree represented by the HTML sent back by the website. It’s possible that the DOM you see in the inspector has been modified by Javascript — or sometime even the browser, if it’s in quirks mode.

Once you find the right node in the DOM tree, you should always view the source of the page (“right click” > “View Source”) to make sure the elements you need are actually showing up in the raw HTML.

This issue has caused me a number of head-scratchers.

Get a Good HTML Parsing Library

It is probably a horrible idea to try parsing the HTML of the page as a long string (although there are times I’ve needed to fall back on that). Spend some time doing research for a good HTML parsing library in your language of choice.

Most of the code I write is in Python, and I love BeautifulSoup for its error handling and super-simple API. I also love its motto:

    You didn’t write that awful page. You’re just trying to get some data out of it. Beautiful Soup is here to help. :)

You’re going to have a bad time if you try to use an XML parser since most websites out there don’t actually validate as properly formed XML (sorry XHTML!) and will give you a ton of errors.

A good library will read in the HTML that you pull in using some HTTP library (hat tip to the Requests library if you’re writing Python) and turn it into an object that you can traverse and iterate over to your heart’s content, similar to a JSON object.

Some Traps To Know About

I should mention that some websites explicitly prohibit the use of automated scraping, so it’s a good idea to read your target site’s Terms of Use to see if you’re going to make anyone upset by scraping.

For two-thirds of the website I’ve scraped, the above steps are all you need. Just fire off a request to your “endpoint” and parse the returned data.

But sometimes, you’ll find that the response you get when scraping isn’t what you saw when you visited the site yourself.

When In Doubt, Spoof Headers

Some websites require that your User Agent string is set to something they allow, or you need to set certain cookies or other headers in order to get a proper response.

Depending on the HTTP library you’re using to make requests, this is usually pretty straightforward. I just browse the site in my web browser and then grab all of the headers that my browser is automatically sending. Then I put those in a dictionary and send them along with my request.

Note that this might mean grabbing some login or other session cookie, which might identify you and make your scraping less anonymous. It’s up to you how serious of a risk that is.

Content Behind A Login

Sometimes you might need to create an account and login to access the information you need. If you have a good HTTP library that handles logins and automatically sending session cookies (did I mention how awesome Requests is?), then you just need your scraper login before it gets to work.

Note that this obviously makes you totally non-anonymous to the third party website so all of your scraping behavior is probably pretty easy to trace back to you if anyone on their side cared to look.

Rate Limiting

I’ve never actually run into this issue myself, although I did have to plan for it one time. I was using a web service that had a strict rate limit that I knew I’d exceed fairly quickly.

Since the third party service conducted rate-limiting based on IP address (stated in their docs), my solution was to put the code that hit their service into some client-side Javascript, and then send the results back to my server from each of the clients.

This way, the requests would appear to come from thousands of different places, since each client would presumably have their own unique IP address, and none of them would individually be going over the rate limit.

Depending on your application, this could work for you.

Poorly Formed Markup

Sadly, this is the one condition that there really is no cure for. If the markup doesn’t come close to validating, then the site is not only keeping you out, but also serving a degraded browsing experience to all of their visitors.

It’s worth digging into your HTML parsing library to see if there’s any setting for error tolerance. Sometimes this can help.

If not, you can always try falling back on treating the entire HTML document as a long string and do all of your parsing as string splitting or — God forbid — a giant regex.

Source: https://blog.hartleybrody.com/web-scraping/

What are the ethics of web scraping?

Someone recently asked: "Is web scraping an ethical concept?" I believe that web scraping is absolutely an ethical concept. Web scraping (or screen scraping) is a mechanism to have a computer read a website. There is absolutely no technical difference between an automated computer viewing a website and a human-driven computer viewing a website. Furthermore, if done correctly, scraping can provide many benefits to all involved.

There are a bunch of great uses for web scraping. First, services like Instapaper, which allow saving content for reading on the go, use screen scraping to save a copy of the website to your phone. Second, services like Mint.com, an app which tells you where and how you are spending your money, uses screen scraping to access your bank's website (all with your permission). This is useful because banks do not provide many ways for programmers to access your financial data, even if you want them to. By getting access to your data, programmers can provide really interesting visualizations and insight into your spending habits, which can help you save money.

That said, web scraping can veer into unethical territory. This can take the form of reading websites much quicker than a human could, which can cause difficulty for the servers to handle it. This can cause degraded performance in the website. Malicious hackers use this tactic in what’s known as a "Denial of Service" attack.

Another aspect of unethical web scraping comes in what you do with that data. Some people will scrape the contents of a website and post it as their own, in effect stealing this content. This is a big no-no for the same reasons that taking someone else's book and putting your name on it is a bad idea. Intellectual property, copyright and trademark laws still apply on the internet and your legal recourse is much the same. People engaging in web scraping should make every effort to comply with the stated terms of service for a website. Even when in compliance with those terms, you should take special care in ensuring your activity doesn't affect other users of a website.

One of the downsides to screen scraping is it can be a brittle process. Minor changes to the backing website can often leave a scraper completely broken. Herein lies the mechanism for prevention: making changes to the structure of the code of your website can wreak havoc on a screen scraper's ability to extract information. Periodically making changes that are invisible to the user but affect the content of the code being returned is the most effective mechanism to thwart screen scrapers. That said, this is only a set-back. Authors of screen scrapers can always update them and, as there is no technical difference between a computer-backed browser and a human-backed browser, there's no way to 100% prevent access.

Going forward, I expect screen scraping to increase. One of the main reasons for screen scraping is that the underlying website doesn't have a way for programmers to get access to the data they want. As the number of programmers (and the need for programmers) increases over time, so too will the need for data sources. It is unreasonable to expect every company to dedicate the resources to build a programmer-friendly access point. Screen scraping puts the onus of data extraction on the programmer, not the company with the data, which can work out well for all involved.

Source: https://quickleft.com/blog/is-web-scraping-ethical/

Why Data mining is still a powerful tool to help companies

The ability of Data mining technologies to sift through volumes of data and arrive at predictive information to empower businesses can in no way be undermined. The advent of new techniques and technologies has made the practice more affordable by organizations both big and small. The new technologies have not only helped in reducing the overhead costs of running the data mining exercise, but also simplified the practice making it more accessible for smaller and mid-size companies employ it in their organizational processes. In the current era, information is power and Web Data Mining Technologies are stretching the limits of their capabilities to help organizations acquire that power.

Data Mining Ensures Better Business Decisions

 Organizations usually have access to large databases which store millions of historical data record. Traditional practices of hands-on analysis of patterns and trends of all available data proved to be too cumbersome to be pursued and were soon replaced with shorter and more selective data sets. This caused hidden patterns to remain hidden thus blocking off possibilities for organizations to grow and evolve. However, the advent of Data Mining as a technology that automates the identification of complex patterns in those databases changed all that. Organizations, now, are engaging in a thorough analysis of massive data sets and are moving ahead to extracting meanings and patterns from them. The analysis helps to unlock the hidden patterns and enables organizations to predict future market behavior and be geared with proactive and knowledge driven decisions for the benefit of their business.

Data Mining provides Fraud Detection Capabilities

 Loss in Revenue has definite adverse impacts on a company’s morale. It slackens productivity and slows down their growth. Fraud is one of the common malpractices that eat into the organization’s revenue earning capability. Data Mining helps to prevent this and ensures a steady rise in their revenue graph. Data mining models can be built to predict consumer behavior patterns which help in effectively detecting fraud.

Data Mining Evolves to be Business Focused

 Traditional Data Mining technologies were focused more on algorithms and statistics on delivering results which, though good failed to address the business issues appropriately. The new age data mining technologies, however, have evolved to become business focused. They understand the needs that drive the business and utilize the strong statistical algorithms built into their system to explore, collect, analyze and summarize data that can be made to work for better health of the business.

Data Mining has become more Granular

 As technology evolves, organizations leverage the benefits it generates. Integration of fundamental data mining functionalists into database engines is one such innovation that has helped organizations to thoroughly benefit from its effect. Mining data from within the database instead of Web Data Extraction the data and then analyzing it saves valuable time for the organization. Moreover, as organizations can now drill down into more granular levels of the data therefore there is a higher possibility of ensuring accuracy. Moreover, as data mining software now have a more direct access to the data sets within the database, there is a higher possibility of ensuring a smoother workflow and hence a better performance.

Conclusion

 Data mining, though capable of helping organizations generate good things, however, needs to be used intelligently. It has to be strongly aligned with the organization’s goals and principles in order to ensure appropriate performance that would strengthen the organization adequately.

We are leading Webdatascraping.us company and enough capable to extract website information, review scraping, contact information scraping, business directory scraping, email list scraping etc.

Web Data Extraction- The most convenient and easy way to extract data from the internet

Web data extraction is the most proficient technique that will help you find the pertaining data for your existing business or any personal use. Many times, we find that experts’ copy and paste information manually from web pages or download the entire website which is a waste of time and effort.

Now with the new technique of Web data extraction you can crawl through loads and loads of web pages in order to extract particular data and at the same time save this data in the following manner

•     CSV FILE
•     XML FILE or
•     Any other custom format for future use.

Below given are some instances of Web data extraction processes:

•     Take a government portal, extracting names of citizens for a survey
•     Search for competitor websites for product pricing and feature information
•     Utilize web scraping to download images from a stock photography site for website design

How can Web Data Extraction serve you?

 You can extract data from any kind of websites like

Extract Data from any kind of Websites: Directories, Classified Websites, News, Websites, Blogs, Articles, and Job Portals, Search Engines, eCommerce Websites, Social Media Websites and any kind of websites whose content can be accessible. Extract Emails, Contacts, Price/Rate, Features, Contact Names, Contact Details, Full Text, Live updates, ASINs, Meta Tags, Address, Phone, Fax, Latitude & Longitude, Images, Links, Reviews, Ratings, etc. Help in Data Collection, Competitor Analysis, Research, Business Intelligence, Social Media Trend analysis, Brand Monitoring, Lead Data Collection, Website & Competitor Web Monitoring, etc. Deliver Data in any Database, Excel, CSV, Access, Text, My SQL, SQL, Oracle, etc. and in any format Custom Services of Web Data Extraction as per client need one time Data Delivery or Continued/Scheduled Data Delivery

The next is Website Data Scraping:

 Web site Data Scraping is the process of extracting data from a website by using a particular software program available from proven website only.

This extracted data can be utilized by any person and for any purposes as per their needs and wants; data extracted can be used in different industries. There are many companies providing best Website data scraping services.

It is one such field which has active developments and also shares a common objective that needs a breakthrough in the following:

•     Text Processing
•     Semantic Understanding
•     Artificial Intelligence
•     Human Computer Interactions

There are many users or end users, companies and experts that need information or data that is accessible in some or the other format. In such cases Web Data Extraction can tailor the need of extracting data from any proven source and preserve the data on a particular destination.

The source platform contains:

•     Excel
•     CSV
•     MySQL and
•     Others

Moreover, the technique of Web data extractor can also extract information from various websites like Google, Amazon, LinkedIn, EBay and many others.

It can also extract data from eCommerce shopping websites, or other social networking websites, any public websites, classifieds websites, job portal websites and any other search engine websites.

Websitedatascraping.com is enough capable to web data scraping, website data scraping, web scraping services, website scraping services, data scraping services, product information scraping and yellowpages data scraping.

Internet Data Mining - How Does it Help Businesses?

Internet has become an indispensable medium for people to conduct different types of businesses and transactions too. This has given rise to the employment of different internet data mining tools and strategies so that they could better their main purpose of existence on the internet platform and also increase their customer base manifold.

Internet data-mining encompasses various processes of collecting and summarizing different data from various websites or webpage contents or make use of different login procedures so that they could identify various patterns. With the help of internet data-mining it becomes extremely easy to spot a potential competitor, pep up the customer support service on the website and make it more customers oriented.

There are different types of internet data_mining techniques which include content, usage and structure mining. Content mining focuses more on the subject matter that is present on a website which includes the video, audio, images and text. Usage mining focuses on a process where the servers report the aspects accessed by users through the server access logs. This data helps in creating an effective and an efficient website structure. Structure mining focuses on the nature of connection of the websites. This is effective in finding out the similarities between various websites.

Also known as web data_mining, with the aid of the tools and the techniques, one can predict the potential growth in a selective market regarding a specific product. Data gathering has never been so easy and one could make use of a variety of tools to gather data and that too in simpler methods. With the help of the data mining tools, screen scraping, web harvesting and web crawling have become very easy and requisite data can be put readily into a usable style and format. Gathering data from anywhere in the web has become as simple as saying 1-2-3. Internet data-mining tools therefore are effective predictors of the future trends that the business might take.

If you are interested to know something more on Web Data Mining and other details, you are welcome to the Screen Scraping Technology site.

Source: http://ezinearticles.com/?Internet-Data-Mining---How-Does-it-Help-Businesses?&id=3860679

Three Common Methods For Web Data Extraction

Probably the most common technique used traditionally to extract data from web pages this is to cook up some regular expressions that match the pieces you want (e.g., URL's and link titles). Our screen-scraper software actually started out as an application written in Perl for this very reason. In addition to regular expressions, you might also use some code written in something like Java or Active Server Pages to parse out larger chunks of text. Using raw regular expressions to pull out the data can be a little intimidating to the uninitiated, and can get a bit messy when a script contains a lot of them. At the same time, if you're already familiar with regular expressions, and your scraping project is relatively small, they can be a great solution.

Other techniques for getting the data out can get very sophisticated as algorithms that make use of artificial intelligence and such are applied to the page. Some programs will actually analyze the semantic content of an HTML page, then intelligently pull out the pieces that are of interest. Still other approaches deal with developing "ontologies", or hierarchical vocabularies intended to represent the content domain.

There are a number of companies (including our own) that offer commercial applications specifically intended to do screen-scraping. The applications vary quite a bit, but for medium to large-sized projects they're often a good solution. Each one will have its own learning curve, so you should plan on taking time to learn the ins and outs of a new application. Especially if you plan on doing a fair amount of screen-scraping it's probably a good idea to at least shop around for a screen-scraping application, as it will likely save you time and money in the long run.

So what's the best approach to data extraction? It really depends on what your needs are, and what resources you have at your disposal. Here are some of the pros and cons of the various approaches, as well as suggestions on when you might use each one:

Raw regular expressions and code

Advantages:

- If you're already familiar with regular expressions and at least one programming language, this can be a quick solution.

- Regular expressions allow for a fair amount of "fuzziness" in the matching such that minor changes to the content won't break them.

- You likely don't need to learn any new languages or tools (again, assuming you're already familiar with regular expressions and a programming language).

- Regular expressions are supported in almost all modern programming languages. Heck, even VBScript has a regular expression engine. It's also nice because the various regular expression implementations don't vary too significantly in their syntax.

Disadvantages:

- They can be complex for those that don't have a lot of experience with them. Learning regular expressions isn't like going from Perl to Java. It's more like going from Perl to XSLT, where you have to wrap your mind around a completely different way of viewing the problem.

- They're often confusing to analyze. Take a look through some of the regular expressions people have created to match something as simple as an email address and you'll see what I mean.

- If the content you're trying to match changes (e.g., they change the web page by adding a new "font" tag) you'll likely need to update your regular expressions to account for the change.

- The data discovery portion of the process (traversing various web pages to get to the page containing the data you want) will still need to be handled, and can get fairly complex if you need to deal with cookies and such.

When to use this approach: You'll most likely use straight regular expressions in screen-scraping when you have a small job you want to get done quickly. Especially if you already know regular expressions, there's no sense in getting into other tools if all you need to do is pull some news headlines off of a site.

Ontologies and artificial intelligence

Advantages:

- You create it once and it can more or less extract the data from any page within the content domain you're targeting.

- The data model is generally built in. For example, if you're extracting data about cars from web sites the extraction engine already knows what the make, model, and price are, so it can easily map them to existing data structures (e.g., insert the data into the correct locations in your database).

- There is relatively little long-term maintenance required. As web sites change you likely will need to do very little to your extraction engine in order to account for the changes.

Disadvantages:

- It's relatively complex to create and work with such an engine. The level of expertise required to even understand an extraction engine that uses artificial intelligence and ontologies is much higher than what is required to deal with regular expressions.

- These types of engines are expensive to build. There are commercial offerings that will give you the basis for doing this type of data extraction, but you still need to configure them to work with the specific content domain you're targeting.

- You still have to deal with the data discovery portion of the process, which may not fit as well with this approach (meaning you may have to create an entirely separate engine to handle data discovery). Data discovery is the process of crawling web sites such that you arrive at the pages where you want to extract data.

When to use this approach: Typically you'll only get into ontologies and artificial intelligence when you're planning on extracting information from a very large number of sources. It also makes sense to do this when the data you're trying to extract is in a very unstructured format (e.g., newspaper classified ads). In cases where the data is very structured (meaning there are clear labels identifying the various data fields), it may make more sense to go with regular expressions or a screen-scraping application.

Screen-scraping software

Advantages:

- Abstracts most of the complicated stuff away. You can do some pretty sophisticated things in most screen-scraping applications without knowing anything about regular expressions, HTTP, or cookies.

- Dramatically reduces the amount of time required to set up a site to be scraped. Once you learn a particular screen-scraping application the amount of time it requires to scrape sites vs. other methods is significantly lowered.

- Support from a commercial company. If you run into trouble while using a commercial screen-scraping application, chances are there are support forums and help lines where you can get assistance.

Disadvantages:

- The learning curve. Each screen-scraping application has its own way of going about things. This may imply learning a new scripting language in addition to familiarizing yourself with how the core application works.

- A potential cost. Most ready-to-go screen-scraping applications are commercial, so you'll likely be paying in dollars as well as time for this solution.

- A proprietary approach. Any time you use a proprietary application to solve a computing problem (and proprietary is obviously a matter of degree) you're locking yourself into using that approach. This may or may not be a big deal, but you should at least consider how well the application you're using will integrate with other software applications you currently have. For example, once the screen-scraping application has extracted the data how easy is it for you to get to that data from your own code?

When to use this approach: Screen-scraping applications vary widely in their ease-of-use, price, and suitability to tackle a broad range of scenarios. Chances are, though, that if you don't mind paying a bit, you can save yourself a significant amount of time by using one. If you're doing a quick scrape of a single page you can use just about any language with regular expressions. If you want to extract data from hundreds of web sites that are all formatted differently you're probably better off investing in a complex system that uses ontologies and/or artificial intelligence. For just about everything else, though, you may want to consider investing in an application specifically designed for screen-scraping.

As an aside, I thought I should also mention a recent project we've been involved with that has actually required a hybrid approach of two of the aforementioned methods. We're currently working on a project that deals with extracting newspaper classified ads. The data in classifieds is about as unstructured as you can get. For example, in a real estate ad the term "number of bedrooms" can be written about 25 different ways. The data extraction portion of the process is one that lends itself well to an ontologies-based approach, which is what we've done. However, we still had to handle the data discovery portion. We decided to use screen-scraper for that, and it's handling it just great. The basic process is that screen-scraper traverses the various pages of the site, pulling out raw chunks of data that constitute the classified ads. These ads then get passed to code we've written that uses ontologies in order to extract out the individual pieces we're after. Once the data has been extracted we then insert it
into a database.

Source:http://ezinearticles.com/?Three-Common-Methods-For-Web-Data-Extraction&id=165416