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 - About Hand Scraped Flooring

Hand scraped hardwood flooring is one of the best floors that you can install in your house.

Advantages of Hand Scraped Hardwood Flooring

The product comes with a number of advantages which include:

Antique and modern technology: The floor professionally brings out the best elements of both antique and modern technology. The modern elements are in the quality of the product.

Unique patterns: Who doesn't want to be unique? These floors allow you to create your unique design. If you are going to use a machine, all you need to do is to set the machine such that it creates the pattern that you want. If the floor will be scraped by a craftsman, you should ask the craftsman to craft your desired pattern.

Character: The different depths in the floor provide you with character and color that you can't find in other types of floors. As the sun changes its angle during the day, the nooks and valleys on the board lit differently thus providing your board with an endless rich appearance.

Durability: Experts have been able to show that hand-scraped hardwood retains its look for a long time. If your kid or pet hits the floor, the dent just blends with the rest of the character making it hard for people to tell that there is a dent.

Making the floors shine again

Although, the scraped floors are designed to look worn and aged, they are made from modern wood which needs to be taken care of in order to retain its original look.

To make the floors shine again you need to remove all the dust and dirt that might be causing the wood to look dull.

After doing this you should mix 1 gallon of warm water with ½ teaspoon of dishwashing detergent and use it to clean the surface of the floor. The aim of doing this is to remove any stains that might be on the floor. When you complete doing this you should dampen the piece of cloth with club soda and then use another piece of cloth to buff the wood until it shines.

Conclusion

This is what you need to know about hand scraped hardwood flooring. When cleaning the floors you should avoid using oil based soaps as they dull the surface making your efforts worthless.

If the above method of shining the floor doesn't work, you should mix one part white vinegar and one part of cooking oil and use it to clean the floor.

Source: http://ezinearticles.com/?About-Hand-Scraped-Flooring&id=8990255

Rvest: easy web scraping with R

Rvest is new package that makes it easy to scrape (or harvest) data from html web pages, by libraries like beautiful soup. It is designed to work with magrittr so that you can express complex operations as elegant pipelines composed of simple, easily understood pieces. Install it with:

install.packages("rvest")

rvest in action

To see rvest in action, imagine we’d like to scrape some information about The Lego Movie from IMDB. We start by downloading and parsing the file with html():

library(rvest)

lego_movie <- html("http://www.imdb.com/title/tt1490017/")

To extract the rating, we start with selectorgadget to figure out which css selector matches the data we want: strong span. (If you haven’t heard of selectorgadget, make sure to read vignette("selectorgadget") – it’s the easiest way to determine which selector extracts the data that you’re interested in.) We use html_node() to find the first node that matches that selector, extract its contents with html_text(), and convert it to numeric with as.numeric():

lego_movie %>%

  html_node("strong span") %>%
  html_text() %>%
  as.numeric()

#> [1] 7.9

We use a similar process to extract the cast, using html_nodes() to find all nodes that match the selector:

lego_movie %>%

  html_nodes("#titleCast .itemprop span") %>%
  html_text()

#>  [1] "Will Arnett"     "Elizabeth Banks" "Craig Berry"  

#>  [4] "Alison Brie"     "David Burrows"   "Anthony Daniels"

#>  [7] "Charlie Day"     "Amanda Farinos"  "Keith Ferguson"

#> [10] "Will Ferrell"    "Will Forte"      "Dave Franco"  

#> [13] "Morgan Freeman"  "Todd Hansen"     "Jonah Hill"

The titles and authors of recent message board postings are stored in a the third table on the page. We can use html_node() and [[ to find it, then coerce it to a data frame with html_table():

lego_movie %>%

  html_nodes("table") %>%
  .[[3]] %>%
  html_table()

#>                                              X 1            NA

#> 1 this movie is very very deep and philosophical   mrdoctor524

#> 2 This got an 8.0 and Wizard of Oz got an 8.1...  marr-justinm

#> 3                         Discouraging Building?       Laestig

#> 4                              LEGO - the plural      neil-476

#> 5                                 Academy Awards   browncoatjw

#> 6                    what was the funniest part? actionjacksin

Other important functions

    If you prefer, you can use xpath selectors instead of css: html_nodes(doc, xpath = "//table//td")).

    Extract the tag names with html_tag(), text with html_text(), a single attribute with html_attr() or all attributes with html_attrs().

    Detect and repair text encoding problems with guess_encoding() and repair_encoding().
    Navigate around a website as if you’re in a browser with html_session(), jump_to(), follow_link(), back(), and forward(). Extract, modify and submit forms with html_form(), set_values() and submit_form(). (This is still a work in progress, so I’d love your feedback.)

To see these functions in action, check out package demos with demo(package = "rvest").

Source: http://www.r-bloggers.com/rvest-easy-web-scraping-with-r/

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

Getting Data from the Web Scraping

You’ve tried everything else, and you haven’t managed to get your hands on the data you want. You’ve found the data on the web, but, alas — no download options are available and copy-paste has failed you. Fear not, there may still be a way to get the data out. For example you can:

•    Get data from web-based APIs, such as interfaces provided by online databases and many modern web applications (including Twitter, Facebook and many others). This is a fantastic way to access government or commercial data, as well as data from social media sites.

•    Extract data from PDFs. This is very difficult, as PDF is a language for printers and does not retain much information on the structure of the data that is displayed within a document. Extracting information from PDFs is beyond the scope of this book, but there are some tools and tutorials that may help you do it.

•    Screen scrape web sites. During screen scraping, you’re extracting structured content from a normal web page with the help of a scraping utility or by writing a small piece of code. While this method is very powerful and can be used in many places, it requires a bit of understanding about how the web works.

With all those great technical options, don’t forget the simple options: often it is worth to spend some time searching for a file with machine-readable data or to call the institution which is holding the data you want.

In this chapter we walk through a very basic example of scraping data from an HTML web page.

What is machine-readable data?

The goal for most of these methods is to get access to machine-readable data. Machine readable data is created for processing by a computer, instead of the presentation to a human user. The structure of such data relates to contained information, and not the way it is displayed eventually. Examples of easily machine-readable formats include CSV, XML, JSON and Excel files, while formats like Word documents, HTML pages and PDF files are more concerned with the visual layout of the information. PDF for example is a language which talks directly to your printer, it’s concerned with position of lines and dots on a page, rather than distinguishable characters.

Scraping web sites: what for?

Everyone has done this: you go to a web site, see an interesting table and try to copy it over to Excel so you can add some numbers up or store it for later. Yet this often does not really work, or the information you want is spread across a large number of web sites. Copying by hand can quickly become very tedious, so it makes sense to use a bit of code to do it.

The advantage of scraping is that you can do it with virtually any web site — from weather forecasts to government spending, even if that site does not have an API for raw data access.

What you can and cannot scrape

There are, of course, limits to what can be scraped. Some factors that make it harder to scrape a site include:

•    Badly formatted HTML code with little or no structural information e.g. older government websites.

•    Authentication systems that are supposed to prevent automatic access e.g. CAPTCHA codes and paywalls.

•    Session-based systems that use browser cookies to keep track of what the user has been doing.

•    A lack of complete item listings and possibilities for wildcard search.

•    Blocking of bulk access by the server administrators.

Another set of limitations are legal barriers: some countries recognize database rights, which may limit your right to re-use information that has been published online. Sometimes, you can choose to ignore the license and do it anyway — depending on your jurisdiction, you may have special rights as a journalist. Scraping freely available Government data should be fine, but you may wish to double check before you publish. Commercial organizations — and certain NGOs — react with less tolerance and may try to claim that you’re “sabotaging” their systems. Other information may infringe the privacy of individuals and thereby violate data privacy laws or professional ethics.

Tools that help you scrape

There are many programs that can be used to extract bulk information from a web site, including browser extensions and some web services. Depending on your browser, tools like Readability (which helps extract text from a page) or DownThemAll (which allows you to download many files at once) will help you automate some tedious tasks, while Chrome’s Scraper extension was explicitly built to extract tables from web sites. Developer extensions like FireBug (for Firefox, the same thing is already included in Chrome, Safari and IE) let you track exactly how a web site is structured and what communications happen between your browser and the server.

ScraperWiki is a web site that allows you to code scrapers in a number of different programming languages, including Python, Ruby and PHP. If you want to get started with scraping without the hassle of setting up a programming environment on your computer, this is the way to go. Other web services, such as Google Spreadsheets and Yahoo! Pipes also allow you to perform some extraction from other web sites.

How does a web scraper work?

Web scrapers are usually small pieces of code written in a programming language such as Python, Ruby or PHP. Choosing the right language is largely a question of which community you have access to: if there is someone in your newsroom or city already working with one of these languages, then it makes sense to adopt the same language.

While some of the click-and-point scraping tools mentioned before may be helpful to get started, the real complexity involved in scraping a web site is in addressing the right pages and the right elements within these pages to extract the desired information. These tasks aren’t about programming, but understanding the structure of the web site and database.

When displaying a web site, your browser will almost always make use of two technologies: HTTP is a way for it to communicate with the server and to request specific resource, such as documents, images or videos. HTML is the language in which web sites are composed.

The anatomy of a web page

Any HTML page is structured as a hierarchy of boxes (which are defined by HTML “tags”). A large box will contain many smaller ones — for example a table that has many smaller divisions: rows and cells. There are many types of tags that perform different functions — some produce boxes, others tables, images or links. Tags can also have additional properties (e.g. they can be unique identifiers) and can belong to groups called ‘classes’, which makes it possible to target and capture individual elements within a document. Selecting the appropriate elements this way and extracting their content is the key to writing a scraper.

Viewing the elements in a web page: everything can be broken up into boxes within boxes.

To scrape web pages, you’ll need to learn a bit about the different types of elements that can be in an HTML document. For example, the <table> element wraps a whole table, which has <tr> (table row) elements for its rows, which in turn contain <td> (table data) for each cell. The most common element type you will encounter is <div>, which can basically mean any block of content. The easiest way to get a feel for these elements is by using the developer toolbar in your browser: they will allow you to hover over any part of a web page and see what the underlying code is.

Tags work like book ends, marking the start and the end of a unit. For example <em> signifies the start of an italicized or emphasized piece of text and </em> signifies the end of that section. Easy.

Figure 57. The International Atomic Energy Agency’s (IAEA) portal (news.iaea.org)

An example: scraping nuclear incidents with Python

NEWS is the International Atomic Energy Agency’s (IAEA) portal on world-wide radiation incidents (and a strong contender for membership in the Weird Title Club!). The web page lists incidents in a simple, blog-like site that can be easily scraped.

To start, create a new Python scraper on ScraperWiki and you will be presented with a text area that is mostly empty, except for some scaffolding code. In another browser window, open the IAEA site and open the developer toolbar in your browser. In the “Elements” view, try to find the HTML element for one of the news item titles. Your browser’s developer toolbar helps you connect elements on the web page with the underlying HTML code.

Investigating this page will reveal that the titles are <h4> elements within a <table>. Each event is a <tr> row, which also contains a description and a date. If we want to extract the titles of all events, we should find a way to select each row in the table sequentially, while fetching all the text within the title elements.

In order to turn this process into code, we need to make ourselves aware of all the steps involved. To get a feeling for the kind of steps required, let’s play a simple game: In your ScraperWiki window, try to write up individual instructions for yourself, for each thing you are going to do while writing this scraper, like steps in a recipe (prefix each line with a hash sign to tell Python that this not real computer code). For example:

# Look for all rows in the table

# Unicorn must not overflow on left side.

Try to be as precise as you can and don’t assume that the program knows anything about the page you’re attempting to scrape.

Once you’ve written down some pseudo-code, let’s compare this to the essential code for our first scraper:

import scraperwiki

In this first section, we’re importing existing functionality from libraries — snippets of pre-written code. scraperwiki will give us the ability to download web sites, while lxml is a tool for the structured analysis of HTML documents. Good news: if you are writing a Python scraper with ScraperWiki, these two lines will always be the same.

doc_text = scraperwiki.scrape(url)

doc = html.fromstring(doc_text)

Next, the code makes a name (variable): url, and assigns the URL of the IAEA page as its value. This tells the scraper that this thing exists and we want to pay attention to it. Note that the URL itself is in quotes as it is not part of the program code but a string, a sequence of characters.

We then use the url variable as input to a function, scraperwiki.scrape. A function will provide some defined job — in this case it’ll download a web page. When it’s finished, it’ll assign its output to another variable, doc_text. doc_text will now hold the actual text of the website — not the visual form you see in your browser, but the source code, including all the tags. Since this form is not very easy to parse, we’ll use another function, html.fromstring, to generate a special representation where we can easily address elements, the so-called document object model (DOM).

In this final step, we use the DOM to find each row in our table and extract the event’s title from its header. Two new concepts are used: the for loop and element selection (.cssselect). The for loop essentially does what its name implies; it will traverse a list of items, assigning each a temporary alias (row in this case) and then run any indented instructions for each item.

The other new concept, element selection, is making use of a special language to find elements in the document. CSS selectors are normally used to add layout information to HTML elements and can be used to precisely pick an element out of a page. In this case (Line. 6) we’re selecting #tblEvents tr which will match each <tr> within the table element with the ID tblEvents (the hash simply signifies ID). Note that this will return a list of <tr> elements.

As can be seen on the next line (Line. 7), where we’re applying another selector to find any <a> (which is a hyperlink) within a <h4> (a title). Here we only want to look at a single element (there’s just one title per row), so we have to pop it off the top of the list returned by our selector with the .pop() function.

Note that some elements in the DOM contain actual text, i.e. text that is not part of any markup language, which we can access using the [element].text syntax seen on line 8. Finally, in line 9, we’re printing that text to the ScraperWiki console. If you hit run in your scraper, the smaller window should now start listing the event’s names from the IAEA web site.

You can now see a basic scraper operating: it downloads the web page, transforms it into the DOM form and then allows you to pick and extract certain content. Given this skeleton, you can try and solve some of the remaining problems using the ScraperWiki and Python documentation:

•    Can you find the address for the link in each event’s title?

•    Can you select the small box that contains the date and place by using its CSS class name and extract the element’s text?

•    ScraperWiki offers a small database to each scraper so you can store the results; copy the relevant example from their docs and adapt it so it will save the event titles, links and dates.

•    The event list has many pages; can you scrape multiple pages to get historic events as well?

As you’re trying to solve these challenges, have a look around ScraperWiki: there are many useful examples in the existing scrapers — and quite often, the data is pretty exciting, too. This way, you don’t need to start off your scraper from scratch: just choose one that is similar, fork it and adapt to your problem.

Source: http://datajournalismhandbook.org/1.0/en/getting_data_3.html

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/

Endorsing web scraping

With more than 200 projects delivered, we stand firmly for new challenges every day. We have served above 60 clients and have won 86% of repeat business, as our main core is customer delight. Successive Softwares was approached by a client having a very exclusive set of requirements. For their project they required customised data mining, in real time to offer profitable information to their customers. Requirement stated scrapping of stock exchange data in real time so that end users can be eased in their marketing decisions. This posed as an ambitious task for us because it required processing of huge amount of data on a routine basis. We welcomed it as an event to evolve and do something aside of classic web application development.

We started with mock-ups, pursuing our very first step of IMPART Framework (Innovative Mock-up based Prototypes Analyzed to develop Reengineered Technology). Our team of experts thought of all the potential requirements with a flow and materialized it flawlessly into our mock up. It was a strenuous tasks but our excitement to do something which others still do not think of, filled our team with confidence and energy and things began to roll out perfectly. We presented our mock-up and statistics to the client as per our expectation client choose us, impressed with the efforts.

We started gathering requirements from client side and started to formulate design about the flow. The project required real time monitoring of stock exchange together with Prices, Market Turnover and then implement them into graphs. The front end part was an easy deal, we were already adept in playing with data the way required. The intractable task was to get the data. We researched and found that it can be achieved either with API or with Web Scarping and we moved with latter because of the limitations in API.

Web scraping is a compelling technique to get the required information straight out of the web page. Lack of documentation and not much forbearance forced us to make a slow start, but we kept all the requirements clear and new that we headed in the right direction.  We divided the scraping process into bits of different but related tasks. Firstly we needed to find the data which has to be captured, some of the problems faced were pagination and use of AJAX but with examination of endpoints in URL and the requests made when data is drawn, we surmounted these problems easily.

After targeting our data we focused on HTML parser which could extract data form all the targets. Using PHP we developed a parser extracting all the information and saving them in Database in a structured way.  After the required data present at our end we easily manipulated it into tables and charts and we used HIGHSTOCK for that. Entire Client side was developed in PHP with Zend frame work and we used MySQL 5.7 for server side.

During the whole development cycle our QA team insured we were delivering a quality product following all standards. We kept our client in the loop during the whole process keeping them informed about every step. Clients were also assured as they watched their project starting from scratch which developed into full fledge website. The process followed a strict time line releasing regular builds and implementing new improvements. We stood up to the expectation our client and delivered a product just as they visualized it to be.

Source: http://www.successivesoftwares.com/endorsing-web-scraping/

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/

Roles of Data Mining in Predicting, Tracking, and Containing the Ebola Outbreak

One of the most diverse continents on earth, Africa astounds the world with its vast savannas and great deserts and with its ancient architecture and modern cities, but Africa also has its share of tragedies and woes.

First identified in Democratic Republic of Congo’s Ebola River in 1976, Ebola Hemorrhagic Fever, a deadly zoonotic disease caused by Ebola virus, has been spreading in West Africa like a wildfire, engulfing everything on its way and creating widespread panic.

What has added insult to injury is the fact that the region has long endured the severe consequences of civil wars and social conflicts, and diseases like malaria, HIV/AIDS, yellow fever, cholera etc. have remained endemic to the region for a long time, causing tens of thousands of deaths every year.

Reportedly, Ebola has already killed at least 2,296 people, and there are about 3,685 confirmed cases of infection. Mortality rate has been swinging between 50% to 90%, depending on the quality of care and nutrition. According to WHO, the disease is likely to infect as much as 20,000 people before it is finally brought under control.

Crisis of Data

When it comes to healthcare management, clinical data is one of the key components. The value of data becomes more urgent in the emergency situation like that of West Africa. The more relevant data you have, the bigger picture you can create for taking aggressive measures. To use Peter Drucker’s words, “What gets measured gets managed.”

Factual data is a precondition for the doctors and health science experts working in the field for measuring and managing the situation. Data helps them to assess their successes or failures and reorient their actions. One of the important reasons why the fight against the Ebola outbreak is turning out into a losing battle is the insufficiency of data. Recently, Scientific American magazine wrote:

Right now, there are not even enough beds for sick patients nor enough data coming in to help track cases. Surveillance and tracking of those who were possibly exposed to Ebola remain inadequate.

In Science magazine, Gretchen Vogel suggests that the death toll of Ebola patients could be much higher than it is currently estimated. She says, “Exactly how many unrecorded Ebola deaths have occurred will never be known. Health officials are keeping track of suspected and probable cases, many of which are people who died before they could be tested.” Greg Slabodkin voices similar concerns in Health Data Management and points at the need of an integrated global biosurveillance system.

The absence of reliable and actionable data has badly hampered the efforts of combatting Ebola and providing proper medical care to the victims. CDC Director Dr. Tom Frieden describes it as a “fog-of-war situation”.

Data Mining: Bots Were the First to Warn

When you flip the coin, however, the situation is not completely bleak and desperate. Even if Big Data technologies have fallen short in predicting, tracking, and containing the epidemic, mainly due to the lack of data from the ground, it has not entirely failed. Data scientists and healthcare experts world over are making concerted efforts to know, track, and defeat the Ebola virus—some on the ground and some in their labs.

The increasing level of collaboration among the biomedical specialists, geneticist, virologists, and IT experts has definitely contributed to slow down the transmission of the virulent disease dubbed as “the plague of modern day”. Médecins Sans Frontières and Healthmap.org are the excellent examples in this regard.

    “By deploying bots and crawlers and by using advanced machine learning algorithms, the Boston-based global infectious disease surveillance system, HealthMap was able to predict and raise concerns about the spread of a mysterious hemorrhagic fever in West Africa nine days earlier than WHO did.”

Run by a team of 45 researchers, epidemiologists, and software developers at Boston Children’s Hospital, HealthMap mines data from search engine queries, social media platforms, health information sites, news reports and crowd-sourced information to track the transmission of the disease and provides an up-to-date timeline report with an interactive map, making it easier for the international health agencies to devise more effective action plans.

HealthMap serves as a good example of how crucial Big Data and data mining technologies could be for handling a healthcare emergency with fact-based and data-driven decisions.

Ebola Data

In their letter to The Lancet, research scientist Rashid Ansumana and his colleagues, working on Ebola in Sierra Leone, highlighted on the need of developing epidemic surveillance systems “by adopting new data-sharing technologies.” They wrote, “Emerging technologies can help early warning systems, outbreak response, and communication between health-care providers, wildlife and veterinary professionals, local and national health authorities, and international health agencies.”

Data-Driven Initiatives to Control the Outbreak

The era of systematic use of data for making better epidemiological predictions and for finding effective healthcare solutions began with Google Flue Trends in 2007, and the rapidly developing tools, technologies, and practices in Big Data have increased the roles of data in healthcare management.

There are a number of data-driven undertakings in progress which have contributed to counter the raging spread of Ebola. Brockmann Lab, run by Professor Dirk Brockmann and his colleagues, for example, has created a computer model for studying correlations and probabilities in the explosion of new cases of infection.

World Airtraffic  Transportation and Relative Import Risk, Source: Brockmann Lab

By applying computational and statistical models, they predict which areas, cities or regions in the world are at the risk of becoming the next Ebola epidemic hotspots. Similarly, Alessandro Vespignani–a network scientist, statistical physicist, and Northeastern professor–has been using human mobility network data to track the cases of Ebola infection and dissemination.

The Swedish NGO Flowminder Foundation has been aggregating, mining, and analyzing anonymized mobile phone location data and is developing national mobility estimates for West Africa to help the local and international agencies to combat the disease.

Meanwhile, innovations with Epi Info VHF, a software tool for case management, contact tracing, analysis and reporting services for Ebola and other hemorrhagic fever outbreaks and OpenStreetMap project for getting location information and spatial data of the affected areas have further helped to guide the intervention initiatives.

However, with all optimism about the growing roles of Big Data and data mining, we also need to be mindful about their limitations. Newsweek aptly puts: “While no media-trawling bot could ever replace national and international health agencies, such tools may be starting to help fill in some of the most gaping holes in real-time knowledge.”

Source: http://www.grepsr.com/blog/data-mining-tracking-ebola-outbreak/

The Features of the "Holographic Meridian Scraping Therapy"

1. Systematic nature: Brief introduction to the knowledge of viscera, meridians and points in traditional Chinese medicine, theory of holographic diagnosis and treatment; preliminary discussion of the treatment and health care mechanism of scraping therapy; systemat­ic introduction to the concrete methods of the holographic meridian scraping therapy; enumerating a host of therapeutic methods of scraping for disorders in both Chinese and Western medicine to em­body a combination of disease differentiation and syndrome differen­tiation; and summarizing the health care scraping methods. It is a practical handbook of gua sha.

2. Scientific: Applying the theories of Chinese and Western medicine to explain the health care and treatment mechanism and clinical applications of scraping therapy; introducing in detail the practical manipulations, items for attention, and indications and contraindications of the scraping therapy. Here are introduced repre­sentative diseases in different clinical departments, for which scrap­ing therapy has a better curative effect and the therapeutic methods of scraping for these diseases. Stress is placed on disease differentia­tion in Western medicine and syndrome differentiation in Chinese medicine, which should be combined in practical application.

Although there are more than 140,000 kinds of disease known to modem medicine, all diseases are related to dysfunction of the 14 meridians and internal organs, according to traditional Chinese med­icine. The object of scraping therapy is to correct the disharmony in the meridians and internal organs to recover the normal bodily func­tions. Thus, the scraping of a set of meridian points can be used to treat many diseases. In the section on clinical application only about 100 kinds of common diseases are discussed, although the actual number is much more than that. For easy reference the "Index of Diseases and Symptoms" is appended at the back of the book.

3. Practical: Using simple language and plenty of pictures and diagrams to guarantee that readers can easily leam, memorize and apply the principles of scraping therapy. As long as they master the methods explained in Chapter Three, readers without any medical knowledge can apply scraping therapy to themselves or others, with reference to the pictures in Chapters Four and Five. Besides scraping therapy, herbal treatment for each disease or syndrome is explained and may be used in combination with the scraping techniques.

Referring to the Holographic Meridian Hand Diagnosis and pic­tures at the back of the book will enhance accuracy of diagnosis and increase the effectiveness of scraping therapy.

Since the first publication and distribution of the Chinese edition of the book in July 1995, it has been welcomed by both medical specialists and lay people. In March 1996 this book was republished and adopted as a textbook by the School for Advanced Studies of Traditional Chinese Medicine affiliated to the Institute of the Acu­puncture and Moxibustion of the China Academy of Traditional Chi­nese Medicine.

In order to bring this health care method to more and more peo­ple and to make traditional Chinese medicine better appreciated They have modified and replenished this book in the spirit of constant im­provement. They hope that they may make a contribution to the health care of mankind with this natural therapy which has no side-effects and causes no pollution.

They hope that the Holographic Meridian Scraping Therapy can help the health and happiness of more and more families in the world.

Source: http://ezinearticles.com/?The-Features-of-the-Holographic-Meridian-Scraping-Therapy&id=5005031

Dapper: The Scraper for the Common Man

Sometimes, especially with Web 2.0 companies, jargon can get a little bit out of hand. When someone says that a service allows you to "build an API for any website", it can be a bit difficult to understand what that really means.

However, put simply, Dapper is a scraper. Nothing more. It allows you to scrape content from a Web page and convert it into an XML document that can be easily used at another location. Though you won't find the words "scrape" or "scraper" anywhere on its site, that is exactly what it does.

What separates Dapper from other scrapers, both legitimate and illegitimate, is that it is both free and easy to use. In short, it makes the process of setting up the scraper simple enough for your every day Internet user. While one has never needed to be a geek to scrape RSS feeds, now the technologically impaired can scrape content from any site, even those that don't publish RSS feeds.

Though the TechCrunch profile of the service says that Dapper "aims to offer some legitimate, valuable services and set up a means to respect copyright" others are expressing concern about the potential for copyright violations, especially by spam bloggers.

Either way though, both the cause for concern and the potential dangers are very, very real.

What is Dapper

When a user goes to create a new "Dapp", he or she first needs to provide a series of links. These links must be on the same domain and in similar formats (IE: Google searches for different terms or different blog posts on a single site) for the service to work. Once the links have been defined, the user is then taken to a GUI where they pick out fields.

In a simple example where the user would create their own RSS feed for a blog, the post title might be one field, perhaps called "post title" and the body would be a second, perhaps called "post body". Dapper, much like the service social bookmarking Clipmarks, is able able to intelligently select blocks of text on a Web page, making it easy to ensure that the entire post body is selected and that extraneous information is omitted.

Once the fields have been selected, the user can then either create groups based upon those fields or simply save the dapp for future use. Once the Dapp has been saved, they can then use it to create both raw XML data, an RSS feed, a Google Gadget or any number of other output files that can be easily used in other services.

If you are interested in viewing a demo of Dapper, you can do so at this link.

There is little doubt that Dapper is an impressive service. It has taken the black art of scraping and made it into a simple, easy-to-use application that just about anyone can pick up. Though it might take a few tries to create a working Dapp, and certainly spending some time reading up on the service is required, most will find it easy to use, especially when compared to the alternatives.

However, it's this ease of use that has so many worried. Though scrapers have been around for many years, they have been either difficult to use or expensive. Dapper's power, when combined with its price tag and sheer ease of use, has many wondered that it might be ushering not a new age for the Web, but a new age for scrapers seeking to abuse other's hard work.

Cause for Concern

While being easy to use or free is not necessarily a problem in and of itself, in the rush to enable users to make an API for any site, they forget that many sites don't have one or restrict access to their APIs for very good reasons. RSS scraping is perhaps the biggest copyright issue bloggers face. It enables a plagiarist or spammer to not only steal all of the content on the blog right then, but also all of the content that will be posted in the future. This is a huge concern for many bloggers, especially those concerned about performing well in the search engines.

This has prompted many blogs to either disable their RSS feeds, truncate them or move them to a feed monitoring service such as Feedburner. However, if users can simply create their own RSS feeds with ease, these protections are circumvented and Webmasters lose control over their content.

Even with potential copyright abuse issues aside, Dapper creates potential problems for Webmasters. It bypasses the usual metrics that site owners have. A user who reads a site, or large portions of it, through a Dapp will not be counted in either the feed statistics or, depending on how Dapper is set up, even in the site's logs. All the while, the site is spending precious resources to feed the Dapp, taking money out of the Webmaster's pocket.

This combination of greater expense, less traffic and less accurate metrics can be dangerous to Webmasters who are working to get accurate traffic counts, visitor feedback or revenue.

Worse still, Dapp users also bypass any ads or other monetization tools that might be included in the site or the original RSS feed. This has a direct impact on sites trying to either turn a profit or, like this one, recoup some of the costs of hosting.

Despite this, it's the copyright concerns that reign supreme. Though screen scraping is not necessarily an evil technology, it is the sinister uses that have gotten the most attention and, sadly, seem to be the most common, especially in regards to blogs.

Even if the makers of Dapper is aiming to add copyright protection at a later date, the service is fully functional today and, though the FAQ states that they will "comply with any verified request by the lawful owner of the content to cease using his content," there is no opt-out procedure, no DMCA information on the United States Copyright Office Web site, no information on how to prevent Dapper from accessing your site and nothing but a contact page to get in touch with the makers of the service.

(Note: An email sent to the makers of Dapper on the 22nd has, as of yet, gone unanswered)

In addition to creating a potential copyright nightmare for Webmasters the site seems to be setting itself up for a lawsuit. In addition to not being DMCA Safe Harbor compliant (PDF), thus opening it up to copyright infringement lawsuits directly, the service seems to be vulnerable to a lawsuit under the MGM v. Grokster case, which found that service providers can be sued for infringement conducted by its users if they fail an "inducement" test. Sadly for Dapper, simply saying that it is the user's responsibility is not adequate to pass such a test, as Grokster found out. The failure to offer filtering technology and encouragement to create API's for "any" site are both likely strikes against Dapper in that regard.

To make matters more grim, copyright is not the only issue scrapers have to worry about, as one pair of lawyers put it, there are at least four different different legal theories that make scraping illegal including the computer fraud and abuse act, trespass against chattels and breach of contract. All in all, copyright is practically the least of Dapper's problems.

When it's all said and done, there is a lot of room for concern, not just on the part of Webmasters that might be affected by Dapper or its users, but also its makers. These intellectual property and other legal issues could easily sink the entire project.

Conclusions

It is obvious that a lot of time and effort went into creating Dapper. It's a very powerful, easy to use service that opens up interesting possibilities. I would hate to see the service used for ill and I would hate even worse to see all of the hard work that went into it lost because of intellectual property issues.

However, in its current incarnation, it seems likely that Dapper is going to encounter significant resistance on the IP front. There is little, if any protection or regard for intellectual property under the current system and, once bloggers find out that their content is being syndicated without their permission by the service, many are likely to start raising a fuss.

Even though Dapper has gotten rave reviews in the Web 2.0 community, it seems likely that traditional bloggers and other Web site owners will have serious objections to it. Those people, sadly, most likely have never heard of Dapper at this point.

With that being said, it is a service everyone needs to make note of. The one thing that is for certain is that it will be in the news again. The only question is what light will it be under.

Source: https://www.plagiarismtoday.com/2006/08/24/dapper-the-scraper-for-the-common-man/

Web Scraping Services Are Important Tools For Knowledge

Data extraction and web scraping techniques are important tools to find relevant data and information for personal or business use. Many companies, self-employed to copy and paste data from web pages. This process is very reliable, but very expensive as it is a waste of time and effort to get results. This is because the data collected and spent less resources and time required to collect these data are compared.

At present, several mining companies and their websites effective web scraping technique specifically for the thousands of pages of information developed culture can be traced. The information from a CSV file, database, XML file, or any other source with the required format is alameda. understanding of correlations and patterns in the data, so that policies can be designed to assist decision making. The information can also be stored for future reference.

The following are some common examples of data extraction process:

In order to rule through a government portal, citizens who are reliable for a given survey name removed.

Competitive pricing and data products include scraping websites

To access the web site or web design Stock download the videos and photos of scratching

Automatic Data Collection

It regularly collects data on a regular basis. Automated data collection techniques are very important because they find the company’s customer trends and market trends to help. By determining market trends, it is possible to understand customer behavior and predict the likelihood of the data will change.

The following are some examples of automated data collection:

Monitoring of special hourly rates for stocks

collects daily mortgage rates from various financial institutions

on a regular basis is necessary to check the weather

By using web scraping services, you can extract all data related to your business. Then analyzed the data to a spreadsheet or database can be downloaded and compared. Storing data in a database or in a required format and interpretation of the correlations to understand and makes it easier to identify hidden patterns.

Data extraction services, it is possible pricing, email, databases, profile data, and consistently to competitors for information about the data. Different techniques and processes designed to collect and analyze data, and has developed over time. Web Scraping for business processes that have beaten the market recently is one. It is a process from various sources such as websites and databases with large amounts of data provides.

Some of the most common methods used to scrape web crawling, text, fun, DOM analysis and include matching expression. After the process is only analyzers, HTML pages or meaning can be achieved through annotations. There are many different ways of scaling data, but more importantly is working toward the same goal. The main purpose of using web scraping service to retrieve and compile data in databases and web sites. In the business world is to remain relevant to the business process.

The central question about the relevance of web scraping contact. The process is relevant to the business world? The answer is yes. The fact that it is used by large companies in the world and many awards speaks derivatives.

Source: http://www.selfgrowth.com/articles/web-scraping-services-are-important-tools-for-knowledge

Customized Web Data Extraction Solutions for Business

As you begin leading your business on the path to success, competitive analysis forms a major part of your homework. You have already mobilized your efforts in finding the appropriate website data scrapping tool that will help you to collect relevant data from competitive websites and shape them up into useable information. There is however a need to look for a customized approach in your search for Data Extraction tools in order to leverage its benefits in the best possible way.

Off-the-shelf Tools Impede Data Extraction

 In the current scenario, Internet Technologies are evolving in abundance. Every organization leverages this development and builds their websites using a different programming language and technology. Off-the-shelf Website Data extraction tools are unable to interpret this difference. They fail to understand the data elements that need to be captured and end up in gathering data without any change in the software source codes.

As a result of this incapability in their technology, off-the-shelf solutions often deliver unclean, incomplete and also inaccurate data. Developers need to contribute a humungous effort in cleaning up and structuring the data to make it useable. However, despite the time-consuming activity, data seldom metamorphoses into the desired information. Also the personnel dealing with the clean-up process needs to have sufficient technical expertise in order to participate in the activities. The endeavor however results in an impediment to the whole process of data extraction leaving you thirsting for the required information to augment business growth.

Understanding how Web Extraction tools work

Web Scrapping tools are designed to extract data from the web automatically. They are usually small pieces of code written using programming languages such as Python, Ruby or PHP depending upon the expertise of the community building it. There are however several single-click models available which tends to make life easier for non-technical personnel.

The biggest challenge faced by a successful web extractor tool is to know how to tackle the right page and the right elements on that page in order to extract the desired information. Consequently, a web extractor needs to be designed to understand the anatomy of a web page in order to accomplish its task successfully. It should be designed to interpret the meaning of HTML elements like , table rows () within those tables, and table data (<td>) cells within those rows in order to extract the exact data. It will also be interfacing with the

element which are blocks of text and know how to extract the desired information from it.

Customized Solutions for your business

 Customized Solutions are provided by most Data Scraping experts. These software's help to minimize the cumbersome effort of writing elaborate codes to successfully accomplish the feat of data extraction. They are designed to seamlessly search competitive websites,identify relevant data elements, and extract appropriate data that will be useful for your business. Owing to their focused approach, these tools provide clean and accurate data thereby eliminating the need to waste valuable time and effort in any clean-up effort.

Most customized data extraction tools are also capable of delivering the extracted data in customized formats like XML or CSV. It also stores data in local databases like Microsoft Access, MySQL, or Microsoft SQL.

Customized Data scraping solutions therefore help you take accurate and informed decisions in order to define effective business strategies.

Source: http://scraping-solutions.blogspot.in/2014_07_01_archive.html

Benefits of Scraping Data from Real Estate Website

With so much of growth in the recent times in real estate industry, it is likely that companies would want to create something different or use another method, so as to get desired benefits. Thus, it is best to go with the technological advancements and create real estate websites to get an edge over others in the industry. And to get all the information regarding website content, one can opt for real estate data scraping methods.

About real estate website scraping

Internet has become an important part of our daily lives and in industry marketing procedures too. With the use of website scraping one can easily scrape real estate listing from various websites. One just needs the help of experts and with proper software and tools; they can easily collect all the relevant real estate data from the required real estate websites and make a structured file containing the information. With internet becoming a valid platform for information and data submitted by numerous sources from around the globe, it is necessary to gather them all in one place for companies. In this way, the company can know what it lacks and work upon their strategies so as to gain profit and get to the top of the business world by taking one step at a time.

Uses of real estate website scraping

With proper use of website scraping one can collect and scrape the real estate listings which can help the company in the real estate market area. One can draw the attention of potential customers by designing the company strategies in such a way as contemplating the changing trends in the real estate global arena. All this is done with the help of the data collected from various real estate websites. With the help of proper website, one can collect the data and these get updated whenever new information gets into the web portal. In this way the company is kept updated about the various changes happening around the global market and thus, ensure in making plans regarding the company. This way one can plan ahead and take steps that can lead to the company gaining profits in future.

Thus, with the help of proper real estate website scraping one can be sure of getting all the information regarding real estate market. This way one can work upon making the company move as per the market trends and get a stronghold in real estate business.

Source: https://3idatascraping.wordpress.com/2013/09/25/benefit-of-scraping-data-from-real-estate-website/

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

How to Properly Scrape Windows During The Cleaning Process

Removing ordinary dirt such as dust, fingerprints, and oil from windows seem simple enough. However, sometimes, you may find stubborn caked-on dirt or debris on your windows that cannot be removed by standard window cleaning techniques such as scrubbing or using a squeegee. The best way to remove caked-on dirt on your windows is to scrape it off. Nonetheless, you have to be extra careful when you are scraping your windows, because they can be easily scratched and damaged. Here are a number of rules that you need to follow when you are scraping windows.

Rule No. 1: It is recommended that you use a professional window scraper to remove caked-on dirt and debris from your windows. This type of scraper is specially made for use on glass, and it comes with certain features that can prevent scratching and other kinds of damage.

Rule No. 2: It is important to inspect your window scraper before using it. Take a look at the blade of the scraper and make sure that it is not rusted. Also, it must not be bent or chipped off at the corners. If you are not certain whether the blade is in a good enough condition, you should just play it safe by using a new blade.

Rule No. 3: When you are working with a window scraper, always use forward plow-like scraping motions. Scrape forward and lift the scraper off the glass, and then scrape forward again. Try not to slide the scraper backwards, because you may trap debris under the blade when you do so. Consequently, the scraper may scratch the glass.

Rule No. 4: Be extra cautious when you are using a window scraper on tempered glass. Tempered glass may have raised imperfections, which make it more vulnerable to scratches. To find out if the window that you are scraping is made of tempered glass, you have to look for a label in one of its corners.

Window Scraping Procedures

Before you start scraping, you have to wet your window with soapy water first. Then, find out how the window scraper works by testing it in a corner. Scrape on the same spot three or four times in forward motion. If you find that the scraper is moving smoothly and not scratching the glass, you can continue to work on the rest of the window. On the other hand, if you feel as if the scraper is sliding on sandpaper, you have to stop scraping. This indicates that the glass may be flawed and have raised imperfections, and scraping will result in scratches.

After you have ascertained that it is safe to scrape your window, start working along the edges. It is best that you start scraping from the middle of an edge, moving towards the corners. Work in a one or two inch pattern, until all the edges of the glass are properly scraped. After that, scrape the rest of the window in a straight pattern of four or five inches, working from top to bottom. If you find that the window is beginning to dry while you are working, wet it with soapy water again.

Source: http://ezinearticles.com/?How-to-Properly-Scrape-Windows-During-The-Cleaning-Process&id=6592930

What is HTML Scraping and how it works

There are many reasons why there may be a requirement to pull data or information from other sites, and usually the process begins after checking whether the site has an official API. There are very few people who are aware about the presence of structured data that is supported by every website automatically. We are basically talking about pulling data right from the HTML, also referred to as HTML scraping. This is an awesome way of gleaning data and information from third party websites.

Any webpage content that can be viewed can be scraped without any trouble. If there is any way provided by the website to the browser of the visitor to download content and use the same in a highly structured manner, in that case, accessing of the content programmatically is possible. HTML scraping works in an amazing manner.

Before indulging in HTML scraping, one can inspect the browser for network traffic. Site owners have a couple of tricks up their sleeve to thwart this access, but majority of them can be worked around.

Before moving on to how HTML scraping works, we must understand the reasons behind the same. Why is scraping needed? Once you get a satisfactory answer to this question, you can start looking for RSS or API feeds or various other traditional structured data forms. It is significant to understand that when compared with APIs, websites are more significant.

The most important advantage of the same is the maintenance of their websites where a lot of visitors visit rather than safeguarding structured data feeds. With Tweeter, the same has been publicly seen when it clamps down on the developer ecosystem. Many times, API feeds change or move without any prior warning. Many times, it can also be a deliberate attempt, but mostly, such issues or problems erupt as there is no authority or an organization that maintains or takes care of the structured data. It is rarely noticed, if the same gets severely mangled or goes offline. In case the website has certain issues or the website no longer works, the problem is more in the form of a ball in your court requiring dealing with the same without losing any time. api-comic-image

Rate limiting is another factor that needs a lot of thinking and in case of public websites, it virtually doesn’t exist. Besides some occasional sign up pages or captchas, many business websites fail to create and built defenses against any unwarranted automated access. Many times, a single website can be scraped for four hours straight without anyone noticing. There are chances that you would not be viewed under DDOS attack unless concurrent requests are being made by you. You will be seen just as an avid visitor or an enthusiast in the logs, that too, in case anyone is looking.

Another factor in HTML scraping is that one can easily access any website anonymously. Behavior tracking can be done with a few ways by the administrator of the website and this turns out to be beneficial if you want to privately gather the data. Many times, registration is imperative with APIs in order to get key and with any request being sent, this key also needs to be sent. But, in case of simple and straightforward HTTP requests, the visitor can stay anonymous besides cookies and IP address, which can again be spoofed.

The availability of HTML scraping is universal and there is no need to wait for the opening of the site for an API or for contacting anyone in the organization. One simply needs to spend some time and browse websites at a leisurely pace until the data you want is available and then find out the basic patterns to access the same.

Now you need to don a hat of a professional scraper and simply dive in. Initially, it may take some time to work up figuring out the way the data have been structured and the way it can be accessed just as we read APIs. If there is no documentation unlike APIs, you need to be a little more smart about it and use clever tricks.

Some of the most used tricks are

Data Fetching

The first thing that is required is data fetching. Find endpoints to begin with, that is the URLs that can help in returning the data that is required. If you are pretty sure about the data and the way it should be structured so as to match your requirements, you will require a particular subset for the same and later you can indulge in site browsing using the navigation tools.

GET Parameter

The URLs must be paid attention to and see the way it changes as you indulge in clicking between the sections and the way they divide into various subsections. Before starting, the other option that can be used is to straight away go to the search functionality of the site. Certain terms can be typed and the URL needs to be focused again for watching the changes on the basis of what is being searched. A GET parameter will be probably seen like q which changes on the basis of the search term used by you. Other GET parameters that are not being used can be removed from the URL until only the ones that are needed are left for data loading. Before a query string, there must always be a “?” beginning.

Now the time has come when you would have started to come across the data that you would like to see and want to access, but sometimes, there may be certain pagination issues that require to be dealt with. Due to these issues, you may not be able to see the data in its entirety. Single requests are kept away by many APIs as well from database slamming. Many times, clicking the next page can add some offset parameter that helps in data visibility on the page. All these steps will help you succeed in HTML scraping.

Source: https://www.promptcloud.com/blog/what-is-html-scraping-and-how-it-works/

The Nasty Problem with Scraping Results from the Engines

One theme that I've been concerned with this week centers around data transparency in the search engine world. Search engines provide information that is critical to the business of optimizing and growing a business on the web, yet barriers to this data currently force many companies to use methods of data extraction that violate the search engines' terms of service.

Specifically, we're talking about two pieces of information that no large-scale, successful web operation should be without. These include rankings (the position of their site(s) vs. their competitors) for important keywords and link data (currently provided most accurately through Yahoo!, but also available through MSN and in lower quality formats from Google).

Why do marketers and businesses need this data so badly? First we'll look at rankings:

•    For large sites in particular, rankings across the board will go up or down based on their actions and the actions of their competition. Any serious company who fails to monitor tweaks to their site, public relations, press and optimization tactics in this way will lose out to competitors who do track this data and, thus, can make intelligent business decisions based on it.

•    Rankings provide a benchmark that helps companies estimate their global reach in the search results and make predictions about whether certain areas of extension or growth make logical sense. If a company must decide on how to expand their content or what new keywords to target or even if they can compete in new markets, the business intelligence that can be extracted from large swaths of ranking data is critical.

•    Rankings can be mapped directly to traffic, allowing companies to consider advertising, extending their reach or forming partnerships

And, on the link data side:

•    Temporal link information allows marketers to see what effects certain link building, public relations and press efforts have on a site's link profile. Although some of this data is available through referring links in analytics programs, many folks are much more interested in the links that search engines know about and count, which often includes many more than those that pass traffic (and also ignores/doesn't count some that do pass traffic).

•    Link data may provide references for reputation management or tracking of viral campaigns - again, items that analytics don't entirely encompass.

•    Competitive link data may be of critical importance to many marketers - this information can't be tracked any other way.

I admit it. SEOmoz is a search engine scraper - we do it for our free public tools, for our internal research and we've even considered doing it for clients (though I'm seriously concerned about charging for data that's obtained outside TOS). Many hundreds of large firms in the search space (including a few that are 10-20X our size) do it, too. Why? Because search engine APIs aren't accurate.

Let's look at each engine's abilities and data sources individually. Since we've got a few hundred thousand points of data (if not more) on each, we're in a good position to make calls about how these systems are working.

Google (all APIs listed here):

•    Search SOAP API - provides ranking results that are massively different from almost every datacenter. The information is often less than useless, it's actually harmful, since you'll get a false sense of what's happening with your positions.

•    AJAX Search API - This is really designed to be integrated with your website, and the results can be of good quality for that purpose, but it really doesn't serve the job of providing good stats reporting.

•    AdSense & AdWords APIs - In all honesty, we haven't played around with these, but the fact that neither will report the correct order of the ads, nor will they show more than 8 ads at a time tells me that if a marketer needed this type of data, the APIs wouldn't work.

Yahoo! (APIs listed here):

•    Search API - Provides ranking information that is a somewhat accurate map to Yahoo!'s actual rankings, but is occassionally so far off-base that they're not reliable. Our data points show a lot more congruity with Yahoo!'s than Google's, but not nearly enough when compared with scraped results to be valuable to marketers and businesses.

•    Site Explorer API - Shows excellent information as far as number of pages indexed on a site and the link data that Yahoo! knows about. We've been comparing this information with that from scraped Yahoo! search results (for queries like linkdomain: and site:) and those at the Site Explorer page and find that there's very little quality difference in the results returned, though the best estimate numbers can still be found through a last page search of results.

•    Search Marketing API - I haven't played with this one at all, so I'd love to hear comments from those who have.

MSN:

•    Doesn't mind scraping as long as you use the RSS results. We do, we love them and we commend MSN for giving them out - bravo! They've also got a web search SDK program, but we've yet to give it a whirl. The only problem is the MSN estimates, which are so far off as to be useless. The links themselves, though, are useful.

Ask.com

•    Though it's somewhat hidden, the XML.Teoma.com page allows for scraping of results and Ask doesn't seem to mind, though they haven't explicitly said anything. Again, bravo! - the results look solid, accurate and match up against the Ask.com queries. Now, if Ask would only provide links

I know a lot of you are probably asking:

•    "Rand, if scraping is working, why do you care about the search engines fixing the APIs?"

•    The straight answer is that scraping hurts the search engines, hurts their users and isn't the most practical way to get the data. Let me give you some examples:

•    Scraped queries have to look as much like real users as possible to avoid detection and banning - thus, they affect the query data that search engineers use to improve web search.

•    These queries also hit advertisers - falsifying the number of "real" impressions that advertisers see and lowering their CTRs unnaturally.

•    They take up search engine resources and though even the heaviest scraping barely impacts their server loads, it's still an annoyance.

•    With all these negative elements, and so many positive incentives to have the data, it's clear what's needed - a way for marketers/businesses to get the data they need without hurting the search engines. Here's how they can do it:

•    Provide the search ranking position of a site in the referral string - this works for ranking data, but not for link data and since Yahoo! (and Google) both send referrals through re-directs at times, it wouldn't be a hard piece to add.

•    Make the API's accurate, complete and unlimited

•    If the last option is too ambitious, the search engines could charge for API queries - anyone who needs the data would be more than happy to pay for it. This might help with quality control, too.

•    For link data - serve up accurate, wholistic data in programs like Google Sitemaps and Yahoo! Search Submit (or even, Google Analytics). Obviously, you'd only get information about your own site after verifying.

I've talked to lots of people at the search engine level about making changes this week (including Jeremy, Priyank, Matt, Adam, Aaron, Brett and more). I can only hope for the best...

Source: http://moz.com/blog/the-nasty-problem-with-scraping-results-from-the-engines