Exploring Web Data Extraction And Its Different Techniques

Web scraping or web data extraction is a distinctive process based on computer software to extract information from different websites. Mostly business organizations are dependent on the web resources for collecting crucial information relating to decision making. With the analysis of such data, they can identify the existing trends of market, details, prices, and product specification. Looking at the time consuming process of manual data extraction, the prominence of data extraction techniques increases.

Different data scraping techniques

Several data extraction techniques are available for the businesses to extract useful information for successful operations. Some of them may include:

    Logical extraction: It comprises logical data extraction of complete source system as well as incremental.
    Physical extraction: This technique involves two different mechanisms for web scrapping that include both online as well as offline.
    HTTP programming: You can also extract data from both dynamic and static websites by implying the technique of socket programming. It allows you to post HTTP requests on the remote web servers.
    Web scraping software: Several software tools are available in the market that serves your individual needs of extracting data with ease. It automatically attempts to recognize the structure of data for a page and extracts the content for further analysis.
    Web scrapping tools: Besides the availability of reliable software, numerous user-friendly web scrapping tools are also helpful in simplifying the entire web scraping process.

Hire a website scrapper

Hiring a suitable website scraper that offers website data extraction services for all your business requirements is an ideal way amongst all other techniques. It provides you filtered and reliable data according to your need for analysis. Some of the major advantages of using website scrapping services may include:

    Automation of data.
    It can retrieve web pages of both static as well as dynamic websites.
    It is also capable of transforming the content into useful information.
    Provides reliable and accurate data.
    It also recognizes several semantic annotations.

Scraping service versus tools

Web scraping services gain more privilege than other tools and software. The basic reason behind this preference is that the service providers are comparatively cheaper than the tools. In fact, they maintain better accuracy and reliability of data.

Summary: It is advisable to look out for suitable web data extraction services instead of any tools or software. This helps in acquiring customized and structured data for your business in legal manner.


 Source : http://www.web-parsing.com/blog/exploring-web-data-extraction-and-its-different-techniques/

Extensive Benefits of Data Mining Services to Marketing – Retail and Outreach Sectors…!!!

There is a vast ocean out there – An ocean of information on internet which is massive, brimming with a lot of data; in fact, it is constantly getting updated, increase the volume with each passing day. In fact, it is believed that around 90% of total information generated in the last two years, is now available on the internet.

Picking right set of information from this heap of data is like searching a needle in the haystack. It is almost next to impossible to search it manually – You need a powerful magnet in form of data mining service provider…!!!

Data mining services work like a magnet – It helps you in finding the right kind of information from huge databases available in the digital world. And with databases getting mammoth every minute, the importance of partnering with a professional and reliable data mining company cannot be overlooked.Though, loaded with a lot of negative connotations; data mining still reigns like a king! In fact, in order to truly appreciate the concept behind data mining, one needs to know it in its entirety.

Every coin has two sides – If there is a brighter side; there tends to be a dark side as well. Though, advantages of web extraction, outweighs disadvantages the fact is it is always the dark underbelly that is highlighted and shown to the world. However, as wise men say, focus on positive sides – Lets see what amazing advantages it can offer to your business and how well you can gain from hiring a professional data mining services.

Upside or Advantage of Data Extraction Services:

While data mining is used primarily in business, it is interesting to know that benefits of data mining goes beyond and across boundaries; it helps various industries as well.

Marketing/Retailing

Data mining can prove to be extremely helpful to the marketers and retailers who are looking out for potential clients as well as aspires to maintain consumer satisfaction. This is one of the methods that allows the businesses to know their potential clients better by acquiring their personal information and preferences.
Not just data extraction helps in determining the trends in goods and services by presenting an overview of online data. With adequate information, you can improve your goods and services, along with changing or choosing the ones which are more in demand. Consequently, success in business has been made quicker and easier these days because of data mining.
Streamline Outreach

Outreach forms an integral part of any business – And to effectively carry out outreach activities; one needs to have a huge cache of database, that can help the marketers to learn how to approach a particular set of customers. Information like that includes relevant e-mail addresses, mailing addresses or social media pages needs to be streamlined any mailers to get the best results.

Data extraction makes this easier; since it gets all the updated information; and in process saves your time and money.

And as it is “the lotus flower grows in mud, but makes our world fragrant” – data mining services is marred by criticism and controversy; however, its extensive advantages outweighs these negativity to a great extent.

Source : http://www.habiledata.com/blog/extensive-benefits-of-data-mining-services-to-marketing-retail-and-outreach-sectors/

ECJ clarifies Database Directive scope in screen scraping case

EC on the legal protection of databases (Database Directive) in a case concerning the extraction of data from a third party’s website by means of automated systems or software for commercial purposes (so called 'screen scraping').

Flight data extracted

The case, Ryanair Ltd vs. PR Aviation BV, C-30/14, is of interest to a range of companies such as price comparison websites. It stemmed from  Dutch company PR Aviation operation of a website where consumers can search through flight data of low-cost airlines  (including Ryanair), compare prices and, on payment of a commission, book a flight. The relevant flight data is extracted from third-parties’ websites by means of ‘screen scraping’ practices.

Ryanair claimed that PR Aviation’s activity:

• amounted to infringement of copyright (relating to the structure and architecture of the database) and of the so-called sui generis database right (i.e. the right granted to the ‘maker’ of the database where certain investments have been made to obtain, verify, or present the contents of a database) under the Netherlands law implementing the Database Directive;

• constituted breach of contract. In this respect, Ryanair claimed that a contract existed with PR Aviation for the use of its website. Access to the latter requires acceptance, by clicking a box, of the airline’s general terms and conditions which, amongst others, prohibit unauthorized ‘screen scraping’ practices for commercial purposes.

Ryanair asked Dutch courts to prohibit the infringement and order damages. In recent years the company has been engaged in several legal cases against web scrapers across Europe.

The Local Court, Utrecht, and the Court of Appeals of Amsterdam dismissed Ryanair’s claims on different grounds. The Court of Appeals, in particular, cited PR Aviation’s screen scraping of Ryanair’s website as amounting to a “normal use” of said website within the meaning of the lawful user exceptions under Sections 6 and 8 of the Database Directive, which cannot be derogated by contract (Section 15).

Ryanair appealed

Ryanair appealed the decision before the Netherlands Supreme Court (Hoge Raad der Nederlanden), which decided to refer the following question to the ECJ for a preliminary ruling: “Does the application of [Directive 96/9] also extend to online databases which are not protected by copyright on the basis of Chapter II of said directive or by a sui generis right on the basis of Chapter III, in the sense that the freedom to use such databases through the (whether or not analogous) application of Article[s] 6(1) and 8, in conjunction with Article 15 [of Directive 96/9] may not be limited contractually?.”

The ECJ’s ruling

The ECJ (without the need of the opinion of the advocate general) ruled that the Database Directive is not applicable to databases which are not protected either by copyright or by the sui generis database right. Therefore, exceptions to restricted acts set forth by Sections 6 and 8 of the Directive do not prevent the database owner from establishing contractual limitations on its use by third parties. In other words, restrictions to the freedom to contract set forth by the Database Directive do not apply in cases of unprotected databases. Whether Ryanair’s website may be entitled to copyright or sui generis database right protection needs to be determined by the competent national court.

The ECJ’s decision is not particularly striking from a legal standpoint. Yet, it could have a significant impact on the business model of price comparison websites, aggregators, and similar businesses. Owners of databases that could not rely on intellectual property protection may contractually prevent extraction and use (“scraping”) of content from their online databases. Thus, unprotected databases could receive greater protection than the one granted by IP law.

Antitrust implications

However, the lawfulness of contractual restrictions prohibiting access and reuse of data through screen scraping practices should be assessed under an antitrust perspective. In this respect, in 2013 the Court of Milan ruled that Ryanair’s refusal to grant access to its database to the online travel agency Viaggiare S.r.l. amounted to an abuse of dominant position in the downstream market of information and intermediation on flights (decision of June 4, 2013 Viaggiare S.r.l. vs Ryanair Ltd). Indeed, a balance should be struck between the need to compensate the efforts and investments made by the creator of the database with the interest of third parties to be granted with access to information (especially in those cases where the latter are not entitled to copyright protection).

Additionally, web scraping triggers other issues which have not been considered by the ECJ’s ruling. These include, but are not limited to trademark law (i.e., whether the use of a company’s names/logos by the web scraper without consent may amount to trademark infringement), data protection (e.g., in case the scraping involves personal data), or unfair competition.

Source: http://www.globallegalpost.com/blogs/global-view/ecj-clarifies-database-directive-scope-in-screen-scraping-case-128701/

Data Scraping - Increasing Accessibility by Scraping Information From PDF

You may have heard about data scraping which is a method that is being used by computer programs in extracting data from an output that comes from another program. To put it simply, this is a process which involves the automatic sorting of information that can be found on different resources including the internet which is inside an html file, PDF or any other documents. In addition to that, there is the collection of pertinent information. These pieces of information will be contained into the databases or spreadsheets so that the users can retrieve them later.

Most of the websites today have text that can be accessed and written easily in the source code. However, there are now other businesses nowadays that choose to make use of Adobe PDF files or Portable Document Format. This is a type of file that can be viewed by simply using the free software known as the Adobe Acrobat. Almost any operating system supports the said software. There are many advantages when you choose to utilize PDF files. Among them is that the document that you have looks exactly the same even if you put it in another computer so that you can view it. Therefore, this makes it ideal for business documents or even specification sheets. Of course there are disadvantages as well. One of which is that the text that is contained in the file is converted into an image. In this case, it is often that you may have problems with this when it comes to the copying and pasting.

This is why there are some that start scraping information from PDF. This is often called PDF scraping in which this is the process that is just like data scraping only that you will be getting information that is contained in your PDF files. In order for you to begin scraping information from PDF, you must choose and exploit a tool that is specifically designed for this process. However, you will find that it is not easy to locate the right tool that will enable you to perform PDF scraping effectively. This is because most of the tools today have problems in obtaining exactly the same data that you want without personalizing them.

Nevertheless, if you search well enough, you will be able to encounter the program that you are looking for. There is no need for you to have programming language knowledge in order for you to use them. You can easily specify your own preferences and the software will do the rest of the work for you. There are also companies out there that you can contact and they will perform the task since they have the right tools that they can use. If you choose to do things manually, you will find that this is indeed tedious and complicated whereas if you compare this to having professionals do the job for you, they will be able to finish it in no time at all. Scraping information from PDF is a process where you collect the information that can be found on the internet and this does not infringe copyright laws.

Source: http://ezinearticles.com/?Increasing-Accessibility-by-Scraping-Information-From-PDF&id=4593863

Migrating Table-oriented Web Scraping Code to rvest w/XPath & CSS Selector Examples

My intrepid colleague (@jayjacobs) informed me of this (and didn’t gloat too much). I’ve got a “pirate day” post coming up this week that involves scraping content from the web and thought folks might benefit from another example that compares the “old way” and the “new way” (Hadley excels at making lots of “new ways” in R :-) I’ve left the output in with the code to show that you get the same results.

The following shows old/new methods for extracting a table from a web site, including how to use either XPath selectors or CSS selectors in rvest calls. To stave of some potential comments: due to the way this table is setup and the need to extract only certain components from the td blocks and elements from tags within the td blocks, a simple readHTMLTable would not suffice.

The old/new approaches are very similar, but I especially like the ability to chain output ala magrittr/dplyr and not having to mentally switch gears to XPath if I’m doing other work targeting the browser (i.e. prepping data for D3).

The code (sans output) is in this gist, and IMO the rvest package is going to make working with web site data so much easier.

library(XML)
library(httr)
library(rvest)
library(magrittr)

# setup connection & grab HTML the "old" way w/httr

freak_get <- GET("http://torrentfreak.com/top-10-most-pirated-movies-of-the-week-130304/")

freak_html <- htmlParse(content(freak_get, as="text"))

# do the same the rvest way, using "html_session" since we may need connection info in some scripts

freak <- html_session("http://torrentfreak.com/top-10-most-pirated-movies-of-the-week-130304/")

# extracting the "old" way with xpathSApply

xpathSApply(freak_html, "//*/td[3]", xmlValue)[1:10]

##  [1] "Silver Linings Playbook "           "The Hobbit: An Unexpected Journey " "Life of Pi (DVDscr/DVDrip)"       

##  [4] "Argo (DVDscr)"                      "Identity Thief "                    "Red Dawn "                        

##  [7] "Rise Of The Guardians (DVDscr)"     "Django Unchained (DVDscr)"          "Lincoln (DVDscr)"                 

## [10] "Zero Dark Thirty "

xpathSApply(freak_html, "//*/td[1]", xmlValue)[2:11]

##  [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10"

xpathSApply(freak_html, "//*/td[4]", xmlValue)

##  [1] "7.4 / trailer" "8.2 / trailer" "8.3 / trailer" "8.2 / trailer" "8.2 / trailer" "5.3 / trailer" "7.5 / trailer"

##  [8] "8.8 / trailer" "8.2 / trailer" "7.6 / trailer"

xpathSApply(freak_html, "//*/td[4]/a[contains(@href,'imdb')]", xmlAttrs, "href")

##                                    href                                    href                                    href

##  "http://www.imdb.com/title/tt1045658/"  "http://www.imdb.com/title/tt0903624/"  "http://www.imdb.com/title/tt0454876/"

##                                    href                                    href                                    href

##  "http://www.imdb.com/title/tt1024648/"  "http://www.imdb.com/title/tt2024432/"  "http://www.imdb.com/title/tt1234719/"

##                                    href                                    href                                    href

##  "http://www.imdb.com/title/tt1446192/"  "http://www.imdb.com/title/tt1853728/"  "http://www.imdb.com/title/tt0443272/"

##                                    href

## "http://www.imdb.com/title/tt1790885/?"


# extracting with rvest + XPath

freak %>% html_nodes(xpath="//*/td[3]") %>% html_text() %>% .[1:10]

##  [1] "Silver Linings Playbook "           "The Hobbit: An Unexpected Journey " "Life of Pi (DVDscr/DVDrip)"       

##  [4] "Argo (DVDscr)"                      "Identity Thief "                    "Red Dawn "                        

##  [7] "Rise Of The Guardians (DVDscr)"     "Django Unchained (DVDscr)"          "Lincoln (DVDscr)"                 

## [10] "Zero Dark Thirty "

freak %>% html_nodes(xpath="//*/td[1]") %>% html_text() %>% .[2:11]

##  [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10"

freak %>% html_nodes(xpath="//*/td[4]") %>% html_text() %>% .[1:10]

##  [1] "7.4 / trailer" "8.2 / trailer" "8.3 / trailer" "8.2 / trailer" "8.2 / trailer" "5.3 / trailer" "7.5 / trailer"

##  [8] "8.8 / trailer" "8.2 / trailer" "7.6 / trailer"

freak %>% html_nodes(xpath="//*/td[4]/a[contains(@href,'imdb')]") %>% html_attr("href") %>% .[1:10]

##  [1] "http://www.imdb.com/title/tt1045658/"  "http://www.imdb.com/title/tt0903624/"

##  [3] "http://www.imdb.com/title/tt0454876/"  "http://www.imdb.com/title/tt1024648/"

##  [5] "http://www.imdb.com/title/tt2024432/"  "http://www.imdb.com/title/tt1234719/"

##  [7] "http://www.imdb.com/title/tt1446192/"  "http://www.imdb.com/title/tt1853728/"

##  [9] "http://www.imdb.com/title/tt0443272/"  "http://www.imdb.com/title/tt1790885/?"

# extracting with rvest + CSS selectors

freak %>% html_nodes("td:nth-child(3)") %>% html_text() %>% .[1:10]

##  [1] "Silver Linings Playbook "           "The Hobbit: An Unexpected Journey " "Life of Pi (DVDscr/DVDrip)"       

##  [4] "Argo (DVDscr)"                      "Identity Thief "                    "Red Dawn "                        

##  [7] "Rise Of The Guardians (DVDscr)"     "Django Unchained (DVDscr)"          "Lincoln (DVDscr)"                 

## [10] "Zero Dark Thirty "

freak %>% html_nodes("td:nth-child(1)") %>% html_text() %>% .[2:11]

##  [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10"

freak %>% html_nodes("td:nth-child(4)") %>% html_text() %>% .[1:10]

##  [1] "7.4 / trailer" "8.2 / trailer" "8.3 / trailer" "8.2 / trailer" "8.2 / trailer" "5.3 / trailer" "7.5 / trailer"

##  [8] "8.8 / trailer" "8.2 / trailer" "7.6 / trailer"

freak %>% html_nodes("td:nth-child(4) a[href*='imdb']") %>% html_attr("href") %>% .[1:10]

##  [1] "http://www.imdb.com/title/tt1045658/"  "http://www.imdb.com/title/tt0903624/"

##  [3] "http://www.imdb.com/title/tt0454876/"  "http://www.imdb.com/title/tt1024648/"

##  [5] "http://www.imdb.com/title/tt2024432/"  "http://www.imdb.com/title/tt1234719/"

##  [7] "http://www.imdb.com/title/tt1446192/"  "http://www.imdb.com/title/tt1853728/"

##  [9] "http://www.imdb.com/title/tt0443272/"  "http://www.imdb.com/title/tt1790885/?"

# building a data frame (which is kinda obvious, but hey)

data.frame(movie=freak %>% html_nodes("td:nth-child(3)") %>% html_text() %>% .[1:10],

           rank=freak %>% html_nodes("td:nth-child(1)") %>% html_text() %>% .[2:11],

           rating=freak %>% html_nodes("td:nth-child(4)") %>% html_text() %>% .[1:10],

           imdb.url=freak %>% html_nodes("td:nth-child(4) a[href*='imdb']") %>% html_attr("href") %>% .[1:10],

           stringsAsFactors=FALSE)

##                                 movie rank        rating                              imdb.url

## 1            Silver Linings Playbook     1 7.4 / trailer  http://www.imdb.com/title/tt1045658/

## 2  The Hobbit: An Unexpected Journey     2 8.2 / trailer  http://www.imdb.com/title/tt0903624/

## 3          Life of Pi (DVDscr/DVDrip)    3 8.3 / trailer  http://www.imdb.com/title/tt0454876/

## 4                       Argo (DVDscr)    4 8.2 / trailer  http://www.imdb.com/title/tt1024648/

## 5                     Identity Thief     5 8.2 / trailer  http://www.imdb.com/title/tt2024432/

## 6                           Red Dawn     6 5.3 / trailer  http://www.imdb.com/title/tt1234719/

## 7      Rise Of The Guardians (DVDscr)    7 7.5 / trailer  http://www.imdb.com/title/tt1446192/

## 8           Django Unchained (DVDscr)    8 8.8 / trailer  http://www.imdb.com/title/tt1853728/

## 9                    Lincoln (DVDscr)    9 8.2 / trailer  http://www.imdb.com/title/tt0443272/

## 10                  Zero Dark Thirty    10 7.6 / trailer http://www.imdb.com/title/tt1790885/?

Source: http://www.r-bloggers.com/migrating-table-oriented-web-scraping-code-to-rvest-wxpath-css-selector-examples/

Web Scraping Services : Data Discovery vs. Data Extraction

Looking at screen-scraping at a simplified level, there are two primary stages involved: data discovery and data extraction. Data discovery deals with navigating a web site to arrive at the pages containing the data you want, and data extraction deals with actually pulling that data off of those pages. Generally when people think of screen-scraping they focus on the data extraction portion of the process, but my experience has been that data discovery is often the more difficult of the two.

The data discovery step in screen-scraping might be as simple as requesting a single URL. For example, you might just need to go to the home page of a site and extract out the latest news headlines. On the other side of the spectrum, data discovery may involve logging in to a web site, traversing a series of pages in order to get needed cookies, submitting a POST request on a search form, traversing through search results pages, and finally following all of the "details" links within the search results pages to get to the data you're actually after. In cases of the former a simple Perl script would often work just fine. For anything much more complex than that, though, a commercial screen-scraping tool can be an incredible time-saver. Especially for sites that require logging in, writing code to handle screen-scraping can be a nightmare when it comes to dealing with cookies and such.

In the data extraction phase you've already arrived at the page containing the data you're interested in, and you now need to pull it out of the HTML. Traditionally this has typically involved creating a series of regular expressions that match the pieces of the page you want (e.g., URL's and link titles). Regular expressions can be a bit complex to deal with, so most screen-scraping applications will hide these details from you, even though they may use regular expressions behind the scenes.

As an addendum, I should probably mention a third phase that is often ignored, and that is, what do you do with the data once you've extracted it? Common examples include writing the data to a CSV or XML file, or saving it to a database. In the case of a live web site you might even scrape the information and display it in the user's web browser in real-time. When shopping around for a screen-scraping tool you should make sure that it gives you the flexibility you need to work with the data once it's been extracted.

Source: http://ezinearticles.com/?Data-Discovery-vs.-Data-Extraction&id=165396

WordPress Titles: scraping with search url

I’ve blogged for a few years now, and I’ve used several tools along the way. zachbeauvais.com began as a Drupal site, until I worked out that it’s a bit overkill, and switched to WordPress. Recently, I’ve been toying with the idea of using a static site generator (a lá Jekyll or Hyde), or even pulling together a kind of ebook of ramblings. I also want to be able to arrange the posts based on the keywords they contain, regardless of how they’re categorised or tagged.

Whatever I wanted to do, I ended up with a single point of messiness: individual blog posts, and how they’re formatted. When I started, I seem to remember using Drupal’s truly awful WYSIWYG editor, and tweaking the HTML soup it produced. Then, when I moved over to WordPress, it pulled all the posts and metadata through via RSS, and I tweaked with the visual and text tools which are baked into the engine.

A couple years ago, I started to write in Markdown, and completely apart from the blog (thanks to full-screen writing and loud music). This gives me a local .md file, and I copy/paste into WordPress using a plugin to get rid of the visual editor entirely.

So, I wrote a scraper to return a list of blog posts containing a specific term. What I hope is that this very simple scraper is useful to others—WordPress is pretty common, after all—and to get some ideas for improving it, and handle post content. If you haven’t used ScraperWiki before, you might not know that you can see the raw scraper by clicking “view source” from the scraper’s overview page (or going here if you’re lazy).

This scraper is based on WordPress’ built-in search, which can be used by passing the search terms to a url, then scraping the resulting page:

http://zachbeauvais.com/?s=search_term&submit=Search

The scraper uses three Python libraries:

    Requests
    ScraperWiki
    lxml.html

There are two variables which can be changed to search for other terms, or using a different WordPress site:

term = "coffee"

site = "http://www.zachbeauvais.com"

The rest of the script is really simple: it creates a dictionary called “payload” containing the letter “s”, the keyword, and the instruction to search. The “s” is in there to make up the search url: /?s=coffee …

Requests then GETs the site, passing payload as url parameters, and I use Request’s .text function to render the page in html, which I then pass through lxml to the new variable “root”.

payload = {'s': str(term), 'submit': 'Search'}

r = requests.get(site, params=payload)  # This'll be the results page

html = r.text

root = lxml.html.fromstring(html)  # parsing the HTML into the var root

Now, my WordPress theme renders the titles of the retrieved posts in <h1> tags with the CSS class “entry-title”, so I loop through the html text, pulling out the links and text from all the resulting h1.entry-title items. This part of the script would need tweaking, depending on the CSS class and h-tag your theme uses.

for i in root.cssselect("h1.entry-title a"):

    link = i.cssselect("a")

    text = i.text_content()

    data = {

        'uri': link[0].attrib['href'],

        'post-title': str(text),

        'search-term': str(term)

    }

    if i is not None:

        print link

        print text

        print data

        scraperwiki.sqlite.save(unique_keys=['uri'], data=data)

    else:

        print "No results."

These return into an sqlite database via the ScraperWiki library, and I have a resulting database with the title and link to every blog post containing the keyword.

So, this could, in theory, run on any WordPress instance which uses the same search pattern URL—just change the site variable to match.

Also, you can run this again and again, changing the term to any new keyword. These will be stored in the DB with the keyword in its own column to identify what you were looking for.

See? Pretty simple scraping.

So, what I’d like next is to have a local copy of every post in a single format.

Has anyone got any ideas how I could improve this? And, has anyone used WordPress’ JSON API? It might be a logical next step to call the API to get the posts directly from the MySQL DB… but that would be a new blog post!

Source: https://scraperwiki.wordpress.com/2013/03/11/wordpress-titles-scraping-with-search-url/