SFTW: Scraping data with Google Refine

For the first Something For The Weekend of 2012 I want to tackle a common problem when you’re trying to scrape a collection of webpage: they have some sort of structure in their URL like this, where part of the URL refers to the name or code of an entity:     http://www.ltscotland.org.uk/scottishschoolsonline/schools/freemealentitlement.asp?iSchoolID=5237521

  tp://www.ltscotland.org.uk/scottishschoolsonline/schools/freemealentitlement.asp?iSchoolID=5237629

    ttp://www.ltscotland.org.uk/scottishschoolsonline/schools/freemealentitlement.asp?iSchoolID=5237823

In this instance, you can see that the URL is identical apart from a 7 digit code at the end: the ID of the school the data refers to.

There are a number of ways you could scrape this data. You could use Google Docs and the =importXML formula, but Google Docs will only let you use this 50 times on any one spreadsheet (you could copy the results and select Edit > Paste Special > Values Only and then use the formula a further 50 times if it’s not too many – here’s one I prepared earlier).

And you could use Scraperwiki to write a powerful scraper – but you need to understand enough coding to do so quickly (here’s a demo I prepared earlier).

A middle option is to use Google Refine, and here’s how you do it.

Assembling the ingredients

With the basic URL structure identified, we already have half of our ingredients. What we need  next is a list of the ID codes that we’re going to use to complete each URL.

An advanced search for “list seed number scottish schools filetype:xls” brings up a link to this spreadsheet (XLS) which gives us just that.

The spreadsheet will need editing: remove any rows you don’t need. This will reduce the time that the scraper will take in going through them. For example, if you’re only interested in one local authority, or one type of school, sort your spreadsheet so that you can delete those above or below them.

Now to combine  the ID codes with the base URL.

Bringing your data into Google Refine

Open Google Refine and create a new project with the edited spreadsheet containing the school IDs.

At the top of the school ID column click on the drop-down menu and select Edit column > Add column based on this column…

In the New column name box at the top call this ‘URL’.

In the Expression box type the following piece of GREL (Google Refine Expression Language):

“http://www.ltscotland.org.uk/scottishschoolsonline/schools/freemealentitlement.asp?iSchoolID=”+value

(Type in the quotation marks yourself – if you’re copying them from a webpage you may have problems)

The ‘value’ bit means the value of each cell in the column you just selected. The plus sign adds it to the end of the URL in quotes.

In the Preview window you should see the results – you can even copy one of the resulting URLs and paste it into a browser to check it works. (On one occasion Google Refine added .0 to the end of the ID number, ruining the URL. You can solve this by changing ‘value’ to value.substring(0,7) – this extracts the first 7 characters of the ID number, omitting the ‘.0') UPDATE: in the comment Thad suggests “perhaps, upon import of your spreadsheet of IDs, you forgot to uncheck the importer option to Parse as numbers?”

Click OK if you’re happy, and you should have a new column with a URL for each school ID.

Grabbing the HTML for each page

Now click on the top of this new URL column and select Edit column > Add column by fetching URLs…

In the New column name box at the top call this ‘HTML’.

All you need in the Expression window is ‘value’, so leave that as it is.

Click OK.

Google Refine will now go to each of those URLs and fetch the HTML contents. As we have a couple thousand rows here, this will take a long time – hours, depending on the speed of your computer and internet connection (it may not work at all if either isn’t very fast). So leave it running and come back to it later.

Extracting data from the raw HTML with parseHTML

When it’s finished you’ll have another column where each cell is a bunch of HTML. You’ll need to create a new column to extract what you need from that, and you’ll also need some GREL expressions explained here.

First you need to identify what data you want, and where it is in the HTML. To find it, right-click on one of the webpages containing the data, and search for a key phrase or figure that you want to extract. Around that data you want to find a HTML tag like <table class=”destinations”> or <div id=”statistics”>. Keep that open in another window while you tweak the expression we come onto below…

Back in Google Refine, at the top of the HTML column click on the drop-down menu and select Edit column > Add column based on this column…

In the New column name box at the top give it a name describing the data you’re going to pull out.

In the Expression box type the following piece of GREL (Google Refine Expression Language):

value.parseHtml().select(“table.destinations”)[0].select(“tr”).toString()

(Again, type the quotation marks yourself rather than copying them from here or you may have problems)

I’ll break down what this is doing:

value.parseHtml()

parse the HTML in each cell (value)

.select(“table.destinations”)

find a table with a class (.) of “destinations” (in the source HTML this reads <table class=”destinations”>. If it was <div id=”statistics”> then you would write .select(“div#statistics”) – the hash sign representing an ‘id’ and the full stop representing a ‘class’.

[0]

This zero in square brackets tells Refine to only grab the first table – a number 1 would indicate the second, and so on. This is because numbering (“indexing”) generally begins with zero in programming.

.select(“tr”)

Now, within that table, find anything within the tag <tr>

.toString()

And convert the results into a string of text.

The results of that expression in the Preview window should look something like this:

<tr> <th></th> <th>Abbotswell School</th> <th>Aberdeen City</th> <th>Scotland</th> </tr> <tr> <th>Percentage of pupils</th> <td>25.5%</td> <td>16.3%</td> <td>22.6%</td> </tr>

This is still HTML, but a much smaller and manageable chunk. You could, if you chose, now export it as a spreadsheet file and use various techniques to get rid of the tags (Find and Replace, for example) and split the data into separate columns (the =SPLIT formula, for example).

Or you could further tweak your GREL code in Refine to drill further into your data, like so:

value.parseHtml().select(“table.destinations”)[0].select(“td”)[0].toString()

Which would give you this:

<td>25.5%</td>

Or you can add the .substring function to strip out the HTML like so (assuming that the data you want is always 5 characters long):

value.parseHtml().select(“table.destinations”)[0].select(“td”)[0].toString().substring(5,10)

When you’re happy, click OK and you should have a new column for that data. You can repeat this for every piece of data you want to extract into a new column.

Then click Export in the upper right corner and save as a CSV or Excel file.

Source: http://onlinejournalismblog.com/2012/01/13/sftw-scraping-data-with-google-refine/

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

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

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

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

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

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

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

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

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

Post titleMigrating 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/

Data Scraping Services - Web Scraping Video Tutorial Collection for All Programming Language

Web scraping is a mechanism in which request made to website URL to get  HTML Document text and that text then parsed to extract data from the HTML codes.  Website scraping for data is a generalize approach and can be implemented in any programming language like PHP, Java, C#, Python and many other.

There are many Web scraping software available in market using which you can extract data with no coding knowledge. In many case the scraping doesn’t help due to custom crawling flow for data scraping and in that case you have to make your own web scraping application in one of the programming language you know. In this post I have collected scraping video tutorials for all programming language.

I mostly familiar with web scraping using PHP, C# and some other scraping tools and providing web scraping service.  If you have any scraping requirement send me your requirements and I will get back with sample data scrape and best price.

Web Scraping Using PHP

You can do web scraping in PHP using CURL library and Simple HTML DOM parsing library.  PHP function file_get_content() can also be useful for making web request. One drawback of scraping using PHP is it can’t parse JavaScript so ajax based scraping can’t be possible using PHP.

Web Scraping Using C#

There are many library available in .Net for HTML parsing and data scraping. I have used Web Browser control and HTML Agility Pack for data extraction in .Net using C#

I have didn’t done web scraping in Java, PERL and Python. I had learned web scraping in node.js using Casper.JS and Phantom.JS library. But I thought below tutorial will be helpful for some one who are Java and Python based.

Web Scraping Using Jsoup in Java

Scraping Stock Data Using Python

Develop Web Crawler Using PERL

Web Scraping Using Node.Js

If you find any other good web scraping video tutorial then you can share the link in comment so other readesr get benefit form that.

Source: http://webdata-scraping.com/web-scraping-video-tutorial-collection-programming-language/

Web Scraping Services - Extracting Business Data You Need

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We scrape data from various sources such as websites, blogs, podcasts, and online directories; and convert them into structured formats such as excel, csv, access, text, My SQL using automated and manual scraping technologies. Through our web data scraping services, we crawl through websites and gather sales leads, competitor’s product details, new offers, pricing methodologies, and various other types of information from the web.

Our web scraping services scrape data such as name, email, phone number, address, country, state, city, product, and pricing details among others.

Areas of Expertise in Web Scraping:

•    Contact Details
•    Statistics data from websites
•    Classifieds
•    Real estate portals
•    Social networking sites
•    Government portals
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•    Business directories
•    Job portals
•    Email ids and Profiles
•    URLs in an excel spreadsheet
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•    Round the clock customer support and technical assistance

Get Quick Cost and Time Estimate

Source: http://www.hitechbposervices.com/web-scraping.php

Which language is the most flexible for scraping websites?

3 down vote favorite

I'm new to programming. I know a little python and a little objective c, and I've been going through tutorials for each. Then it occurred to me, I need to know which language is more flexible (python, obj c, something else) for screen scraping a website for content.

What do I mean by "flexible"?

Well, ideally, I need something that will be easy to refactor and tweak for similar projects. I'm trying to avoid doing a lot of re-writing (well, re-coding) if I wanted to switch some of the variables in the program (i.e., the website to be scraped, the content to fetch, etc).

Anyways, if you could please give me your opinion, that would be great. Oh, and if you know any existing frameworks for the language you recommend, please share. (I know a little about Selenium and BeautifulSoup for python already).

4 Answers

I recently wrote a relatively complex web scraper to harvest a TON of data. It had to do some relatively complex parsing, I needed it to stuff it into a database, etc. I'm C# programmer now and formerly a Perl guy.

I wrote my original scraper using Python. I started on a Thursday and by Sunday morning I was harvesting over about a million scores from a show horse site. I used Python and SQLlite because they were fast.

HOWEVER, as I started putting together programs to regularly keep the data updated and to populate the SQL Server that would backend my MVC3 application, I kept hitting snags and gaps in my Python knowledge.

In the end, I completely rewrote the scraper/parser in C# using the HtmlAgilityPack and it works better than before (and just about as fast).

Because I KNEW THE LANGUAGE and the environment so much better I was able to add better database support, better logging, better error handling, etc. etc.

So... short answer.. Python was the fastest to market with a "good enough for now" solution, but the language I know best (C#) was the best long-term solution.

EDIT: I used BeautifulSoup for my original crawler written in Python.

5 down vote

The most flexible is the one that you're most familiar with.

Personally, I use Python for almost all of my utilities. For scraping, I find that its functionality specific to parsing and string manipulation requires little code, is fast and there are a ton of examples out there (strong community). Chances are that someone's already written whatever you're trying to do already, or there's at least something along the same lines that needs very little refactoring.

1 down vote

I think its safe to say that Python is a better place to start than Objective C. Honestly, just about any language meets the "flexible" requirement. All you need is well thought out configuration parameters. Also, a dynamic language like Python can go a long way in increasing flexibility, provided that you account for runtime type errors.

1 down vote

I recently wrote a very simple web-scraper; I chose Common Lisp as I'm learning the language.

On the basis of my experience - both of the language and the availability of help from experienced Lispers - I recommend investigating Common Lisp for your purpose.

There are excellent XML-parsing libraries available for CL, as well as libraries for parsing invalid HTML, which you'll need unless the sites you're parsing consist solely of valid XHTML.

Also, Common Lisp is a good language in which to implement DSLs; a DSL for web-scraping may be a solution to your requirement for flexibility & re-use.

Source: http://programmers.stackexchange.com/questions/74998/which-language-is-the-most-flexible-for-scraping-websites/75006#75006

Scraping Data: Site-specific Extractors vs. Generic Extractors

Scraping is becoming a rather mundane job with every other organization getting its feet wet with it for their own data gathering needs. There have been enough number of crawlers built – some open-sourced and others internal to organizations for in-house utilities. Although crawling might seem like a simple technique at the onset, doing this at a large-scale is the real deal. You need to have a distributed stack set up to take care of handling huge volumes of data, to provide data in a low-latency model and also to deal with fail-overs. This still is achievable after crossing the initial tech barrier and via continuous optimizations. (P.S. Not under-estimating this part because it still needs a team of Engineers monitoring the stats and scratching their heads at times).

Social Media Scraping

Focused crawls on a predefined list of sites

However, you bump into a completely new land if your goal is to generate clean and usable data sets from these crawls i.e. “extract” data in a format that your DB can process and aid in generating insights. There are 2 ways of tackling this:

a. site-specific extractors which give desired results

b. generic extractors that result in few surprises

Assuming you still do focused crawls on a predefined list of sites, let’s go over specific scenarios when you have to pick between the two-

1. Mass-scale crawls; high-level meta data – Use generic extractors when you have a large-scale crawling requirement on a continuous basis. Large-scale would mean having to crawl sites in the range of hundreds of thousands. Since the web is a jungle and no two sites share the same template, it would be impossible to write an extractor for each. However, you have to settle in with just the document-level information from such crawls like the URL, meta keywords, blog or news titles, author, date and article content which is still enough information to be happy with if your requirement is analyzing sentiment of the data.

cb1c0_one-size

A generic extractor case

Generic extractors don’t yield accurate results and often mess up the datasets deeming it unusable. Reason being

programatically distinguishing relevant data from irrelevant datasets is a challenge. For example, how would the extractor know to skip pages that have a list of blogs and only extract the ones with the complete article. Or delineating article content from the title on a blog page is not easy either.

To summarize, below is what to expect of a generic extractor.

Pros-

•    minimal manual intervention
•    low on effort and time
•    can work on any scale

Cons-

•    Data quality compromised
•    inaccurate and incomplete datasets
•    lesser details suited only for high-level analyses
•    Suited for gathering- blogs, forums, news
•    Uses- Sentiment Analysis, Brand Monitoring, Competitor Analysis, Social Media Monitoring.

2. Low/Mid scale crawls; detailed datasets – If precise extraction is the mandate, there’s no going away from site-specific extractors. But realistically this is do-able only if your scope of work is limited i.e. few hundred sites or less. Using site-specific extractors, you could extract as many number of fields from any nook or corner of the web pages. Most of the times, most pages on a website share similar templates. If not, they can still be accommodated for using site-specific extractors.

cutlery

Designing extractor for each website

Pros-

•    High data quality
•    Better data coverage on the site

Cons-

High on effort and time

Site structures keep changing from time to time and maintaining these requires a lot of monitoring and manual intervention

Only for limited scale

Suited for gathering – any data from any domain on any site be it product specifications and price details, reviews, blogs, forums, directories, ticket inventories, etc.

Uses- Data Analytics for E-commerce, Business Intelligence, Market Research, Sentiment Analysis

Conclusion

Quite obviously you need both such extractors handy to take care of various use cases. The only way generic extractors can work for detailed datasets is if everyone employs standard data formats on the web (Read our post on standard data formats here). However, given the internet penetration to the masses and the variety of things folks like to do on the web, this is being overly futuristic.

So while site-specific extractors are going to be around for quite some time, the challenge now is to tweak the generic ones to work better. At PromptCloud, we have added ML components to make them smarter and they have been working well for us so far.

What have your challenges been? Do drop in your comments.

Source: https://www.promptcloud.com/blog/scraping-data-site-specific-extractors-vs-generic-extractors/

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/

Thoughts on scraping SERPs and APIs

Google says that scraping keyword rankings is against their policy from what I've read. Bummer. We comprise a lot of reports and manual finding and entry was a pain. Enter Moz! We still manually check and compare, but it's nice having that tool. I'm confused now though about practices and getting SERPs in an automated way. Here are my questions

    Is it against policy to get SERPs from an automated method? If that is the case, isn't Moz breaking this policy with it's awesome keyword tracker?

    If it's not, and we wanted to grab that kind of data, how would we do it? Right now, Moz's API doesn't offer this data. I thought Raven Tools at one point offered this, but they don't now from what I've read. Are there any APIs out there that we can grab this data and do what we want with it? (let's day build our own dashboard)?

Thanks for any clarification and input!

Source: http://moz.com/community/q/thoughts-on-scraping-serps-and-apis

Why Data mining is still a powerful tool to help companies

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

Data Mining Ensures Better Business Decisions

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

Data Mining provides Fraud Detection Capabilities

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

Data Mining Evolves to be Business Focused

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

Data Mining has become more Granular

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

Conclusion

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

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

The Great Advantages of Data Extraction Software – Why a Company Needs it?

Data extraction is being a huge problem for large corporate companies and businesses, which needs to be handled technically and safely. There are many different approaches used for data extraction from web and various tools have designed to solve certain problems.

Moreover, algorithms and advanced techniques were also developed for data extraction. In this array, the Data Extraction Software is widely used to extract information from web as designed.

Data Extraction Software:

 This is a program specifically designed to collect and organize the information or data from the website or webpage and reformat them.

Uses of Data Extraction Software:

Data extraction software can be used at various levels including social web and enterprise levels.

Enterprise Level: Data extraction techniques at the enterprise level are used as the prime tool to perform analysis of the data in business process re-engineering, business system and in competitive intelligence system.

Social Web Level: This type of web data extraction techniques is widely used for gathering structured data in large amount that are continuously generated by Web.2.0, online social network users and social media. 

To specify other uses of Data Extraction software:

•     It helps in assembling stats for the business plans
•     It helps to gather data from public or government agencies
•     It helps to collect data for legal needs

Does the Data Extraction Software make Your Job Simple?

The usage of data extraction software has been widely appreciated by many large corporate companies. In this array, here are a few points to favor the usage of the software;

•     Data toolbar consists of web scraping tool to automate the process of web data extraction
•     Point data fields from which the data need to be collected and the tool will do the rest
•     There are no technical skills required to use data tool
•     It is possible to extract a huge number of data records in just a few seconds

Benefits of Data Extraction Software:

This data extraction software benefits many computer users. Here follows a few remarkable benefits of the software;

•     It can extract detailed data like description, name, price, image and more as defined from a website
•     It is possible to create projects in the extractor and extract required information automatically from the site without the user’s interference
•     The process saves huge effort and time
•     It makes extracting data from several websites easy like online auctions, online stores, real estate portal, business directories, shopping portals and more
•     It makes it possible to export extracted data to various formats like Microsoft Excel, HTML, SQL, XML, Microsoft Access, MySQL and more
•     This will allow processing and analyzing data in any custom format

Who majorly Benefits from Data Extraction Software?

Any computer user benefit from this data extraction software, however, it is majorly benefiting users like;

•     Business men to collect market figures, real estate data and product pricing data
•     Book lovers to extract information about titles, authors, images, descriptions prices and more
•     Collectors and hobbyists to extract auction and betting information
•     Journalists to extract article and news from new websites
•     Travelers to extract information about holiday places, vacations, prices, images and more
•     Job seekers to extract information about jobs available, employers and more

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

Internet Data Mining - How Does it Help Businesses?

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

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

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

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

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

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