Data Scraping - Enjoy the Appeal of the Hand Scraped Flooring

Hand scraped flooring is appreciated for the character it brings into the home. This style of flooring relies on hand scraped planks of wood and not the precise milled boards. The irregularities in the planks provide a certain degree of charm and help to create a more unique feature in the home.

Distressed vs. Hand scraped

There are two types of flooring in the market that have an aged and unique charm with a non perfect finish. However, there is a significant difference in the process used to manufacture the planks. The more standard distresses flooring is cut on a factory production line. The grooves, scratches, dents, or other irregularities in these planks are part of the manufacturing process and achieved by rolling or pressed the wood onto a patterned surface.

The real hand scraped planks are made by craftsmen and they work on each plant individually. By using this working technique, there is complete certainty that each plank will be unique in appearance.

Scraping the planks

The hand scraping process on the highest-quality planks is completed by the trained carpenter or craftsmen who will produce a high-quality end product and take great care in their workmanship. It can benefit to ask the supplier of the flooring to see who completes the work.

Beside the well scraped lumber, there are also those planks that have been bought from the less than desirable sources. This is caused by the increased demand for this type of flooring. At the lower end of the market the unskilled workers are used and the end results aren't so impressive.

The high-quality plank has the distinctive look that feels and functions perfectly well as solid flooring, while the low-quality work can appear quite ugly and cheap.

Even though it might cost a little bit more, it benefits to source the hardwood floor dealers that rely on the skilled workers to complete the scraping process.

Buying the right lumber

Once a genuine supplier is found, it is necessary to determine the finer aspects of the wooden flooring. This hand scraped flooring is available in several hardwoods, such as oak, cherry, hickory, and walnut. Plus, it comes in many different sizes and widths. A further aspect relates to the finish with darker colored woods more effective at highlighting the character of the scraped boards. This makes the shadows and lines appear more prominent once the planks have been installed at home.

Why not visit Bellacerafloors.com for the latest collection of luxury floor materials, including the Handscraped Hardwood Flooring.

Source: http://ezinearticles.com/?Enjoy-the-Appeal-of-the-Hand-Scraped-Flooring&id=8995784

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/

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/

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This type of web scraping is bad because:

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

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

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

Web scrapers are sometimes good

Sometimes a web scraper is a potential partner in disguise.

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

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

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

–> Site Indexer: Good.

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

–> Fraud Bot: Always bad.

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

How to identify and block bad bots and web scrapers

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

In-house

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

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

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

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

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

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

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

Some example providers are:

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

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

Software-as-a-service

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

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

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

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

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

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

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

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

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

•    What kind of latency does this solution introduce?

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

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

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.

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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.

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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/

Web Scraping: Startups, Services & Market

I got recently interested in startups using web scraping in a way or another and since I find the topic very interesting I wanted to share with you some thoughts. [Note that I’m not an expert. To correct me / share your knowledge please use the comment section]

Web scraping is everything but a new technique. However with more and more data shared on internet (from user generated content like social networks & review websites to public/government data and the growing number of online services) the amount of data collected and the use cases possible are increasing at an incredible pace.

We’ve entered the age of “Big Data” and web scraping is one of the sources to feed big data engines with fresh new data, let it be for predictive analytics, competition monitoring or simply to steal data.

From what I could see the startups and services which are using “web scraping” at their core can be divided into three categories:

•    the shovel sellers (a.k.a we sell you the technology to do web scraping)

•    the shovel users (a.k.a we use web scraping to extract gold and sell it to our users)

•    the shovel police (a.k.a the security services which are here to protect website owners from these bots)

The shovel sellers

From a technology point of view efficient web scraping is quite complicated. It exists a number of open source projects (like Beautiful Soup) which enable anyone to get up and running a web scraper by himself. However it’s a whole different story when it has to be the core of your business and that you need not only to maintain your scrapers but also to scale them and to extract smartly the data you need.

This is the reason why more and more services are selling “web scraping” as a service. Their job is to take care about the technical aspects so you can get the data you need without any technical knowledge. Here some examples of such services:

    Grepsr
    Krakio
    import.io
    promptcloud
    80legs
    Proxymesh (funny service: it provides a proxy rotator for web scraping. A shovel seller for shovel seller in a way)
    scrapingHub
    mozanda

The shovel users

It’s the layer above. Web scraping is the technical layer. What is interesting is to make sense of the data you collect. The number of business applications for web scraping is only increasing and some startups are really using it in a truly innovative way to provide a lot of value to their customers.

Basically these startups take care of collecting data then extract the value out of it to sell it to their customers. Here some examples:

Sales intelligence. The scrapers screen marketplaces, competitors, data from public markets, online directories (and more) to find leads. Datanyze, for example, track websites which add or drop javascript tags from your competitors so you can contact them as qualified leads.

Marketing. Web scraping can be used to monitor how your competitors are performing. From reviews they get on marketplaces to press coverage and financial published data you can learn a lot. Concerning marketing there is even a growth hacking class on udemy that teaches you how to leverage scraping for marketing purposes.

Price Intelligence. A very common use case is price monitoring. Whether it’s in the travel, e-commerce or real-estate industry monitoring your competitors’ prices and adjusting yours accordingly is often key. These services not only monitor prices but with their predictive algorithms they can give you advice on where the puck will be. Ex: WisePricer, Pricing Assistant.

Economic intelligence, Finance intelligence etc. with more and more economical, financial and political data available online a new breed of services, which collect and make sense of it, are rising. Ex: connotate.

The shovel police

Web scraping lies in a gray area. Depending on the country or the terms of service of each website, automatically collecting data via robots can be illegal. Whatever the laws say it becomes crucial for some services to try to block these crawlers to protect themselves. The IT security industry has understood it and some startups are starting to tackle this problem. Here are 3 services which claim to provide solutions to stop bots from crawling your website:

•    Distil
•    ScrapeSentry
•    Fireblade

From a market point of view

A couple of points on the market to conclude:

•    It’s hard to assess how big the “web scraping economy” is since it is at the intersection of several big industries (billion dollars): IT security, sales, marketing & finance intelligence. This technique is of course a small component of these industries but is likely to grow in the years to come.

•    A whole underground economy also exists since a lot of web scraping is done through “botnets” (networks of infected computers)

•    It’s a safe bet to say that more and more SaaS (like Datanyze pr Pricing Assistant) will find innovative applications for web scraping. And more and more startups will tackle web scraping from the security point of view.

•    Since these startups are often entering big markets through a niche product / approach (web scraping is a not the solution to everything, there are more a feature) they are likely to be acquired by bigger players (in the security, marketing or sales tools industries). The technological barrier are there.

Source: http://clementvouillon.com/article/web-scraping-startups-services-market/

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/

Hard-Scraped Hardwood Flooring: Restoration of History

Throughout History hardwood flooring has undergone dramatic changes from the meticulous hard-scraped hardwood polished floors of majestic plantations of the Deep South, to modern day technology providing maintenance free wood flooring designed for comfort and appearance. The hand-scraped hardwood floors of the South, depicted charm with old rustic nature and character that was often associated with this time era. To date, hand-scraped hardwood flooring is being revitalized and used in up-scale homes and places of businesses to restore the old country charm that once faded into oblivion.

As the name implies, hand-scraped flooring involves the retexturing the top layer of flooring material by various methods in an attempts to mimic the rustic appearance of flooring in yesteryears. Depending on the degree of texture required, hand scraping hardwood material is often accomplished by highly skilled craftsmen with specialized tools and years of experience perfecting this procedure. When properly done, hand-scraped hardwood floors add texture, richness and uniqueness not offered in any similar hardwood flooring product.

Rooted with history, these types of floors are available in finished or unfinished surfaces. The majority of the individuals selecting hand-scraped hardwood flooring elect a prefinished floor to reduce costs per square foot in installation and finishing labor charges, allowing for budget guidelines to bend, not break. As expected, hand-scraped flooring is expensive and depending on the grade and finish selected, can range from $15-40$ per square foot and beyond for material only. Preparation of the material is labor intensive adding to the overall cost per square foot dramatically. Recommended professional installation can and often does increase the cost per square foot as well, placing this method of hardwood flooring well out of reach of the average hardwood floor purchaser.

With numerous selections of hand-scraped finishes available, each finish is designed to bring out a different appearance making it a one-of-a-kind work of art. These numerous finish selections include:

• Time worn aged, dark coloring stain application bringing out grain characteristics

• Wire brushed, providing a highlighted "grainy" effect with obvious rough texture

• Hand sculpted, smoother distressed uniform appearance

• French Bleed, staining of edges and side joints with a much darker stain to give a bleeding effect to the wood

• Hand Hewn or Rough Sawn, with visible and noticeable saw marks

Regardless of the selection made, scraped flooring cannot be compared to any other available flooring material based on durability, strength and visual appearance. Limited by only the imagination and creativity, several wood species can be used to create unusual floor patterns, highlighting main focal points of personal libraries and art collections.

The precise process utilized in the creation of scraped floors projects a custom look with deep color and subtle warm highlights. With radiant natural light reflecting off this type of floor, the effect of beauty and depth is radiated in a fashion that fills the room with solitude and serenity encompassing all that enter. Hand-scraped hardwood floors speak of the past, a time of decent, a time or war and ambiguity towards other races and the blood- shed so that all men could be treated as equals. More than exquisite flooring, hand-scraped hardwood flooring is the restoration of History.

Source: http://ezinearticles.com/?Hard-Scraped-Hardwood-Flooring:-Restoration-of-History&id=6333218

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

Scraping expert's Amazon Scraper provides huge access to find your desired product on Amazon

Today, with latest advancement of technology we find plenty of ecommerce websites offering huge benefits to people by giving out various products from different categories at an affordable cost. One of the most renowned ecommerce website Amazon has come up with its all new launch of Amazon Scraper for the comfort of their customers. This product Amazon Scraper is also called web harvesting which is a computer software technique for getting out data from websites.

Today anyone can find such web scraping tools that are specifically designed for particular websites. Like for example, Amazon Scraper is also a web scraper tool or technique utilised to crawl, or scrap or even extract the data from the largest e commerce website called Amazon.com. Scrapingexpert.com offers best Amazon scraper for extracting plenty of products from websites easily.

Amazon scraper

Let us see how the Amazon Scraper works:

How to use: Step 1) Select the Category; Enter the (Keyword, UPC, and ASIN) Step 2) Set the delay in seconds Step 3) Click Start

Also you can Scrape the below given details from Amazon.com:

•     Product Title & Description
•     Category & Cost Manufacture,
•     QTY Seller Name,
•     Total Sellers Shipping Cost,
•     Shipping / Product Weight ImageURL, IsBuyBoxFBA, Source Link
•     Stars, Customer Reviews
•     ASIN, UPC, Model Number Sales Rank,
•     Sales Rank In Category
• Here are some interesting Product Features:
•     Single Screen Dashboard that shows total extracted records, extracted keywords, and elapse.
•     Filter Search - Skip data that do not match phrases or keywords
•     Compatible for Microsoft XP/Vista/Windows 7
•     Option to set delay between requests to simulate a human surfing in a browser
•     Extracted data is stored in CSV format, which you can easily open in excel
• Benefits:
•     Less Expensive - With our valuable services, we allow you to save both your efforts and money. We have some competitors who outsource their scraping projects to us.
•     Guaranteed Accurate Results - We assure you get most reliable solutions with accurate results that cannot be collected by any ordinary human being or anyone else.
•     Delivers Fast Results - We promise to get your work done in just few hours, which can take plenty of time if done by someone else. We save your time, workforce and money and give you an opportunity to stand at a distinction over your multiple competitors.
•     System Requirement: Operating System - Windows XP, Windows Vista, Windows 7 Net Framework 2.0

Are you searching for some cost effective programs to extract data of other users? If your answer is yes, then we offer Amazon Screen Scraping which is the best Amazon Screen Scraping method of data extraction. Today, in this competitive world of advanced technology there are multiple companies who claim to offer best Amazon Screen Scraping services, so hiring their services for Amazon Screen Scraping can allow you to scrap almost any data in any format you wish to obtain. Well, we at Scrapingexpert.com study each and every single bit of little details of the scraping project and then provide you with a free quote and the date of completing the work

In order to get accurate data pertaining to a specific product, you can use our Awesome Amazon Scraper Tool. This Awesome Amazon Scraping Tool is very effective tool that will help you to extract information about any product from Amazon.

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.

Life’s Solutions through Web Scraping

Incredibly, there is no other time in human existence that personal data can be accessed as easily and quickly as it is in the present time. Sadly though, records about a person’s activities are kept without even his or her knowledge about them. It is then the act of web scraping that unearths what these are and puts them to commodities of priceless value and of great use.

On the brighter side, solutions about one’s life’s dilemmas can be acquired completely by retrieving the information entered online by every individual. Specifically, it would be more convenient these days to write a person’s biography; evaluate his or her health history; and trace his or her activities through data mining.

Biography

A person’s life story can be known and written about by gleaning through his or her online activities such as emails; purchase records; and every other recorded online presence he or she has made during his or her life time. The biography can be objective as well as subjective: objective in the sense that actual activities and concrete evidences are on record; and subjective in the sense that each activity or data can be analyzed and construed based on other related activities or based on the context where the information is taken or made.

A person’s emails, for instance, can reveal a lot about his or her major decisions and activities in his or her life time. These emails are like journals that directly and indirectly reveal a person’s unique behavior, personality, and preferences. In addition, what he or she exposes through these electronic messages can show the kind of person he or she has been through the different stages in his or her life. It would then be very interesting to discover the many changes in one’s life at specific points and be amazed at how one has matured or developed through the years. Moreover, the person may discover more about himself or herself if he or she would take the time to study his or her own electronic correspondence.

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

Why Outsourcing Data Mining Services is the Leading Business Trend

Businesses usually have huge volumes of raw data that remains unprocessed. Processing data results into information. A company’s hunt for valuable information ends when it outsources its data mining process to reputable and professional data mining companies. In this way a company is able to derive more clarity and accuracy in the decision making process.

It is important too note that information is critical to the growth of a business. With the internet you are offered flexible communication and good flow of data. It is a good idea to make the data that is available readily and in a workable format where it will be useful to a business. The filtered data is deemed important to the organization and the services can be used to increase profits, ameliorating overall risks and smooth work flow.

Data mining process must engage the sorting data process through the vast data amounts of data and acquire pertinent information. Data mining is usually undertaken by professional, financial and business analysts. Nowadays, there are many growing fields that require data extraction services.

When making decisions data mining plays an important role as it enables experts to make decisions quick and in a feasible manner. The information that is processed finds wide applications for decision making that relate to e-commerce, direct marketing, health care, telecommunications, customer relationship management, financial utilities and services.

The following are the data mining services that are commonly outsourced to the professional data mining companies:

•    Data congregation. This is the process of extracting data from different websites and web pages. The common processes involved here include web scraping and screen scraping services. The data congregated is then in put into databases.

•    Collecting of contact data. This is the process of searching and collecting of information concerning contacts from different websites.

•    E-commerce data. This is data about various online stores. The information collected includes the various products and prices offered. Other information that is collected is about discounts.

•    Competitors. Information about your business competitors is quite important as it helps a business to gauge itself against other businesses. In this way a company can use this information to re-design its marketing strategies and develop its own pricing matrix.

In this era where business is hugely impacted by globalization, handling data is becoming a headache. This is where outsourcing becomes quite profitable and important to your business. Huge savings in terms money, time and infrastructure can be realized when data mining projects are customized to suit exact needs of a customer.

There are many benefits accrued when outsourcing data mining services to professional companies. The following are some of benefits that are accrued from the outsourcing process:

•    Qualified and skilled technical staff. Data mining companies employ highly competent staffs who have a successful career in IT industry and data mining. With such personnel you are assured of quality information extracted from databases and websites.

•    Improved technology. These companies have invested huge resources in terms of software and technology so as to handle the information and data in a technological way.

•    Quick turnaround time. Your data is processed in an efficient way and information presented in a timely way. These companies are able to present data in a timely manner even in tight deadlines.

•    Cost-effective prices. Nowadays there are many companies dealing with web scraping and data mining. Due to competition, these companies offer quality services at competitive prices.

•    Data safety. Data is quite critical and should not leak to your competitors. These companies are using the latest technology in ensuring that your data is not stolen by other vendors.

•    Increased market coverage. These companies serve many businesses and organizations with different data needs. By outsourcing to them you are assured of expertise dealing with your data have wide market coverage.

Outsourcing enables a company to shift its focus to the core business operations and improve its overall productivity. In fact outsourcing can be considered as a wise choice for any business. Therefore outsourcing helps businesses in managing data effectively. In this way you will be able to achieve and generate more profits. When outsourcing, it is advisable that you only consider professional companies only so as to be assured of high quality services.

Source: http://www.loginworks.com/blogs/web-scraping-blogs/216-why-outsourcing-data-mining-services-is-the-leading-business-trend/

How Web Data Extraction Services Will Save Your Time and Money by Automatic Data Collection

Data scrape is the process of extracting data from web by using software program from proven website only. Extracted data any one can use for any purposes as per the desires in various industries as the web having every important data of the world. We provide best of the web data extracting software. We have the expertise and one of kind knowledge in web data extraction, image scrapping, screen scrapping, email extract services, data mining, web grabbing.

Who can use Data Scraping Services?
Data scraping and extraction services can be used by any organization, company, or any firm who would like to have a data from particular industry, data of targeted customer, particular company, or anything which is available on net like data of email id, website name, search term or anything which is available on web. Most of time a marketing company like to use data scraping and data extraction services to do marketing for a particular product in certain industry and to reach the targeted customer for example if X company like to contact a restaurant of California city, so our software can extract the data of restaurant of California city and a marketing company can use this data to market their restaurant kind of product. MLM and Network marketing company also use data extraction and data scrapping services to to find a new customer by extracting data of certain prospective customer and can contact customer by telephone, sending a postcard, email marketing, and this way they build their huge network and bu
ild large group for their own product and company.

We helped many companies to find particular data as per their need for example.

Web Data Extraction

Web pages are built using text-based mark-up languages (HTML and XHTML), and frequently contain a wealth of useful data in text form. However, most web pages are designed for human end-users and not for ease of automated use. Because of this, tool kits that scrape web content were created. A web scraper is an API to extract data from a web site. We help you to create a kind of API which helps you to scrape data as per your need. We provide quality and affordable web Data Extraction application

Data Collection

Normally, data transfer between programs is accomplished using info structures suited for automated processing by computers, not people. Such interchange formats and protocols are typically rigidly structured, well-documented, easily parsed, and keep ambiguity to a minimum. Very often, these transmissions are not human-readable at all. That's why the key element that distinguishes data scraping from regular parsing is that the output being scraped was intended for display to an end-user.

Email Extractor

A tool which helps you to extract the email ids from any reliable sources automatically that is called a email extractor. It basically services the function of collecting business contacts from various web pages, HTML files, text files or any other format without duplicates email ids.

Screen scrapping

Screen scraping referred to the practice of reading text information from a computer display terminal's screen and collecting visual data from a source, instead of parsing data as in web scraping.

Data Mining Services
Data Mining Services is the process of extracting patterns from information. Datamining is becoming an increasingly important tool to transform the data into information. Any format including MS excels, CSV, HTML and many such formats according to your requirements.

Web spider

A Web spider is a computer program that browses the World Wide Web in a methodical, automated manner or in an orderly fashion. Many sites, in particular search engines, use spidering as a means of providing up-to-date data.

Web Grabber

Web grabber is just a other name of the data scraping or data extraction.

Web Bot

Web Bot is software program that is claimed to be able to predict future events by tracking keywords entered on the Internet. Web bot software is the best program to pull out articles, blog, relevant website content and many such website related data We have worked with many clients for data extracting, data scrapping and data mining they are really happy with our services we provide very quality services and make your work data work very easy and automatic.

Source:http://ezinearticles.com/?How-Web-Data-Extraction-Services-Will-Save-Your-Time-and-Money-by-Automatic-Data-Collection&id=5159023

Benefits of Predictive Analytics and Data Mining Services

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

Predictive analytics can be helpful in answering questions like:

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

Benefits of Data mining include:

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

Basic steps of Predictive Analytics are as follows:

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

Standard techniques used for it are:

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

    Product Association

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

Richard Kaith is member of Data Mining services team at Outsourcing Web Research firm - an established BPO company offering effective Web Data mining, Data extraction and Web research services at affordable rates.

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

Uranium Mining Revival in New Mexico through Solution Mining

"We've got to get quickly on a track to energy independence from foreign oil, and that means, among other things, going back to nuclear power," U.S. Senator John McCain (R-AZ) recently told Fox News. U.S. Sen. Pete Domenici (R-NM) invited Louisiana Enrichment Services (LES) to build a gas-centrifuge uranium enrichment facility near Hobbs, New Mexico. The facility is currently undergoing the permitting process. Southwest Research and Information Center's Annette Aguayo told us the group planned to begin working on stopping that project. Some environmentalists remain behind the times.

Other environmentalists, who led before, are leading again. James Lovelock, the spiritual guru of the world's environmental movement, sometimes called the "Father of the Green Revolution," because of his research and widely embraced warnings on DDT and CFCs, wrote in Reader's Digest, (March, 2005), "The figures show that many people's fears of nuclear energy are unreasonable." Dr. Lovelock also said "the Greens are plain wrong to oppose it." In May, 2004, Lovelock wrote, "Nuclear power is the only green solution."

New Mexico is primed for a uranium revival, not with conventional mining, but with ISL operations. The in situ leaching method, also known as solution mining, is environmentally friendly. Because it is low cost and does not contaminate the environment in ways that uranium mining did in the 1950s, many uranium companies plan to use this safer method for mining uranium in New Mexico.

In a conversation, late last year, with Grants Chamber of Commerce and Mining Museum employee Barbara Hahn, a deep resentment resounded in her voice when talking about the collapse of the uranium mining business in the 1980s. Grants (NM) was a boom town, during the 1970s uranium boom, when spot uranium prices climbed, and stayed above $40/pound. "Grants replaced the lost mining jobs by opening prisons," she told us. "Now, others bring us their prisoners." Ms. Hahn believed only 35 percent of the uranium had been extracted from the Grants Mineral Belt. "Most of it is still there," she added. According to a McLemore and Chenoweth geological report, a resource of 558 million pounds (279,000 short tons) might still be extracted. The question in the 1980s as it is today revolves around the spot price of uranium.

The higher the spot price of uranium, the more economic it can be to mine. As the price of uranium rises, then the quantity of an economic resource increases. At $30/pound, the U.S. Energy Information Administrated reported the state of New Mexico held 84 million pounds of uranium oxide, grading 0.28/ton, as of Dec 31, 2003. However, at $50/pound uranium, that quantity would jump to 341 million pounds. The spread on the gross value of the uranium assets between those price levels is nearly $15 billion! As the spot price escalates, the economic reserves grow.

Said William Sheriff, Director of Corporate Development for Energy Metals (TSX: EMC), "Our long-term, big, big projects are going to be in New Mexico. Long term, we think New Mexico is going to be quite valuable to us." He explained his company's plans are to first develop production centers in Texas and Wyoming, before developing ISL operations in The Enchanted State. Sheriff added, "Nothing in New Mexico in terms of the first five years, but that's not to say we're going to sit idly by. We're going to be aggressively pursuing these. The only thing we're going to be pursuing is ISL production." Based upon the company's extensive acquisitions in Wyoming, New Mexico and elsewhere, Sheriff threw down the gauntlet at Cameco and Cogema, whose ISL operations in Wyoming contribute the largest share of U.S. uranium production, "We intend to become the largest ISL producer in the United States."

David Miller, President and Chief Operation Officer of Strathmore Minerals, (TSX: STM; Other OTC: STHJF), believes, "The ISL production method will continue to grow in the United States, but we will also see a return to conventional mining and milling in the western states." In addition to their Wyoming uranium properties, Strathmore hopes to move forward their Church Rock uranium property on the heels of Uranium Resources' (OTC BB: URRE) permitting on Section 17, held by their HRI subsidiary. Basically, all three companies are friendly neighbors in the area. There is evidence they frequently talk among themselves, comparing notes. The three uranium juniors appear to be the current major players in New Mexico for ISL uranium mining.

Ron Driscoll, one of the co-founders of Quincy Energy, which has been acquired by Energy Metals, said, "It will get interesting when the oil companies get involved again." It is probably early for the oil giants to rush back into uranium. In the last uranium boom, many of the major oil companies were leaders in the uranium exploration and mining. Kerr-McGee Nuclear was the number one private sector uranium producer in the world. Other major oil companies involved in uranium mining and exploration included Mobil, Phillips, Conoco, Exxon, Chevron, Amoco and others. Another of the recently arrived uranium juniors, Max Resources (TSX: MXR) also plans to drill at the other end of New Mexico, in Socorro County (about 100 miles south of Albuquerque). MXR's property was once drilled by OxyMin, a subsidiary of Occidental Petroleum, during the 1980s, before the price of uranium fell off a cliff.

Perhaps, one major company will emerge in New Mexico, consolidating the others, or some of the others. "There's a huge number of small uranium plays in the North American market that need critical mass," Neal Froneman, CEO of Uranium One (TSE: SXR) recently told a South African newspaper. "Consolidation will drive our business in the US and Canada, where we think it's tactically smart to be." Uranium One was itself a consolidation between Toronto-based Southern Cross and South African-based Aflease. Froneman concluded, ""It makes sense to have a major presence in North America in order to supply the (U.S.) utilities that will need to be built."

"The geology for this area, with regards to ISL uranium operations, could help make New Mexico an important supplier to U.S. utilities, possibly before the end of this decade," Strathmore's David Miller agreed. "I would not be surprised at all if there were more uranium to be found in New Mexico than is currently estimated. That's why companies have exploration programs." From a state, which has produced over 300 million pounds of uranium, and which may have between 300 million and 600 million additional pounds of uranium, New Mexico will be a prime target for uranium companies as long as the price of uranium continues to rise. Will uranium crash and burn, as it did in the 1980s? After accurately predicting the spot price of uranium would double in a StockInterview feature in June 2004, Miller recently told StockInterview, "I wouldn't be surprised to see the price double again."

Source:http://ezinearticles.com/?Uranium-Mining-Revival-in-New-Mexico-through-Solution-Mining&id=179129

The Equipment Used in Mining

Bureau of Labor statistics reported that there are five major segments in the mining industry. They are gas and oil extracting, coal mining, non-metal mineral mining, metal ore mining and the supporting activities. In this matter, each segment might need different equipment. But, there are some types of mining equipment that are used by all segments of the mining industry.

Excavators

Excavators are types of equipment that are used by the miners to break and remove soil today. Traditionally, they used steam shovels and shovels to do the jobs. An excavator is a vehicle that moves with standard wheels or moves on tracks. There is a rotating platform and a bucket to its end for digging the soil.

Draglines

Draglines are very big earth moving machines that are used in mining industry. These machines are used to expose the underlying mineral deposits. These are also used to drag away the dirt. The Kentucky Coal Education said that draglines are one of the largest machine in the world. These can remove several hundred tons of the material in one pass.

Drills

Drills are very important for miners that extract natural gas and oil. Miners use these machines to reach underground deposits before they pipe the resources to the surface. Instead of being used in gas and oil mining, these machines are also used to mine coal and mineral.

Roof bolters

These machines are used to prevent underground collapses when the mining process is in progress. These are also used to support the tunnel roofs in mining location.

Continuous miners and longwall miners

These machines are usually used by subterranean coal miners. These machines are used to scrape coal from the coal beds. Meanwhile, the longwall miners are machines that are used to remove large, rectangular sections of coal instead of scraping coal from a bed.

Rock duster

These are pressurized pieces of equipment that are used in coal mining to spray inert mineral dust over the highly flammable coal dust. This inert dust will help prevent accidental explosions and fires.

Source:http://ezinearticles.com/?The-Equipment-Used-in-Mining&id=5633103

Data Mining Services in various types

How Companies Can the Most with Data Mining Services

The modern way to use data, effectively.

Data Mining is an act of transferring data into beneficial Information and actionable insight. Often known as Knowledge Discovery in Databases (KDD), Data Mining is a automated process to uncover a series of never-seen-before information in bulk quantities of data scenario. Post evaluating a series of random factors, which the human mind cannot easily look at or comprehend, it helps in reaching towards an actionable insight by means of progressive mathematical algorithms. These data mining reports are further distributed among esteemed influencers and stakeholders, and are used for enterprise-caliber data mining observations in an insightful manner.

The Process of Data Mining

Here’s a lowdown of a few used cases of how companies are using Data Mining Services in business: ASSOCIATION: Catching hold of frequently appearing observations. For instance, if you want to know which products are regularly purchased in pair, and could be offered together in a combo offer to boost sales.

CLASSIFICATION: Allowing the Data Mining experts at LoginWorks Software to attach observations towards repeated financial patterns of existing groups or categories. For instance, spotting fraudulent transactions or possibly bankrupt companies.

CLUSTERING:Identifying similarities and common ground between observations and groups. For instance, creating profiles for website users or clients by mapping website usage pattern and customer behavior.

DESCRIPTION:Detailing out patterns and showcasing them in a visual manner using explanatory analysis.

ESTIMATION: Revealing features that are difficult to observe with a straight-lined approach because of cost of observation or technical problems. PREDICTION: Predicting an estimated future using previous and present observations. for examples, predicting sales for the next financial period.

What are the Strategic Benefits of Incorporating Data Mining

A Comprehensive suite of Data Mining Services can help your company to:

•    Iron out strategic business problems with the use of number crunching, predictive and inferential analysis.

•    Recuperate your data mining atmosphere by making use of advanced algorithms, artificial neural networks, induction techniques, along with in-data and base-data mining technologies.

•    Automate business trends, understand human behavior and patterns predictions.

•    Do away with complexities of difficult-to-comprehend statistics and, need not necessarily require users to make use of complex applications/interface. Instead, we deliver compact results in the form of touch points, such as Excel, CSV, XML, text file and more.

•    Achieve high-end connectivity and communication capabilities.

The Power of LOGINWORKS Data Mining Services.

LOGINWORKS SOFTAWARES Data Mining Service is an advanced solution for predictive analytics designed to help companies in their strategic decision making. An ongoing process of discovery and interpretation, data mining unearths new and reliable patterns in your accumulated data and patterns, which you can make use of to adhere to testing business questions that calls for constant prediction and inference. With the ever evolving increase of business complexities, as well as the quantity and multiplicity of data, there’s a buzzing need for methods that are intelligently mechanical in nature and are backed by LOGINWORKS SOFTAWARES’ expert support; and data mining that fits the need of today’s businesses aptly. By and large, predictive data mining services makes use of pattern recognition technologies and statistical tools to help accelerate strategic business decisions and lead to more informed conversations with the target audience.

What is offered in our Data Mining Service.

•    First stage of discussion and estimating future direction: If your company would like to gain a competitive edge from our high-calibre Data Mining Services, do get in touch with our sales team at sales@loginworks.com so as to help you in understanding the most advanced benefits and opportunities.

•    Sharing feasibility statistics and studies: If you or your company has a clear view point of how you would want to make use of Data Mining in your flow of business, then do share with us your requirement to ask for a quote.

•    Segmentation and Profitability: Right from assessing the initial assessment to assessing the benefits and completing the data, we’ll share with you a comprehensive report on understanding of data needs.

•    The Final Stage: Data Mining Implementation Service: As soon as the Data Mining requirement is clearly undertstood, we build customized solutions to collect data in an automated fashion and export structured data into usable format.

BIG DATA SOLUTIONS AND SERVICES

Big data swiftly harnesses the ever-increasing volume on data on day-to-day basis and the incessant need of enterprisers to harness the true business value of such data in a quick turnaround time. Opening gates to a world of opportunities to find new and insightful calculations, Big Data can be generated at a variety of myriad speeds and types. This data further lends organizations, especially the burgeoning e-commerce industry of today, a competitive advantage, where estimated predictions becomes the bedrock of constant in-flow of costs and revenue.

LOGINWORKS SOFTWARES BIG DATA ADVANTAGE

Unearth the power of the accumulated data by making significant inroads into the digital revolution of 21st century. Leverage the advantage by using LOGINWORKS SOFTWARES end-to-end Big Data Solutions and Services. Our passion, backed with years of domain expertise and rich technical prowess empowers you to outline a Big Data strategy for your business to help uplift your overall IT roadmap, architect and re-imagine your business strategies. With us, you get the following services: Our all-encompassing  THINK, DEVELOP AND IMPLEMENT model for Big Data Services aids you to pick up the best strategies to adopt and use data. Our principle areas of focus for Big Data services are:

•    Big Data Management for the IT Organization
•    Big Data Analytics for the Business Organization

*change the Think, Build and Operate Model headline with THINK, DEVELOP AND IMPLEMENT model. big data process chart LOGINWORKS SOFTWARES Data Mining Services, also known as Loginworks DataStream is a perfect amalgamation of unlimited volumes, robust technology and matchless expertise. What sets up apart is our one-of-a-kind personalised approach, which makes use of optimal data warehouse technology. IF YOU ARE READY TO TAKE THE ADVANTAGE OF DATA MINING AND BOOST YOUR BUSINESS

– CONTACT LOGINWORKS SOFTWARES TODAY!

Source: http://www.loginworks.com/blogs/web-scraping-blogs/data-mining-services-various-types/

How to Deal with Content Scrapers

There are few approaches that people take when dealing with content scrapers. The Do Nothing Approach, Kill them all approach, Take Advantage of them approach.

The Do Nothing Approach

This is by far the easiest approach you can take. Usually the most popular bloggers would recommend this because it takes A LOT of time fighting the scrapers. This approach simply recommends that “instead of fighting them, spend your time producing even more quality content and having fun”. Now obviously if it is a well-known blog like Smashing Magazine, CSS-Tricks, Problogger, or others, then they do not have to worry about it. They are authority sites in Google’s eyes.

However during the Panda Update, we know some good sites got flagged as scrapers because google thought their scrapers were original content. So this approach is not always the best in our opinion.

Kill them all Approach

The exact opposite of the “Do Nothing Approach”. In this approach, you simply contact the scraper and ask them to take the content down. If they refuse to do so or simply do not reply to your requests, then you file a DMCA (Digital Millennium Copyright Act) with their host. In our experience, majority of the scraping websites do not have a contact form available. If they do, then utilize it. If they do not have the contact form, then you need to do a Whois Lookup.

Whois Lookup

You can see the contact info on the administrative contact. Usually the administrative, and technical contact is the same. The whois also shows the domain registrar. Most well-known web hosting companies and domain registrars have DMCA forms or emails. You can see that this specific person is with Hostgator because of their nameservers. HostGator has a form for DMCA complaints. If the nameserver is something like ns1.theirdomain.com, then you have to dig deeper by doing reverse IP lookups and searching for IPs.

You can also use a third party service for DMCA.com for takedowns.

Jeff Starr in his article suggest that you should block the bad guy’s IPs. Access your logs for their IP address, and then block it with something like this in your root .htaccess file:

1    Deny from 123.456.789

You can also redirect them to a dummy feed by doing something like this:

1    RewriteCond %{REMOTE_ADDR} 123\.456\.789\.

2    RewriteRule .* http://dummyfeed.com/feed [R,L]

You can get really creative here as Jeff suggests. Send them to really large text feeds full with Lorem Ipsum. You can send them some disgusting images of bad things. You can also send them right back to their own server causing an infinite loop which will crash their site.

The last approach that we take is to take Advantage of them.

Source:http://www.wpbeginner.com/beginners-guide/beginners-guide-to-preventing-blog-content-scraping-in-wordpress/

Data Mining - Techniques and Process of Data Mining

Data mining as the name suggest is extracting informative data from a huge source of information. It is like segregating a drop from the ocean. Here a drop is the most important information essential for your business, and the ocean is the huge database built up by you.

Recognized in Business

Businesses have become too creative, by coming up with new patterns and trends and of behavior through data mining techniques or automated statistical analysis. Once the desired information is found from the huge database it could be used for various applications. If you want to get involved into other functions of your business you should take help of professional data mining services available in the industry

Data Collection

Data collection is the first step required towards a constructive data-mining program. Almost all businesses require collecting data. It is the process of finding important data essential for your business, filtering and preparing it for a data mining outsourcing process. For those who are already have experience to track customer data in a database management system, have probably achieved their destination.

Algorithm selection

You may select one or more data mining algorithms to resolve your problem. You already have database. You may experiment using several techniques. Your selection of algorithm depends upon the problem that you are want to resolve, the data collected, as well as the tools you possess.

Regression Technique

The most well-know and the oldest statistical technique utilized for data mining is regression. Using a numerical dataset, it then further develops a mathematical formula applicable to the data. Here taking your new data use it into existing mathematical formula developed by you and you will get a prediction of future behavior. Now knowing the use is not enough. You will have to learn about its limitations associated with it. This technique works best with continuous quantitative data as age, speed or weight. While working on categorical data as gender, name or color, where order is not significant it better to use another suitable technique.

Classification Technique

There is another technique, called classification analysis technique which is suitable for both, categorical data as well as a mix of categorical and numeric data. Compared to regression technique, classification technique can process a broader range of data, and therefore is popular. Here one can easily interpret output. Here you will get a decision tree requiring a series of binary decisions.

Our best wishes are with you for your endeavors.

Source:http://ezinearticles.com/?Data-Mining---Techniques-and-Process-of-Data-Mining&id=5302867