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/

Making data on the web useful: scraping

Introduction

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

Scraping to the rescue!

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

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

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

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

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

Recipes

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

    Extracting data from HTML tables.

    Scraping using the Scraper Extension for Chrome

Both methods are useful for:

    Extracting individual lists or tables from single webpages

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

Neither will work for:

    Extracting data spread across multiple webpages

Challenge

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

Tip

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

Scraping more than one webpage: Scraperwiki

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

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

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

Structure of a scraper

Scrapers are comprised of three core parts:

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

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

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

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

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

A few reasons (non-exhaustive list!):

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

2.    If your information is spread across numerous pages.

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

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

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

Summary:

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

Further Reading

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

2.    Scraping for Journalists (ebook): Paul Bradshaw

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

4.    An Introduction to Compassionate Screen Scraping: Will Larson

Any questions? Got stuck? Ask School of Data!

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

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

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

A few reasons (non-exhaustive list!):

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

2.    If your information is spread across numerous pages.

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

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

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

Summary:

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

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

Scraping Services - Assuring Scraping Success with Proxy Data Scraping

Have you ever heard of "Data Scraping?" Data Scraping is the process of collecting useful data that has been placed in the public domain of the internet (private areas too if conditions are met) and storing it in databases or spreadsheets for later use in various applications. Data Scraping technology is not new and many a successful businessman has made his fortune by taking advantage of data scraping technology.

Sometimes website owners may not derive much pleasure from automated harvesting of their data. Webmasters have learned to disallow web scrapers access to their websites by using tools or methods that block certain ip addresses from retrieving website content. Data scrapers are left with the choice to either target a different website, or to move the harvesting script from computer to computer using a different IP address each time and extract as much data as possible until all of the scraper's computers are eventually blocked.

Thankfully there is a modern solution to this problem. Proxy Data Scraping technology solves the problem by using proxy IP addresses. Every time your data scraping program executes an extraction from a website, the website thinks it is coming from a different IP address. To the website owner, proxy data scraping simply looks like a short period of increased traffic from all around the world. They have very limited and tedious ways of blocking such a script but more importantly -- most of the time, they simply won't know they are being scraped.

You may now be asking yourself, "Where can I get Proxy Data Scraping Technology for my project?" The "do-it-yourself" solution is, rather unfortunately, not simple at all. Setting up a proxy data scraping network takes a lot of time and requires that you either own a bunch of IP addresses and suitable servers to be used as proxies, not to mention the IT guru you need to get everything configured properly. You could consider renting proxy servers from select hosting providers, but that option tends to be quite pricey but arguably better than the alternative: dangerous and unreliable (but free) public proxy servers.

There are literally thousands of free proxy servers located around the globe that are simple enough to use. The trick however is finding them. Many sites list hundreds of servers, but locating one that is working, open, and supports the type of protocols you need can be a lesson in persistence, trial, and error. However if you do succeed in discovering a pool of working public proxies, there are still inherent dangers of using them. First off, you don't know who the server belongs to or what activities are going on elsewhere on the server. Sending sensitive requests or data through a public proxy is a bad idea. It is fairly easy for a proxy server to capture any information you send through it or that it sends back to you. If you choose the public proxy method, make sure you never send any transaction through that might compromise you or anyone else in case disreputable people are made aware of the data.

A less risky scenario for proxy data scraping is to rent a rotating proxy connection that cycles through a large number of private IP addresses. There are several of these companies available that claim to delete all web traffic logs which allows you to anonymously harvest the web with minimal threat of reprisal. Companies such as offer large scale anonymous proxy solutions, but often carry a fairly hefty setup fee to get you going.

The other advantage is that companies who own such networks can often help you design and implementation of a custom proxy data scraping program instead of trying to work with a generic scraping bot. After performing a simple Google search, I quickly found one company (www.ScrapeGoat.com) that provides anonymous proxy server access for data scraping purposes. Or, according to their website, if you want to make your life even easier, ScrapeGoat can extract the data for you and deliver it in a variety of different formats often before you could even finish configuring your off the shelf data scraping program.

Whichever path you choose for your proxy data scraping needs, don't let a few simple tricks thwart you from accessing all the wonderful information stored on the world wide web!

Source: http://ezinearticles.com/?Assuring-Scraping-Success-with-Proxy-Data-Scraping&id=248993

Twitter Scraper Python Library

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

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

from scraperwiki import swimport

search = swimport('twitter_search').search

Then search for terms.

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

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

A more complete search

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

from scraperwiki.sqlite import save, select

from time import sleep

# Search by user to get some more

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

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

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

for user in users:

    search([user], num_pages = 2)

    save_var('previous_from_user', user)

    sleep(2)

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

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

Library

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

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

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

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

What is Web Scraping?

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

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

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

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

Uses of Web Scraping:

The following are some of the uses of web scraping:

•    Collect data from real estate listing

•    Collecting retailer sites data on daily basis

•    Extracting offers and discounts from a website.

•    Scraping job posting.

•    Price monitoring with competitors.

•    Gathering leads from online business directories – directory scraping

•    Keywords research

•    Gathering targeted emails for email marketing – email scraping

•    And many more.

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

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

•    Programming the Custom Web Scraper as per the needs.

•    Using Web Scraping Softwares available in market.

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

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

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

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

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

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

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

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

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

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

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

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

Improving performance for web scraping code

2 down vote favorite

I have a website in which the code scrapes other websites for getting the accurate data. While the code works good but there a decent lag in performance because the code firsts downloads the html stream from various sites(some times 9 websites), extracts the relative part and then renders the html page.

What should I do to get an optimal performance. Should I change from shared hosting (godaddy) to my own server or it has nothing to do with my hosting and I need to make changes to my code?

1 Answer

API/CSV

Ask those websites if they provide an API, or, if you don't need an up-to-date information or the information you need doesn't change frequently, if they can sell/give you for free the data itself (for example in an CSV file). Some small websites may have fancier ways to access data, like a CSV file for the older information, and an RSS feed for the changed one.

Those websites would probably be happy to help you, since providing you with an API would reduce their own CPU and bandwidth usage by you.

Profile

Screen scrapping is really ugly when it comes to performance and scaling. You may be limited by:

    your machine performance, since parsing, sometimes an invalid HTML file, takes time,

    your network speed,

    their network speed usage, i.e. how fast can you access the pages of their website depending on the restrictions they set, like the DOS protection and the number of requests per second for screen scrappers and search engine crawlers,

    their machine performance: if they spend 500 ms. to generate every page, you can't do anything to reduce this delay.

If, despite your requests to them, those websites cannot provide any convenient way to access their data, but they give you a written consent to screen scrape their website, then profile your code to determine the bottleneck. It may be the internet speed. It may be your database queries. It may be anything.

For example, you may discover that you spend too much time finding with regular expressions the relevant information in the received HTML. In that case, you would want to stop doing it wrong and use a parser instead of regular expressions, then see how this improve the performance.

You may also find that the bottleneck is the time the remote server spends generating every page. In this case, there is nothing to do: you may have the fastest server, the fastest connection and the most optimized code, the performance will be the same.

Do things in parallel:

Remember to use parallel computing wisely and to always profile what you're doing, instead of doing premature optimization, in hope that you're smarter than the profiler.

Especially when it comes to using network, you may be very surprised. For example, you may believe that making more requests in parallel will be faster, but as Steve Gibson explains in episode 345 of Security Now, this is not always the case.

Legal aspects

Also note that screen scrapping is explicitly forbidden by the conditions of use (like on IMDB) on many websites. And if nothing is said on this subject in conditions of use, it doesn't mean that you can screen scrape those websites.

The fact that the information is available publicly on the internet doesn't give you the right to copy and reuse it this way neither.

Why? you may ask. For two reasons:

    Most websites are relying on advertisement and marketing. When people use one of those websites directly, they waste some CPU/network bandwidth of the website, but in response, they may click on an ad or buy something sold on the website. When you screen scrape, your bot waste their CPU/network bandwidth, but will never click on an ad or buy something.

    Displaying the information you screen scrapped on your website can have even worse effects. Example: in France, there are two major websites selling hardware. The first one is easy and fast to use, has a nice visual design, better SEO, and in general is very well done. The second one is a crap, but the prices are lower. If you screen scrape them and give the raw results (prices with links) to your users, they will obviously click on the lower price every time, which means that the website with pretty design will have less chances to sell the products.

    People made an effort in collecting, processing and displaying some data. Sometimes they paid to get it. Why would they enjoy seeing you pulling this data conveniently and for free?

Source: http://programmers.stackexchange.com/questions/141403/improving-performance-for-web-scraping-code/141406#141406

How to prevent getting blacklisted while scraping

Crawlers can retrieve data much quicker and in greater depth than human searchers, so bad scraping practices can have some impact on the performance of the site.

Needless to say, if a single crawler is performing multiple requests per second and/or downloading large files, a under powered server would have a hard time keeping up with requests from multiple crawlers.

Since spiders don’t bring direct organic traffic and seemingly affect the performance of the site, most site admins hate spiders and do their best to prevent them.

Lets go through how websites detect and block spiders and also know the techniques to overcome those barriers.

Most websites don’t have anti scraping mechanisms since it would affect the user experience, but some sites do not believe in open data access.

Before going through this article always keep in mind that

    A GOOD SPIDER MUST OBEY A WEBSITE’S CRAWLING POLICIES.

HOW DOES DETECTING ‘SPIDER ACTIVITY’ WORK?

A web server can use different mechanisms to detect a spider from a normal user. Here are some methods used by a site to detect a spider:

•    Unusual traffic/high download rate especially from a single client/or IP address within a short time span raises a bot alert.

•    Repetitive tasks done on website based on an assumption that a human user won’t perform the same repetitive tasks all the time.

•    The site has honeypot traps inside their pages, these honeypots are usually links which aren’t visible to a normal user but only to a spider . When a scraper/spider tries to access the link, the alarms are tripped.

Spend some time and investigate the anti-scraping mechanisms used by a site and build the spider accordingly, it will provide a better outcome in the long run and increase the longevity and robustness of your work.

EASIEST WAY TO FIND IF A SITE HATES BOTS

Check the robots.txt file if it contains line like these, It means the site doesn’t like bots. However, since most sites want to be on Google (arguably the largest scraper of websites globally ;-)) they do allow access to bots and spiders.

User-agent: *
Disallow: /

This line is for preventing well-behaved bots or the bots which respect robots.txt.

Another way is CAPTCHAs irritating presence in the sites other than in authentication page.

WHAT HAPPENS WHEN YOU GET BANNED

There are two ways to ban a webspider, either by banning all accesses from a particular IP or by banning all accesses that use a specific id to access the server (most browsers and web spiders identify themselves whenever they request a page by user agents. Chrome browser for example uses Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/33.0.1750.149 Safari/537.36

The banning can be temporary or permanent. Temporary blocks can last minutes or hours.

HOW DO WE KNOW A SITE HAS BLOCKED US?

If any of the following symptoms appear on the site that you are crawling, it is a sign of being blocked or banned.

•    Showing CAPTCHA pages
•    Unusual content delivery delay
•    Frequent response with 404,301,500 errors,

also frequent appearance of these status codes are also indication of blocking.

•    401 Unauthorized
•    403 Forbidden
•    404 Not Found
•    408 Request Timeout
•    429 Too Many Requests

WEB CRAWLING BEST PRACTICES

These are the best practices we can follow to overcome the detection.

1. MAKE CRAWLING SLOWER, DO NOT DDoS THE SERVER, TREAT THEM NICELY

Use auto throttling mechanisms, which will automatically throttle crawling speed based on the load on both spider and the website, you are crawling and also adjust the spider to optimum crawling speed. The faster you crawl, the worse it is for everyone.

Put some random sleeps in between requests, Add some delays after crawled number of pages. Choose the lowest number of concurrent requests possible. These techniques make the spider looks like a human being.

2. DISGUISE YOUR REQUESTS BY ROTATING IP/PROXY

A server can easily detects a bot by checking the requests from a single IP address, So we use different IPs for making request to a server and detection rate become lesser. Make a pool of IPs that you can use and use random ones for each request.

There are several methods can be used to change the IP. Services like VPN ,shared proxies, TOR can help  and some third parties are also provides services for IP rotation.

3. USER-AGENT SPOOFING

Since every request made from a client end contains a user-agent header ,Using the same useragent multiple times leads to the detection of a bot. User agent spoofing is the best solution for this. Spoof the User agent by making a list of user agents and pick a random one for each request.

Websites do not want to block genuine users so you should try to look like one. Set your user-agent to a common web browser instead of using the library default (such as wget/version or urllib/version). You could even pretend to be the Google Bot: Googlebot/2.1; (http://www.google.com/bot.html)

You can check your user-agent string here:

http://www.whatsmyuseragent.com/

A good user-agent string list can be found here:

http://www.useragentstring.com/pages/useragentstring.php

4. BE AWARE OF HONEYPOTS

Some site designers put honeypot traps inside websites to detect web spiders, They may be links that normal user can’t see and a spider can.

When following links always take care that the link has proper visibility with no nofollow tag. Some honeypot links to detect spiders will be have the CSS style display:none or will be color disguised to blend in with the page’s background color.

5. DO NOT ALWAYS FOLLOW THE SAME CRAWLING PATTERN

Only robots follow the same crawling pattern,Sites that have intelligent anti-crawling mechanisms can easily detect spiders from finding pattern in their actions. Humans wont perform repetitive tasks a lot of times. Incorporate some random clicks on the page, mouse movements and random actions that will make a spider looks like a human client.

6. ALWAYS RESPECT THE robots.txt

All web spiders are supposed to follow rules that you place in a robots.txt file in a website, such as how frequently they are allowed to request pages, and from what directories they are allowed to crawl through. They should also be supplying a consistent valid User-Agent string that identifies the requests as a bot request.

Source: http://learn.scrapehero.com/how-to-prevent-getting-blacklisted-while-scraping/

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

Metadata Scraping Service

As mentioned in Robert's last blog post we set up a scraping service which supports users working with citations by extracting automatically references from digital library or publisher websites. We use a very similar service in BibSonomy to support our users while posting a new reference. However, the service is independent from BibSonomy. Our main goal is to make the metadata of other websites easily accessible to every user who needs bibliographic metadata. Therefore we offer the extracted information in BibTeX format. Most tools allow to import BibTeX so it should be very easy for everyone to get the data into his own tool. The service is running under the following URL:

http://scraper.bibsonomy.org/

Currently we support more than 60 different websites (here the full list) and we are working on further extensions. In the near future we will make the source code of our scrapers publicly available under GPL and we hope that other people will find it useful and start to help us by implementing their own scrapers.

How does the service work?

In principle there are two ways to use the service. One uses a so

called bookmarklet and the other is simply based on the URL. If you

have a webpage of a supported site e.g. from ACM digital library the

following page:

Logsonomy - social information retrieval with logdata

then you can copy this URL into the form on the service homepage and the service will return you the extracted BibTeX information. As this is not a very convenient way to access the data we provide a ScrapePublication button. This button is a small piece of JavaScript and can be copied to the toolbar of the browser. By pressing this button while visiting a digital library webpage the URL will be automatically copied and sent to the scraping service and the metadata is extracted.

The service has three options which can be used to customize it and to make it useful for other systems. Obviously one parameter is the URL itself which is used by the bookmarklet, too. The next is the selection parameter which allows to send text to the service and the last parameter allows to change the output format from html to plain BibTeX. This last parameter makes integration with other systems very simple.

If needed we can provide the metadata in other formats as well but currently we support only BibTeX.

Source: http://blog.bibsonomy.org/2008/11/metadata-scraping-service.html

Kimono Is A Smarter Web Scraper That Lets You “API-ify” The Web, No Code Required

A new Y Combinator-backed startup called Kimono wants to make it easier to access data from the unstructured web with a point-and-click tool that can extract information from webpages that don’t have an API available. And for non-developers, Kimono plans to eventually allow anyone track data without needing to understand APIs at all.

This sort of smarter “web scraper” idea has been tried before, and has always struggled to find more than a niche audience. Previous attempts with similar services like Dapper or Needlebase, for example, folded. Yahoo Pipes still chugs along, but it’s fair to say that the service has long since been a priority for its parent company.

But Kimono’s founders believe that the issue at hand is largely timing.

“Companies more and more are realizing there’s a lot of value in opening up some of their data sets via APIs to allow developers to build these ecosystems of interesting apps and visualizations that people will share and drive up awareness of the company,” says Kimono co-founder Pratap Ranade. (He also delves into this subject deeper in a Forbes piece here). But often, companies don’t know how to begin in terms of what data to open up, or how. Kimono could inform them.

Plus, adds Ranade, Kimono is materially different from earlier efforts like Dapper or Needlebase, because it’s outputting to APIs and is starting off by focusing on the developer user base, with an expansion to non-technical users planned for the future. (Meanwhile, older competitors were often the other way around).

The company itself is only a month old, and was built by former Columbia grad school companions Ranade and Ryan Rowe. Both left grad school to work elsewhere, with Rowe off to Frog Design and Ranade at McKinsey. But over the nearly half-dozen or so years they continued their careers paths separately, the two stayed in touch and worked on various small projects together.

One of those was Airpapa.com, a website that told you which movies were showing on your flights. This ended up giving them the idea for Kimono, as it turned out. To get the data they needed for the site, they had to scrape data from several publicly available websites.

“The whole process of cleaning that [data] up, extracting it on a schedule…it was kind of a painful process,” explains Rowe. “We spent most of our time doing that, and very little time building the website itself,” he says. At the same time, while Rowe was at Frog, he realized that the company had a lot of non-technical designers who needed access to data to make interesting design decisions, but who weren’t equipped to go out and get the data for themselves.

With Kimono, the end goal is to simplify data extraction so that anyone can manage it. After signing up, you install a bookmarklet in your browser, which, when clicked, puts the website into a special state that allows you to point to the items you want to track. For example, if you were trying to track movie times, you might click on the movie titles and showtimes. Then Kimono’s learning algorithm will build a data model involving the items you’ve selected.

That data can be tracked in real time and extracted in a variety of ways, including to Excel as a .CSV file, to RSS in the form of email alerts, or for developers as a RESTful API that returns JSON. Kimono also offers “Kimonoblocks,” which lets you drop the data as an embed on a webpage, and it offers a simple mobile app builder, which lets you turn the data into a mobile web application.

For developer users, the company is currently working on an API editor, which would allow you to combine multiple APIs into one.

So far, the team says, they’ve been “very pleasantly surprised” by the number of sign-ups, which have reached ten thousand*. And even though only a month old, they’ve seen active users in the thousands.

Initially, they’ve found traction with hardware hackers who have done fun things like making an airhorn blow every time someone funds their Kickstarter campaign, for instance, as well as with those who have used Kimono for visualization purposes, or monitoring the exchange rates of various cryptocurrencies like Bitcoin and dogecoin. Others still are monitoring data that’s later spit back out as a Twitter bot.

Kimono APIs are now making over 100,000 calls every week, and usage is growing by over 50 percent per week. The company also put out an unofficial “Sochi Olympics API” to showcase what the platform can do.

The current business model is freemium based, with pricing that kicks in for higher-frequency usage at scale.

The Mountain View-based company is a team of just the two founders for now, and has initial investment from YC, YC VC and SV Angel.

Source: http://techcrunch.com/2014/02/18/kimono-is-a-smarter-web-scraper-that-lets-you-api-ify-the-web-no-code-required/

Web Data Scraping - Scrape Business Data in no time

The Internet has evolved as one of the largest repositories of information for your business. You can design intelligent business processes to access a whole host of relevant information sources that will help you strategize, implement and deliver effective business objectives. Leveraging the benefits and usefulness of Web Scraping Tools is one such methodology that most businesses have adopted. Let us take a look at some of the ways it helps you easily scrape data relevant for your business.

Scraping for Business Information

Web Data Scraping is a technique, employed by most organizations. It involves the implementation of tools that help businesses extract unstructured data and convert them into usable business information. The focus of most scraping initiatives revolves around the organization’s need to glean the following information:

•    Competitor analysis to structure and strategist effectively

•    Price comparisons to price their products competitively

•    Customer feedbacks to enhance their product portfolio and provide customers with better brand experience   Market dynamics to help them identify areas of opportunities and threats

Using Scraping Tools

The abundance of information available on the Internet that helps you build up a productive business strategy can be easily extracted and leveraged to benefit your business. Tools have been designed with intuitive interface and intelligent algorithms which help in furthering this end.

Website Data Scraping tools are equipped for compatibility with a wide variety of applications so as to be able to explore a huge range of information sources.  These tools are fully automated and display the drag and drop facility ensuring users get to leverage the benefits of speed and convenience.

Data extraction tools are not only adept at extracting data, but are also equally well-equipped to combine relevant statistics from several social media platforms like YouTube, Twitter, and Google Analytics and so on. This helps businesses to analyse trends and plan strategies accordingly.

Challenges of the Data Scraping Process

Just as there is no dearth of data to be collected from the Web, there is also an abundance of web scraping tools to execute the data collection process. However, the capability of the tool to help you collect the appropriate data needs to be assured before you can proceed with its implementation. Some of the challenges faced by most businesses owing to their wrong choice of tools include the following:

•    Run-of-the-mill extraction tools are unable to scale up sufficiently in order to capture large volumes of data

•    Some tools are also unable to establish compatibility with most data sources and therefore do not provide a holistic data collection approach

•    Some tools are also not equipped to conduct an automatic detection of updates made to a data source and therefore end up providing inaccurate data.

In the light of all this it is essential that you identify the right tool for your need and select one that is embedded with an updated technology to help you achieve the following:

•    Ensure that you are able to access the appropriate data that you want

•    Help you structure it in the format you want

•    Provide quick and easy access to all available data sources no matter how complex

•    Run accurately and is a reliable source to help you churn out usable information.

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

Scraping the Bottom of the Barrel - The Perils of Online Article Marketing

Many online article marketers so desperately wish to succeed, they want to dump corporate life and work for themselves out of their home. They decide they are going to create an online money making website. Therefore, they look around to see what everyone else is doing, and watch the methods others use to attract online buyers, and then they mimic their marketing, their strategies, and their business models.

Still, if you are copying what other people (less ethical people) are doing in online article marketing, those which are scraping the bottom of the barrel and using false advertising and misrepresentations, then all you are really doing is perpetuating distrust on the Internet. Therefore, you are hurting everyone, including people like me. You must realize that people like me don't appreciate that.

Let me give you a few examples of some of the things going on out there, thing that are being done by people who are ethically challenged. Far too many people write articles and then on their byline they send the Internet surfer or reader of the article to a website that has a squeeze page. The squeeze page has no real information on it, rather it asks for their name and e-mail address.

If the would-be Internet surfer is unwise enough to type in their name and email address they will be spammed by e-mail, receiving various hard-sell marketing pieces. Then, if the Internet Surfer does decide to put in their e-mail address, the website grants them access and then takes them to the page with information about what they are selling, or their online marketing "make you a millionaire" scheme.

Generally, these are five page sales letters, with tons of testimonials of people you've never heard of, and may not actually exist, and all sorts of unsubstantiated earnings claims of how much money you will make if you give them $39.35 by way of PayPal, for this limited offer "Now!" And they will send you an E-book with a strategic plan of how you can duplicate what they are doing. The reality is whatever they are doing is questionable to begin with.

If you are going to do online article marketing please don't scrape the bottom of the barrel, there's just too much competition down there from what I can see. Please consider all this.

Source: http://ezinearticles.com/?Scraping-the-Bottom-of-the-Barrel---The-Perils-of-Online-Article-Marketing&id=2710103

SEO No No! Scraping & Splogging – Content Theft!

Until recently, you could as well as might possibly not have acknowledged how you can perform the earlier mentioned. Even so, the following element could be the really cool element.

Several. Get back to ScrapeBox Add-Ons and also down load your ScrapeBox Blog Analyzer add-on. Open it upwards, and transfer the actual .txt record you merely rescued. Struck start.

ScrapeBox goes through almost every back link you merely scraped and look these phones determine if these are your site that will ScrapeBox presently facilitates placing comments in. If it is, that turns environmentally friendly. If it isn’t, that turns reddish. Soon after it really is concluded, it is possible to “clean” the list insurance agencies the idea remove unsupported websites.

Just what you’re destined to be left with is ALL of the sites the competitor has back-links via, and most importantly, they all are capable of being mentioned in employing ScrapeBox!!

Help save that will “clean” listing with a report, import it this list involving websites you wish to touch upon, and then keep to the exact same steps you’d probably typically follow for you to touch upon websites. Inside of Ten mins you’ll have got all the comps website backlinks (which may be blocked by Public relations if you’d just like) along with you’ll be able to reply to every one of them inside a 20 min (because the list most likely won’t end up being Large).

Desire to force this specific even more?? Obviously you are doing, you’re in BHW

Each step is the same as over with the exception of one tiny issue as well as the addition of an extra step.

Instead of just employing a single foot print inside your first bounty (both from SB’s regular gui after which also the back link checker add-on) you’re likely to be using a A lot of open all of them. Here is what you do to consider this particular to a whole new amount.

Initial, you’re going to pick each of the URLs via AOL, Aol, Ask & Search engines using this footprint:

site:domainyourcompetingwith.org

That will go back ALL the at present found web pages in the area. Remove copy Web addresses along with save that will with a .TXT report.

Now, you’re planning to create the subsequent right in front of each of these URLs:

hyperlink:

Right now follow all of the steps while outlined above. Exactly what this may is actually obtain each of the backlinks to every single site of the rivals web site.

Because Google Back link Checker is simply capable of getting the first 1k Web addresses through Aol (while that’s all Google allows you to view) you could have missed out on a decent amount associated with website inbound links if they had been at night 1st 1k final results. Consequently performing the aforementioned further methods ensures that every brand new web site in the website anyone pay attention to backlinks implies a fresh and other pair of a listing of back-links that is possibly 1k back links long.

Now you understand how to locate, filtration along with take your competitors back links, stop looking at and also move and take action!

Source: https://freescrapeboxlist19.wordpress.com/

Data Mining and Predictive Analysis

Data collection and curing is the core foundation of most businesses. Database building thus is an important function and activity where enterprises invest heavily. With information now available on the Internet and easily obtained, it raises the importance of having professionals who crawl data and offer web scraping services.

Once the data is accessed, though, it is important to filter out the relevant data based on the business need. Although Many DaaS provider convert the unstructured web data into meaningful structured data it is recommended to be internally equipped to use the data to its maximum.

This understanding has given rise to the field of Data Mining. Data Mining is designed to explore large amounts of data in search of consistent patterns and connections between the variables and validate the findings by applying the detected patterns to the new sets of the data. Once these connections are established and understood, the end goal is to be able to predict the possible outcomes using predictive analysis techniques.

Together, both Data Mining and predictive analysis aid in making marketing campaigns more efficient. While predictive analysis helps simulate and understand what may happen, data mining helps identify exciting data patterns and connections.

The process of Data Mining and Predictive analysis consists of 3 steps

Exploration

Once a database is compiled, it needs to be cleaned, analysed and potential connections need to be built. This process involves filtering the relevant data and identifying the possible predictors. Data Exploration also sets a premise for preliminary feature selection to manage number of variables. This data is then prepared for statistical analysis using a wide variety of graphical and statistical parameters. This helps identify the most relevant variables and setups the predictive models to be built.

Data mining process

Validation

Next comes building various models and choosing the most relevant ones. This decision is based on their possible predictive performance and of being able to produce stable results across all the samples. Simple as it sounds, to truly get the results, all possible models must be treated with data to simulate scenarios. The model with most stable statistical feature is validated.

Application

Once the relevant models are finalised, the same is applied to new data to understand and predict the estimated outcomes. Application of data models is an ongoing and complex process since every new dataset needs to be configured in the model.

Data Mining and predictive analysis essentially involves blending statistical methodology where the traditional statistics machine learning and complex algorithms. This greatly increases the need for efficient and skilled data handlers. This could include data analysts and scientists.

See how you can become data scientist here:

Data crunchers use data mining and predictive analysis actively to get an edge in the big data management. Database platforms like Hadoop assist in database management and large-scale distribution. But the costs involved in setting up data centres and big data management capacity are high. Budgets allocated within the enterprise are more project-focussed and analytics budgets are usually limited. Quite often, big data and analytics project fail to launch because of this problem! The other problem is that to run effective predictive models, data requires to be handled by scientists with experience. Finding and setting together a technologically-advanced team is a daunting task most enterprises face outside the tech domain.

Predictive Analysis model

A predictive analysis model is essentially predicting the all possible outcomes from a given set of data. Here are a few steps that can be taken to help build and identify the “ideal” predictive analysis model. These steps more or less mirror the usual statistical methodology of building a test model.

Defining an objective

This is the first and a critical step. Unless the objective is identified and defined there can be no concrete results since there wouldn’t be clarity to compare the final outcome to the expected result. It also helps understand the scope of the project.

Preparing the data

This is more to do with data mining. Historic data used for training the model is scattered across multiple platforms and sources. To compound the problem, data can be unstructured with possible duplicate accounts and missing values! Data quality determines the quality of the model, and thus it becomes imperative that data is healthy and relevant.

Data Sampling

Once mined, Data is essentially split into 2 parts. One set is for training that is used to build the model and the second is the ‘test’ set that is used to verify the accuracy of the final output. This also helps identify and filter the noise component.

Model Building

Sampling cam equally result in a single algorithm or parallel & connected algorithms. In such a case the data goes through multiple testing and a decision is based on the final output.

Execution

Once a model gets finalised, the other teams in the organization need to be involved to build a deployable model and understand its impact on the overall business.

The possibilities with Data mining & Predictive analysis are huge. It also gives a huge room for learning and experimenting. There are several tools available in the industry to aid through all the steps of data mining and predictive analysis. The combination of human expertise and intellect along with the help of the available tools and the overall cooperation within the multiple channels within the organization essentially ensures a stronger grip on the ability to build a solid predictive model.

When used together, predictive analytics and data mining help marketing professionals anticipate and get ready for customer needs, rather than just reacting to them.

Source: https://www.promptcloud.com/blog/data-mining-and-predictive-analysis/

How to Build Data Warehouses using Web Scraping

Businesses all over the world are facing an avalanche of information which needs to be collated, organized, analyzed and utilized in an appropriate fashion. Moreover, with each increasing year there is a perceived shortening of the turnaround time for businesses to take decisions based on information they have assimilated. Data Extractors, therefore, have evolved with a more significant role in modern day businesses than just mere collectors or scrapers of unstructured data. They cleanse structure and store contextual data in veritable warehouses, so as to make it available for transformation into useable information as and when the business requires. Data warehouses, therefore, are the curators of information which businesses seek to treasure and to use.

Understanding Data Warehouses

 Traditionally, Data Warehouses have been premised on the concept of getting easy access to readily available data. Modern day usage has helped it to evolve as a rich repository to store current and historical data that can be used to conduct data analysis and generate reports. As it also stores historical data, Data Warehouses are used to generate trending reports to help businesses foresee their prospects. In other words, data warehouses are the modern day crystal balls which businesses zealously pore over to foretell their future in the Industry.

Scraping Web Data for Creating Warehouses

The Web, as we know it, is a rich repository of a whole host of information. However, it is not always easy to access this information for the benefit of our businesses through manual processes. The data extractor tools, therefore, have been built to quickly and easily, scrape, cleanse and structure and store it in Data Warehouses so as to be readily available in a useable format.

Web Scraping tools are variously designed to help both programmers as well as non-programmers to retain their comfort zone while collecting data to create the data warehouses. There are several tools with point and click interfaces that ease out the process considerably. You can simply define the type of data you want and the tool will take care of the rest. Also, most tools such as these are able to store the data in the cloud and therefore do not need to maintain costly hardware or whole teams of developers to manage the repository.

Moreover, as most tools use a browser rendering technology, it helps to simulate the web viewing experience of humans thereby easing the usability aspect among business users facilitating the data extraction and storage process further.

Conclusion

The internet as we know it is stocked with valuable data most of which are not always easy to access. Web Data extraction tools have therefore gained popularity among businesses as they browse, search, navigate simulating your experience of web browsing and finally extract data fields specific to your industry and appropriate to your needs. These are stored in repositories for analysis and generation of reports. Thus evolves the need and utility of Data warehouses. As the process of data collection and organization from unstructured to structured form is automated, there is an assurance of accuracy built into the process which enhances the value and credibility of data warehouses. Web Data scraping is no doubt the value enhancers for Data warehouses in the current scenario.

Source: http://scraping-solutions.blogspot.in/2014/09/how-to-build-data-warehouses-using-web.html

How does Web Scraping Identify the Data you Want

The Web is one of the biggest sources of data that should be leveraged for your business. Be it an email, an URL or even a hyperlink text you are looking at, it comprises data that could be translated into useful information for your business. The challenge however lies in identifying the data that is relevant for your needs and enabling access to the required data. Web Scraping tools, however, are geared to help you address this need and leverage the benefit of this huge information repository.

Web Scraping and how it Works?

 Web Scraping is the practice followed to extract data from relevant sources on the Web and transforming them into crucial information packages for use in your business. This is an automated process which is executed with the help of a host of intuitive Web Extraction tools, thus facilitating ease, accuracy and convenience in extracting vital data.

Scrapers also work by writing intelligent pieces of code that scour the web and extract data that you need for the benefit of your business. The languages used for coding these scrapers are Python, Ruby and PHP. The language you use will be determined by the community you have access to.

As mentioned earlier, the biggest challenge that web scraping is subjected to include the identification of the right URL, page and element in order to scrape out the required information. No matter how good you may be at coding scripts, no amount of that will help you achieve your objective if you fail to develop an understanding of the way the web is structured. It is this which will enable you to structure your code in a manner that will be the most effective in scraping the desired information.

Understanding a Web Site

 A Web Site appears on your browser owing to two technologies. These include:

•     HTTP – The language used to communicate with the server for requesting the retrieval of resources, namely, images, videos, and documents and so on.
•     HTML – The language that helps to display the retrieved information on the browser.

The display format of your website is therefore defined using the HTML. It is within the folds of its syntax, that you will find the data which you need to extract. It is, therefore, important that you understand the anatomy of a web site by studying the structure of an HTML Page.

The HTML Page Structure

 An HTML page comprises a stack of elements known as tags, each bearing a specific significance. The first among these being the header tags that comprises mostly all the elements within it. The table element, the most important so far as data containers are concerned, is a crucial element that you need to study. It comprises several table rows (TR) and table data (TD) elements that hold the vital data nuggets that you might need to train your scrapers to extract.

In addition to these, HTML pages comprise a series of other tags that act as vital data holders, namely, image tags (img src), hyperlinks (a href) and the div tags which essentially refer to a block of text.

The scraper code needs to be built around your understanding of the HTML elements. Knowing the elements will help you to understand the specific location where relevant data are stacked. This helps you to correctly define the code so as to enable the scraper to search and extract the right element in order to provide you with the most appropriate information.

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.

Data Mining Process - Why Outsource Data Mining Service?

Overview of Data Mining and Process:

Data mining is one of the unique techniques for investigating information to extract certain data patterns and decide to outcome of existing requirements. Data mining is widely use in client research, services analysis, market research and so on. It is totally based on mathematical algorithm and analytical skills to drive the desired results from the huge database collection.

Information mining is mostly used by financial analyzer, business and professional organization and also there are many growing area of business that are get maximum advantages of data extract with use of data warehouses in their small to large level of businesses.

Most of functionalities which are used in information collecting process define as under:

* Retrieving Data
* Analyzing Data
* Extracting Data
* Transforming Data
* Loading Data
* Managing Databases

Most of small, medium and large levels of businesses are collect huge amount of data or information for analysis and research to develop business. Such kind of large amount will help and makes it much important whenever information or data required.

Why Outsource Data Online Mining Service?

Outsourcing advantages of data mining services:

o Almost save 60% operating cost

o High quality analysis processes ensuring accuracy levels of almost 99.98%

o Guaranteed risk free outsourcing experience ensured by inflexible information security policies and practices

o Get your project done within a quick turnaround time

o You can measure highly skilled and expertise by taking benefits of Free Trial Program.

o Get the gathered information presented in a simple and easy to access format

Thus, data or information mining is very important part of the web research services and it is most useful process. By outsource data extraction and mining service; you can concentrate on your co relative business and growing fast as you desire.

Outsourcing web research is trusted and well known Internet Market research organization having years of experience in BPO (business process outsourcing) field.

If you want to more information about data mining services and related web research services, then contact us.

Outsourcing Web Research has best infrastructure includes 200+ workstations supported by advanced technologies for operational efficiency and optimum security of your data and information.

Source: http://ezinearticles.com/?Data-Mining-Process---Why-Outsource-Data-Mining-Service?&id=3789102

Safeguarding the Future Through Data Mining

Web scraping can be a powerful tool not only in business and research. In fact, it has the capacity to protect the future by its predicting power. You may find this declaration incredible; but data mining is indeed a tangible way of predicting future events and thus protecting life in the future.

With the thousands of years of existence on earth, humans are able to gather as much information and experience to have a glimpse of what is to come. With the cycles of changes in the environment and in the whole universe aside from the human behavior, so much can be learned and applied.

At least three major things can be determined by careful and diligent data mining. These are: future threats; future trends; and future tactics.

Future threats

According to reports, the US intelligence agencies have been using web extraction as a way of studying the present and past terrorism acts and personages to predict future terrorist events. This has been actively done since the year 2010.

Data is gathered about a known terrorist such as: his activities; his contacts; his routines; the places he frequents; and other related information. These data are analyzed and classified. Any suspicious activities as well as unusual contact are monitored closely. Through these stored data and monitoring processes, any untoward activities can be precluded and preempted. You may say that terrorists can be using data mining too; and that is obviously possible. In this way, web scraping can also be used as a weapon for destruction. There is then a need for the government agencies to be very careful in protecting their data so that the enemies cannot retrieve them.

In the overall picture, you can just imagine how many lives, trauma, and damage can be prevented if future terrorist activities are prevented.

Moreover, climate change is another phenomenon that has already been predicted and is beginning to occur nowadays. Scientists have been studying the effects of global warming and environmental degradation through online data too. So much information drives and warning have been published by scholarly papers and by the experts but many of these have remained unheeded. Now that erratic weather conditions are happening, people can only regret and feel guilty that they are part of the cause of the problem.

However, it is not really too late to do some actions. People can avoid places where abnormal conditions are expected to happen; they can do some measures to protect themselves; and they can be informed ahead of time before anything catastrophic could happen.

Future trends

In relation to the predictions of possible threats, data extraction can also predict future trends. This is most helpful in businesses because they can be helped to produce items and employ strategies that will suit the expected patrons and clients.  Since history tends to repeat itself, data gathered in the past and present if studied judiciously and compared intelligently can bring in positive results.

Oftentimes, the companies that study their books as well as of those who have gone before them can gain more knowledge and expertise that will surely put them ahead of their contemporaries.

Future tactics

Naturally, along with knowing the possible events and trends in the future, strategies and ways to combat threats and cope with trends can also be predicted through web scraping.

Safeguarding the future is no longer a dream or wish. As early as today, experts can create equipment, structures, strategies, and even weapons to prevent any untoward incidents and collateral damage.

Studying the strengths and weaknesses of the past and present plans, procedures, and tools can lead to better technologies and techniques. The future can be a better and safer place if people can learn from the mistakes of the past and go from good to better.

The statement: “The best is yet to come,” will finally be realized if proper management of data and information collected and analyzed through web scraping will be conducted.

Bright future

Looking at the horizon, one can always expect the sun to shine and bring in a bright day. This same positive expectation for the future is indeed possible. Thanks to data mining; life can be handled more securely and precisely.

It does not mean that humans have become gods. It only proves that a person’s talents and skills, when used properly can make his/her future brighter and more successful. On the other hand, carelessness and lack of sensibilities to other people and the environment can surely bring in future doom.

Everything is laid bare and you are given the chance to handle the present with enough wisdom and capabilities. Although the world is too big to be understood and there is still a huge field of knowledge to be conquered, life can surely go on positively.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/257-safeguarding-the-future-through-data-mining/

6 Benefits Associated with Data Mining

Data has been used from time immemorial by various companies to manage their operations.Data is needed by various organizations strategically aimed at expanding their business operations, reduction of costs, improve their marketing force and above all improve profitability. Data mining is aimed at the creation of information assets and uses them to leverage their objectives.

In this article, we discuss some of the common questions asked about the data mining technology. Some of the questions we have addressed include:

•    How can we define data mining?
•    How can data mining affect my organization?
•    How can my business get started with data mining?

Data Mining Defined

Data mining can be regarded as a new concept in the enterprise decision support system, usually abbreviated as DSS. It does more than complementing and interlocking with the DSS capabilities that may involve reporting and query. It can also be used in on-line analytical processing (OLAP), traditional statistical analysis and data visualization. The technology comes up with tables, graphs and reports of the past business history.

We may define data mining as modeling of hidden patterns and discovering data from large volumes of data.It is important to note that data mining is very different from other retrospective technologies because it involves the creation of models. By using this technology, the user can discover patterns and use them to build models without even understanding what you are after. It gives explanation why the past events happened and even predicting what is likely to happen.

Some of the information technologies that can be linked to data mining include neural networks, fuzzy logic, rule induction and genetic algorithms. In this article we do not cover those technologies but focus on how data mining can be used to meet your business needs and you can translate the solutions thereafter into dollars.

Setting Your Business Solutions and Profits

One of the common questions asked about this technology is; what role can data mining play for my organization? At the start of this article we described some of the opportunities that can be associated with the use of data. Some of those benefits include cost reduction, business expansion, sales and marketing and profitability. In the following paragraphs we look into some of the situations where companies have used data mining to their advantage.

Business Expansion

Equity Financial Limited wanted to expand their customer base and also attract new customers. They used the Loan Check offer to meet their objectives. Initiating the loan, a customer had to go to any branch of Equity branch and just cash the loan. Equity introduced a $6000 LoanCheck by just mailing the promotion to their existing customers. The equity database was able to track about 400 characteristics of every customer. The characteristics were about loan history of the customer, their active credit cards, current balance on the credit cards and if they could respond to the loan offer. Equity used data mining to shift through 400 customer features and also finding the significant ones. They used the data and build model based on the response to the Loan Check offer. They then integrated this model to 500,000 potential customers from credit bureau. They then selectively mailed the most potential customers that were determined by the data mining model.At the end of the process they were able to generate a tot
al of $2.1M in extra net income from 15,000 new customers.

Reduction of Operating Costs
Empire is one of the largest insurance companies in the country. In order to compete with other insurance companies, it has to offer quality services and at the same time reducing costs.Therefore it has to attack costs that may in form of fraud and abuse. This demands a considerable investigation skills and use of data management technology. The latter calls for data mining application that can profile every physician in their network based on claims records of every patient in their data warehouse. The application is able to detect subtle deviations on the physician behavior that are linked to her/her peer group. The deviations are then reported to the intelligence and fraud investigators as “suspicion index.” With this effort derived from data mining, the company was able to save $31M, $37M, and $41M in the first three years respectively from frauds.

Sales Effectiveness and Profitability

In this case we look into pharmaceutical sector. Their sales representatives have wide range of assortment tools they use in promoting various products to physicians. Some of the tools include product samples, clinical literature, dinner meetings, golf outings, teleconferences and many more. Therefore getting to know the promotions methods that are ideal for particular physician is of valuable importance and it is likely to cost the company a lot of dollars in sales call and thereby more lost revenue.

Through data mining, a drug maker was able to link eight months of promotional activity based on corresponding sales found in their database. They then used this information to build a predictive model for each physician.The model revealed that for the six promotional alternatives, only three had a significant impact. Then they used the knowledge found in the data mining models and thereby customizing the ROI.

Looking at those two case studies, then ask yourself, was data mining necessary?

Getting Started

All the cases presented above have revealed how data mining was used to yield results to the various businesses. Some of the results led to increased revenue and increased customer base. Others can be regarded as bottom-line improvements that impacted on cost savings and also improved productivity.In the next few paragraphs we try to answer the question; how can my company get started and start realizing the benefits of data mining.

The right time to start your data mining project is now. With the emergence of specialized data mining companies, starting the process has been simplified and the costs greatly reduced. Data mining project can offer important insights into the field and also aggregate the idea of creating a data warehouse.

In this article we have addressed some of the common questions regarding data mining, what are the benefits associated with the process and how a company can get started. Now, with this knowledge your company should start with a pilot project and then continue building a data mining capability in your company; to improve profitability, market your products more effectively, expand your business and also reduce costs.

Source: http://www.loginworks.com/blogs/web-scraping-blogs/255-benefits-associated-with-data-mining/

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

What is Data Mining? Why Data Mining is Important?

Searching, Collecting, Filtering and Analyzing of data define as data mining. The large amount of information can be retrieved from wide range of form such as different data relationships, patterns or any significant statistical co-relations. Today the advent of computers, large databases and the internet is make easier way to collect millions, billions and even trillions of pieces of data that can be systematically analyzed to help look for relationships and to seek solutions to difficult problems.

The government, private company, large organization and all businesses are looking for large volume of information collection for research and business development. These all collected data can be stored by them to future use. Such kind of information is most important whenever it is require. It will take very much time for searching and find require information from the internet or any other resources.

Here is an overview of data mining services inclusion:

* Market research, product research, survey and analysis

* Collection information about investors, funds and investments

* Forums, blogs and other resources for customer views/opinions

* Scanning large volumes of data

* Information extraction

* Pre-processing of data from the data warehouse

* Meta data extraction

* Web data online mining services

* data online mining research

* Online newspaper and news sources information research

* Excel sheet presentation of data collected from online sources

* Competitor analysis

* data mining books

* Information interpretation

* Updating collected data

After applying the process of data mining, you can easily information extract from filtered information and processing the refining the information. This data process is mainly divided into 3 sections; pre-processing, mining and validation. In short, data online mining is a process of converting data into authentic information.

The most important is that it takes much time to find important information from the data. If you want to grow your business rapidly, you must take quick and accurate decisions to grab timely available opportunities.

Outsourcing Web Research is one of the best data mining outsourcing organizations having more than 17 years of experience in the market research industry. To know more information about our company please contact us.

Outsourcing Web Research is one of the best data mining outsourcing organizations having more than 17 years of experience in the market research industry.

Source: http://ezinearticles.com/?What-is-Data-Mining?-Why-Data-Mining-is-Important?&id=3613677