How to scrape address from Google Maps

If you want to build a new online directory based website and want it to be popular with latest web contents, then you need the help of web scraping services from iWeb scraping. If you want to scrape address from maps.google.com, there is a specialized web scraping tool developed by iWeb scraping which can do the job for you. There are plenty of benefits with web scraping which includes market research, gathering customer information, managing product catalogs, compare prices, gather real estate data, gather job posting information etc. Web scraping technology is very popular nowadays and it saves lot of time and effort involved in manual extraction of data from websites.

The web scraping tools developed iWeb Scraping is very user-friendly and can extract specific information from targeted websites. It converts data from HTML web pages to useful formats like Excel spread sheets or Access database. Whatever web scraping requirements you have, you can contact iWeb Scraping as they have more than 3.5 years of web data extraction experience and offer the best prices in the industry. Also their services are available in 24x7 basis and free pilot projects will be done based on request.

Companies which require specific web data and look for an application which can automate the process and export the HTML data in structured format could benefit greatly from web scraping applications of iWeb scraping. You can easily extract data from multiple target websites, parse and re-assemble the information in HTML format to database or spread sheets as you wish. The application has simple point-and-click user-interface and any beginner can use it scrape address from Google Maps. If you want to gather address of people in particular region from Google maps, you can do it with help of web scraping application developed by iWebscraping.

Web Scraping is a technology that able to digest target website databases that are visible only as HTML web pages, and create a local, identical replica of those databases as a information or result. With our web scraping & web data extraction service we can capture web pages, then pin-point specific pieces of data/information you'd like to extract from web pages. What is needed in this process is much more than a Website crawler and set of Website wrappers. The time required to do web data extraction goes down in comparison to manually data copying and pasting job.

Source:http://www.articlesbase.com/information-technology-articles/how-to-scrape-address-from-google-maps-4683906.html

Most Of The Recommended Web Scraping Data Into Business

More traditional Web search engines, websites visited, depending on how they were collected. The main disadvantage of these search engines is that they do not provide a method to extract the necessary information.

However, in modern times, the concept of scraping offs the website. Scraping all the relevant information and data contained in any web site can be found on the Internet together with the appearance.

Organizations and individuals to effectively and quickly recognized the need to gather information on the web scraping. Data structure that is more cut and paste can be accessed without having to contend with can not be collected.

If any other type of information to be able to arrange for the document. Traditional search engines use tools to harvest this website to a combination of individual clerks more sophisticated nuance with broad power. According to the criteria specified in the field of information is required.

News of the report on the software makes it easy for the crowd. The price and other analyzes to compare a pair of runs. Therefore, the Internet continues to work on the agencies that are required are a website as scrap. Web scraping by is the main reason for the growing number of companies.

Scraping the most reliable data Services Company based in India, offshore website provides information solutions to customers scraping. Data services to accomplish with your web search to try scraping, data mining, data conversion, data extraction, web scraping and web data in the data scraping.

Data Services are owned by scraping solution internet - India-based "Most of your trusted and reliable" service provider outsourcing. Data scraping Services offers high quality, accurate and manual internet scrape data and on the web scraping services at the lowest possible rate industry.

Data scraping Services is a firm based on the Indian expertise in outsourcing data entry, data processing, and Internet search and website scrape data. Data scraping Services offers great variety of data entry, data conversion, document scanning and data scraping service at the lowest possible rate industry since 2005. Services we offer cover the following areas; data entry, data mining, Web search, data conversion, data processing, scrape web sites, harvesting and collection of data internet email.

Data scraping Services follow the standard process to the highest quality Web search, data mining and web site services scratching. Search our website, data mining and data conversion projects to the process quality standards.

Most often the data must be scratched for the industry as part of lawyers, doctors, hospitals, students, schools, universities, chiropractor, dentists, hotels, property, real estate, pub, the bars, night club, a restaurant, and IT professionals. The most common medium to the database scraping and email numbers are directory business online, linked to, Twitter, Face book, social networking sites and search Google.

Data scraping service provider is the most trusted and reliable world of service, service of process data, data scrape, scrape data website, data mining, data extraction and business development database. We have already scraped some popular online business directories. We are only able to scrape public database available in any of the directory business.

Source:http://www.articlesbase.com/outsourcing-articles/most-of-the-recommended-web-scraping-data-into-business-5697814.html

Limitations and Challenges in Effective Web Data Mining

Web data mining and data collection is critical process for many business and market research firms today. Conventional Web data mining techniques involve search engines like Google, Yahoo, AOL, etc and keyword, directory and topic-based searches. Since the Web's existing structure cannot provide high-quality, definite and intelligent information, systematic web data mining may help you get desired business intelligence and relevant data.

Factors that affect the effectiveness of keyword-based searches include:

• Use of general or broad keywords on search engines result in millions of web pages, many of which are totally irrelevant.

• Similar or multi-variant keyword semantics my return ambiguous results. For an instant word panther could be an animal, sports accessory or movie name.

• It is quite possible that you may miss many highly relevant web pages that do not directly include the searched keyword.

The most important factor that prohibits deep web access is the effectiveness of search engine crawlers. Modern search engine crawlers or bot can not access the entire web due to bandwidth limitations. There are thousands of internet databases that can offer high-quality, editor scanned and well-maintained information, but are not accessed by the crawlers.

Almost all search engines have limited options for keyword query combination. For example Google and Yahoo provide option like phrase match or exact match to limit search results. It demands for more efforts and time to get most relevant information. Since human behavior and choices change over time, a web page needs to be updated more frequently to reflect these trends. Also, there is limited space for multi-dimensional web data mining since existing information search rely heavily on keyword-based indices, not the real data.

Above mentioned limitations and challenges have resulted in a quest for efficiently and effectively discover and use Web resources. Send us any of your queries regarding Web Data mining processes to explore the topic in more detail.

Source: http://ezinearticles.com/?Limitations-and-Challenges-in-Effective-Web-Data-Mining&id=5012994

Scraping Fantasy Football Projections from the Web

In this post, I show how to download fantasy football projections from the web using R.  In prior posts, I showed how to scrape projections from ESPN, CBS, NFL.com, and FantasyPros.  In this post, I compile the R scripts for scraping projections from these sites, in addition to the following sites: Accuscore, Fantasy Football Nerd, FantasySharks, FFtoday, Footballguys, FOX Sports, WalterFootball, and Yahoo.

Why Scrape Projections?

Scraping projections from multiple sources on the web allows us to automate importing the projections with a simple script.  Automation makes importing more efficient so we don’t have to manually download the projections whenever they’re updated.  Once we import all of the projections, there’s a lot we can do with them, like:

•    Determine who has the most accurate projections
•    Calculate projections for your league
•    Calculate players’ risk levels
•    Calculate players’ value over replacement
•    Identify sleepers
•    Calculate the highest value you should bid on a player in an auction draft
•    Draft the best starting lineup
•    Win your auction draft
•    Win your snake draft

The R Scripts

To scrape the projections from the websites, I use the readHTMLTable function from the XML package in R.  Here’s an example of how to scrape projections from FantasyPros:

1 2 3 4 5 6 7 8    

#Load libraries

library("XML")

#Download fantasy football projections from FantasyPros.com

qb_fp <- readHTMLTable("http://www.fantasypros.com/nfl/projections/qb.php", stringsAsFactors = FALSE)$data

rb_fp <- readHTMLTable("http://www.fantasypros.com/nfl/projections/rb.php", stringsAsFactors = FALSE)$data

wr_fp <- readHTMLTable("http://www.fantasypros.com/nfl/projections/wr.php", stringsAsFactors = FALSE)$data

te_fp <- readHTMLTable("http://www.fantasypros.com/nfl/projections/te.php", stringsAsFactors = FALSE)$data

view raw FantasyPros projections hosted with ❤ by GitHub

The R Scripts for scraping the different sources are located below:

1.    Accuscore
2.    CBS - Jamey Eisenberg
3.    CBS – Dave Richard
4.    CBS – Average
5.    ESPN
6.    Fantasy Football Nerd
7.    FantasyPros
8.    FantasySharks
9.    FFtoday
10.    Footballguys – David Dodds
11.    Footballguys – Bob Henry
12.    Footballguys – Maurile Tremblay
13.    Footballguys – Jason Wood
14.    FOX Sports
15.    NFL.com
16.    WalterFootball
17.    Yahoo

Density Plot

Below is a density plot of the projections from the different sources:Calculate projections

Conclusion

Scraping projections from the web is fast, easy, and automated with R.  Once you’ve downloaded the projections, there’s so much you can do with the data to help you win your league!  Let me know in the comments if there are other sources you want included (please provide a link).

Source:http://fantasyfootballanalytics.net/2014/06/scraping-fantasy-football-projections.html

Extractions and Skin Care

As an esthetician or skin care professional, you may have heard some controversy over the matter of performing extractions during a routine facial service. What may seem like a relatively simple procedure can actually raise great controversy in the world of esthetics. Some estheticians regard extractions as a matter of providing a complete service while others see this as inflicting trauma to the skin. Learning more about both sides of the issue can help you as a professional in making an informed decision and explaining the issue to your clients.

What is an extraction?

As a basic review, an extraction is removing impurity (plug of dead skin or oil) from a pore or pimple. It is the removal of both blackheads and whiteheads from the skin. Extractions occur after the skin has been thoroughly cleansed, exfoliated and sometimes steamed to soften the area prior to extraction.

Why Do It?

Extractions are considered a "must" by many estheticians when performing a routine facial because they want to leave their clients skin looking and feeling it's best. When done correctly, a simple extraction should be quick and relatively painless. As a trained esthetician it is important to know if your client has sensitive skin which would make them more prone to the damage that can be caused by extractions.

Why Not?

Extractions should only be performed by a trained esthetician and should not be done in excess. Extractions can cause broken capillaries or sin irritations that can lead to more (not less) breakouts. Extractions can also cause discomfort for your client when done incorrectly so you should seek their permission before performing any type of extraction during their facial. Remember your client has the right to know any product or procedure being performed on their skin and make an informed choice.

Who Decides?

As an esthetician it may be entirely up to you or it may be a procedure within your salon to do or not do extractions. It is important to check the guidelines of your employer and know their policies before performing any procedure. Remember to explain extractions and their benefits and possible complications to your client. Trust is an important part of any relationship and your client needs to know you are being open and honest with them. The last thing you want as a professional is a reputation for inflicting unnecessary and unwanted procedures or damage to your client's skin.

Bellanina Institute's owner and director, Nina Howard, is a multi-talented, forward-thinking entrepreneur who has built the Bellanina brand form the ground up to a successful million-dollar spa, spa training business, and skin care product line. Nina is a Licensed Esthetician with Para-Medical studies, Massage Therapist, Polarity Therapist, Skin Care Educator, Artist, and Professional Interior Designer.

Source:http://ezinearticles.com/?Extractions-and-Skin-Care&id=5271715

Data Mining - Techniques and Process of Data Mining

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

Recognized in Business

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

Data Collection

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

Algorithm selection

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

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

Classification Technique

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

Our best wishes are with you for your endeavors.

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

RAM Scraping a New Old Favorite For Hackers

Some of the best stories involve a conflict with an old enemy: a friend-turned-foe, long thought dead, returning from the grave for violent retribution; an ancient order of dark siders from the distant reaches of the galaxy, hiding in plain sight and waiting to seize power for themselves; a dark lord thought destroyed millennia ago, only to rise again and seek his favorite piece of jewelry.  The list goes on.

Granted, 2011 isn’t quite “millennia,” and this story isn’t meant for entertainment, but the old foe in this instance is nonetheless dangerous in its own right.  That is the year when RAM scraping malware first made major headlines: originating as an advanced version of the Trackr malware, controlled through a botnet, it was discovered in the compromised Point of Sale (POS) systems of a university and several hotels.  And while it seemed recently that this method had dwindled in popularity, the Target and other retail breaches saw it return with a vengeance.  With 110 million Target customers having their information compromised, it was easily one the largest incidents involving memory scrapers.

How does it work?  First, the malware has to be introduced into the POS network, which can happen via any machine that is connected to the network, or unsecured wireless networks.  Even with firewalls, an infected laptop could serve as a vector.  Once installed, the malware can hide in the shadows, employing encryption or antivirus-avoiding tools to prevent its identification until it’s ready to strike.  Then, when a customer’s card gets used at a POS machine, the data contained within—name, card number, security code, etc.—gets sent to the system memory.  “There is that opportunity to steal the credit card information when it is in memory, perhaps even before your payment has even been authorized, and the data hasn't even been written to the hard drive yet,” says security researcher Graham Cluley.

So, why not encrypt the system’s memory, when it’s at its most vulnerable?  Not that simple, sadly: “No matter how strong your encryption is, if the system needs to process data or process the code, everything needs to be decrypted in memory,” Chris Elisan, principal malware scientist at security firm RSA, explained to Dark Reading.

There are certain steps a company can take, of course, and should take, to reduce the risk.  Strong passwords to access the POS machines, firewalls to isolate the POS network from the Internet, disabling remote access to POS systems, to name a few.  All the same, while these measures are vital and should be used, I don’t think, in light of recent breaches, they are sufficient.  Now, I wrote a short time ago about the impending October 2014 deadline imposed by the credit card industry, regarding the systematic switch to chipped credit card technology; adopting this standard will definitely assist in eradicating this problem.  But, until such a time when a widespread implementation of new systems comes about, always be vigilant to protect your data from attack, because what’s old is new again, and a colossal data breach is a story consumers are liable to seek financial restitution for.

Source:http://www.netlib.com/blog/application-security/RAM-Scraping-a-New-Old-Favorite-For-Hackers.asp

Local ScraperWiki Library

It quite annoyed me that you can only use the scraperwiki library on a ScraperWiki instance; most of it could work fine elsewhere. So I’ve pulled it out (well, for Python at least) so you can use it offline.

How to use
pip install scraperwiki_local
A dump truck dumping its payload

You can then import scraperwiki in scripts run on your local computer. The scraperwiki.sqlite component is powered by DumpTruck, which you can optionally install independently of scraperwiki_local.

pip install dumptruck
Differences

DumpTruck works a bit differently from (and better than) the hosted ScraperWiki library, but the change shouldn’t break much existing code. To give you an idea of the ways they differ, here are two examples:

Complex cell values
What happens if you do this?
import scraperwiki
shopping_list = ['carrots', 'orange juice', 'chainsaw']
scraperwiki.sqlite.save([], {'shopping_list': shopping_list})
On a ScraperWiki server, shopping_list is converted to its unicode representation, which looks like this:
[u'carrots', u'orange juice', u'chainsaw']
In the local version, it is encoded to JSON, so it looks like this:
["carrots","orange juice","chainsaw"]


And if it can’t be encoded to JSON, you get an error. And when you retrieve it, it comes back as a list rather than as a string.

Case-insensitive column names
SQL is less sensitive to case than Python. The following code works fine in both versions of the library.

In [1]: shopping_list = ['carrots', 'orange juice', 'chainsaw']
In [2]: scraperwiki.sqlite.save([], {'shopping_list': shopping_list})
In [3]: scraperwiki.sqlite.save([], {'sHOpPiNg_liST': shopping_list})
In [4]: scraperwiki.sqlite.select('* from swdata')

Out[4]: [{u'shopping_list': [u'carrots', u'orange juice', u'chainsaw']}, {u'shopping_list': [u'carrots', u'orange juice', u'chainsaw']}]

Note that the key in the returned data is ‘shopping_list’ and not ‘sHOpPiNg_liST’; the database uses the first one that was sent. Now let’s retrieve the individual cell values.

In [5]: data = scraperwiki.sqlite.select('* from swdata')
In [6]: print([row['shopping_list'] for row in data])
Out[6]: [[u'carrots', u'orange juice', u'chainsaw'], [u'carrots', u'orange juice', u'chainsaw']]

The code above works in both versions of the library, but the code below only works in the local version; it raises a KeyError on the hosted version.

In [7]: print(data[0]['Shopping_List'])
Out[7]: [u'carrots', u'orange juice', u'chainsaw']

Here’s why. In the hosted version, scraperwiki.sqlite.select returns a list of ordinary dictionaries. In the local version, scraperwiki.sqlite.select returns a list of special dictionaries that have case-insensitive keys.

Develop locally

Here’s a start at developing ScraperWiki scripts locally, with whatever coding environment you are used to. For a lot of things, the local library will do the same thing as the hosted. For another lot of things, there will be differences and the differences won’t matter.

If you want to develop locally (just Python for now), you can use the local library and then move your script to a ScraperWiki script when you’ve finished developing it (perhaps using Thom Neale’s ScraperWiki scraper). Or you could just run it somewhere else, like your own computer or web server. Enjoy!

Source:https://blog.scraperwiki.com/2012/06/local-scraperwiki-library/

Content Scraping Reuses Blog Posts without Permission

What do popular blogs and websites such as Social Media Examiner, Copy Blogger, CNN.com, Mashable, and Type A Parent have in common? No, it’s not traffic and a loyal online community, each was a victim of the content scraping site “BuzzMyFx.” Although most bloggers fall victim to content scrapers at least once, the offending website was such an extreme case the backlash against it was fast and furious. Thanks to the quick action of many angry bloggers, BuzzMyFix was taken down in a matter of days.

If you’re not familiar with content scraping sites and aren’t sure why they’re bad and what you can do if you fall prey, read on. Not knowing what steps you can take to remove your content from a scraping site can mean someone else is profiting from your hard work.

What is content scraping?

Content scraping is when a blog or website pulls in other bloggers’ content without permission, in many cases passing it off as their own. Instead of stocking their sites with unique content, they steal entire blog posts. Some do leave the original authors’ bylines, but there are plenty that don’t provide attribution at all. This is not a good thing at all.

If you don’t care about someone taking your content and putting it on their blogs and websites without your permission, you should. These sites are stealing traffic, search engine rankings, and even advertising revenue from bloggers. Moreover, by ignoring scraping sites you’re giving the message that this practice is OK.

It’s not OK.

How was BuzzMyFx different?
BuzzMyFx was a little different from your usual scrapers. Bloggers didn’t just find their content had been posted on this site, they learned their entire blogs — down to the design and comments — had been cloned. Plus, any bloggers checking to see if their blogs were being cloned immediately found themselves being scraped as well. Dozens, if not hundreds of blogs were affected. However, bloggers didn’t take this incident sitting down. They spread the word and contacted the site’s host en masse. Thanks to their swift action, and the high number of complaints, the site was removed quickly.

How can I tell if my content is being scraped?
Fortunately for content creators, scrapers are a lazy bunch. Because their sites are automated, and they don’t check or read the content being pulled, they don’t take many precautions to ensure the people they scrape from don’t find their sites. In fact, they may not even care. Fortunately, this makes it easy to learn if your content is being stolen.

    Link to your own articles — When you write a blog post and link to other (of your own) blog posts within that post, it’s not only good SEO. You also will get pingbacks whenever someone else steals your content because of your interlinks. You’re alerted when someone links to your content, and when content is published with your links, you’ll get that alert.

    Google Alerts — If your name, blog’s name, or other unique keywords are set up as Google Alerts, you’ll receive an e-mail every time content is published with these keywords.

    Analytics — When people click on your links that are in scraped content, it will show up as referring traffic in your analytics program. You should always check referring traffic so you can thank the referring site owner, but also to make sure no one is stealing your content.

What steps can I take to remove my content from a scraper?
If you find your content is being stolen, know you have several options. First, you’ll need to find out who owns the scraping site. You can find this out by doing a WHOis domain lookup, which will enable you to search for the website’s details, including the name of the webmaster, contact info, and the name of the site’s host.

Keep in mind that sometimes the website’s owner will pay extra to have his or her name kept private, but you will always be able to find the name of the host. Once you have this information, you can take the necessary steps to have your content removed.

    Contact the site’s owner personally: Your first step should always be a polite request to remove your content immediately. Let the website owner know he or she is in violation of the Digital Millennium Copyright Act (DMCA), and you will take the necessary steps to report him if he doesn’t comply.

    Contact the site’s host: If you can’t find the name of the person who owns the site, or if he won’t comply with your takedown request, contact the website’s host. You’ll have to prove your content is being stolen. As the host can be held liable for allowing the content theft, it’s in their best interest to contact the website owner and request removal.

    Contact Google: You can contact Google and fill out a form to have them remove the website from their search engines.

    Spread the word: Let all your blogging friends know about content scrapers when you come across them. The more people who take action against content scrapers, the less likely they are to do it again.

Contacting the webmaster with a takedown notice doesn’t have to be an intimidating process, either. The website Plagiarism Today has a wonderful set of stock letters to use to contact webmasters, web hosts, and even Google. All you have to do is insert the necessary information.

Content scrapers and cloners may try to steal your content, but you don’t have to let them. Stand up for what’s yours.

Source: http://www.dummies.com/how-to/content/content-scraping-reuses-blog-posts-without-permiss.html

Scraping and Analyzing Angel List Syndicates: Kimono Labs + Silk

Because we use Silk to tell stories and visualize data, we are always looking for interesting ways to pull data into a Silk. Right now that means getting data into the CSV format. Fortunately, a wave of new and powerful visual webscraping tools and services have emerged. These make it very simple for anyone (no technical skills required) to scrape data from a website and export that data into a CSV which we can quickly upload into a Silk.

Cool New Scraping Tools
One of the tools we love in this new space is Kimono Labs. Backed by Y Combinator, Kimono combines a visual scraping editor with the ability to do very granular code-inspector level editing to scraping paths. Saved scrapes can be turned into APIs and exported as JSON. Kimono also lets you save time-series versioning of scrapes.

Data from angel-list-syndicates.silk.co

Like many startups, we watch the goings on at AngelList very closely. Syndicates are of particular interest. Basically, these are DIY venture capital pools that allow a qualified investor to serve as a syndicate leader and aggregate small investments from other qualified investors who are members of AngelList. The idea of the syndicates is to democratize the VC process and make it easier and less risky for individuals to participate.

We used Kimono to scrape information on the Top 25 Syndicates ranked by dollars backing each round. Kimono makes it very easy to visually designate which parts of a page to scrape and how many rows there are on a page. (Here you can see me highlighting the minimum dollar investment). We downloaded the information as a CSV and did a quick scrub to get it ready for upload to Silk. The process took no more than 15 minutes.

We could tell by eyeballing the numbers beforehand that a serious Power Law was in effect. And the actual data analysis on Silk bore this out. We chose to use a pie chart to show distribution. Three syndicates control nearly two-thirds of all the committed capital by Angel.co members in the syndicate program. One of the top three - Tim Ferriss - has no background as a venture capitalist or building technology companies but is rapidly becoming a force in startup investing. The person with the largest committed syndicate pool, Gil Penachina, is someone who is a quiet mover and shaker in Silicon Valley but he clearly packs a huge punch.

The largest syndicate in terms of likely commitments of deals per year is Foundry Group Angels, a group led by Brad Feld (@bfeld). While they put in less per deal, they are planning to back over 50 deals per year - a huge number. Trailing far behind those three was media impresario and Launch conference mogul Jason Calacanis, who is one of the most visible people in the startup space.

Source: http://blog.silk.co/post/83501793279/scraping-and-analyzing-angel-list-syndicates

What you have to know before requesting web scraping services?

Before you request web scraping services you have to know what are your needs (what data you need, structure of it and where you can find this data).

Step 1: Define what data you need?

Data needs depending on purpose, if you want to find new customers you probably need contact data from players in your industry. Also if you want to study your competitors you need to define who are they. Only after that you can select data sources (websites feeds or other electronic sources) for this extraction.

In many cases for discovering and defining data sources are used search engines like Google, Bing, Yahoo, and others.

Step 2: Structure of data

Data structure it’s directly linked to usage purpose. In many cases data structure it’s a table where a row represents an entity and a cell of this row represents a property of this entity. In other cases Data structure is a a chart or another graphic representation builder with data extracted from a web source.

Step 3: Number of data extraction

In many cases is needed one time data extraction. In other cases when you need a regular report, are needed periodically extractions.

If you have defined all of above points you are ready to request a quote and an amount estimation from this contact form.

Source: http://thewebminer.com/blog/2013/08/