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

So What Exactly Is A Private Data Scraping Services To Use You?

If your computer connects to the Internet or resources on the request for this information, and queries to different servers. If you have a website to introduce to the site server recognizes your computer's IP address and displays the data and much more. Many e - commerce sites use to log your IP address, and the browsing patterns for marketing purposes.

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The  Scraping server you connect to your destination or to process your information and make a filter. For example, IP address or protocol filtering traffic through a  Scraping service. As you might guess, there are many types of  Scraping services. including the ability to a high demand for the software. Email messages are quickly sent to businesses and companies to help you search for contacts.

Although there are Sanding free  Scraping IP addresses in this way can work, the use of payment services, and automatic user interface (plug and play) are easy to give.  Scraping web information services, thus offering a variety of relevant sources of data.  Scraping information service organizations are generally used where large amounts of data every day. It is possible for you to receive efficient, high precision is also affordable.

Information on the various strategies that companies,  Scraping excellent information services, and use the structure planned out and has led to the introduction of more rapid relief of the Earth.

In addition, the application software that has flexibility as a priority. In addition, there is a software that can be tailored to the needs of customers, and satisfy various customer requirements play a major role. Particular software, allows businesses to sell, a customer provides the features necessary to provide the best experience.

If you do not use a private Data Scraping Services suggest that you immediately start your Internet marketing. It is an inexpensive but vital to your marketing company. To choose how to set up a private  Scraping service, visit my blog for more information. Data Scraping Services software as the activity data and provides a large amount of information, Sorting. In this way, the company reduced the cost and time savings and greater return on investment will be a concept.

Without the steady stream of data from these sites to get stopped? Scraping HTML page requests sent by argument on the web server, depending on changes in production, it is very likely to break their staff. 

Data Scraping Services is common in the respective outsourcing company. Many companies outsource  Data Scraping Services service companies are increasingly outsourcing these services, and generally dealing with the Internet business-related activities, in particular a lot of money, can earn.

Web  Data Scraping Services, pull information from a structured plan format. Informal or semi-structured data source from the source.They are there to just work on your own server to extract data to execute. IP blocking is not a problem for them when they switch servers in minutes and back on track, scraping exercise. Try this service and you'll see what I mean.

It is an inexpensive but vital to your marketing company. To choose how to set up a private  Scraping service, visit my blog for more information. Data Scraping Services software as the activity data and provides a large amount of information, Sorting. In this way, the company reduced the cost and time savings and greater return on investment will be a concept.

Source:http://www.articlesbase.com/outsourcing-articles/so-what-exactly-is-a-private-data-scraping-services-to-use-you-5587140.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

GScholarXScraper: Hacking the GScholarScraper function with XPath

Kay Cichini recently wrote a word-cloud R function called GScholarScraper on his blog which when given a search string will scrape the associated search results returned by Google Scholar, across pages, and then produce a word-cloud visualisation.

This was of interest to me because around the same time I posted an independent Google Scholar scraper function  get_google_scholar_df() which does a similar job of the scraping part of Kay’s function using XPath (whereas he had used Regular Expressions). My function worked as follows: when given a Google Scholar URL it will extract as much information as it can from each search result on the URL webpage  into different columns of a dataframe structure.

In the comments of his blog post I figured it’d be fun to hack his function to provide an XPath alternative, GScholarXScraper. Essensially it’s still the same function he wrote and therefore full credit should go to Kay on this one as he fully deserves it – I certainly had no previous idea how to make a word cloud, plus I hadn’t used the tm package in ages (to the point where I’d forgotten most of it!). The main changes I made were as follows:

    Restructure internal code of GScholarScraper into a series of local functions which each do a seperate job (this made it easier for me to hack because I understood what was doing what and why).

    As far as possible, strip out Regular Expressions and replace with XPath alternatives (made possible via the XML package). Hence the change of name to GScholarXScraper. Basically, apart from a little messing about with the generation of the URLs I just copied over my get_google_scholar_df() function and removed the Regular Expression alternatives. I’m not saying one is better than the other but f0r me personally, I find XPath shorter and quicker to code but either is a good approach for web scraping like this (note to self: I really need to lean more about regular expressions!) :)

•    Vectorise a few of the loops I saw (it surprises me how second nature this has become to me – I used to find the *apply family of functions rather confusing but thankfully not so much any more!).
•    Make use of getURL from the RCurl package (I was getting some mutibyte string problems originally when using readLines but this approach automatically fixed it for me).
•    Add option to make a word-cloud from either the “title” or the “description” fields of the Google Scholar search results
•    Added steaming via the Rstem package because I couldn’t get the Snowball package to install with my version of java. This was important to me because I was getting word clouds with variations of the same word on it e.g. “game”, “games”, “gaming”.
•    Forced use of URLencode() on generation of URLs to automatically avoid problems with search terms like “Baldur’s Gate” which would otherwise fail.

I think that’s pretty much everything I added. Anyway, here’s how it works (link to full code at end of post):

</pre>
<div id="LC198"># #EXAMPLE 1: Display word cloud based on the title field of each Google Scholar search result returned</div>
<div id="LC199"># GScholarXScraper(search.str = "Baldur's Gate", field = "title", write.table = FALSE, stem = TRUE)</div>
<div id="LC200">#</div>
<div id="LC201"># # word freq</div>
<div id="LC202"># # game game 71</div>
<div id="LC203"># # comput comput 22</div>
<div id="LC204"># # video video 13</div>
<div id="LC205"># # learn learn 11</div>
<div id="LC206"># # [TRUNC...]</div>
<div id="LC207"># #</div>
<div id="LC208"># #</div>
<div id="LC209"># # Number of titles submitted = 210</div>
<div id="LC210"># #</div>
<div id="LC211"># # Number of results as retrieved from first webpage = 267</div>
<div id="LC212"># #</div>
<div id="LC213"># # Be aware that sometimes titles in Google Scholar outputs are truncated - that is why, i.e., some mandatory intitle-search strings may not be contained in all titles</div>

<pre>

// image

I think that’s kind of cool and corresponds to what I would expect for a search about the legendary Baldur’s Gate computer role playing game :)  The following is produced if we look at the ‘description’ filed instead of the ‘title’ field:

</pre>

<div id="LC215"># # EXAMPLE 2: Display word cloud based on the description field of each Google Scholar search result returned</div>
<div id="LC216">GScholarXScraper(search.str = "Baldur's Gate", field = "description", write.table = FALSE, stem = TRUE)</div>
<div id="LC217">#</div>
<div id="LC218"># # word freq</div>
<div id="LC219"># # page page 147</div>
<div id="LC220"># # gate gate 132</div>
<div id="LC221"># # game game 130</div>
<div id="LC222"># # baldur baldur 129</div>
<div id="LC223"># # roleplay roleplay 21</div>
<div id="LC224"># # [TRUNC...]</div>
<div id="LC225"># #</div>
<div id="LC226"># # Number of titles submitted = 210</div>
<div id="LC227"># #</div>
<div id="LC228"># # Number of results as retrieved from first webpage = 267</div>
<div id="LC229"># #</div>
<div id="LC230"># # Be aware that sometimes titles in Google Scholar outputs are truncated - that is why, i.e., some mandatory intitle-search strings may not be contained in all titles</div>
<pre>

//image

Not bad. I could see myself using the text mining and word cloud functionality with other projects I’ve been playing with such as Facebook, Google+, Yahoo search pages, Google search pages, Bing search pages… could be fun!

Many thanks again to Kay for making his code publicly available so that I could play with it and improve my programming skill set.

Code:

Full code for GScholarXScraper can be found here: https://github.com/tonybreyal/Blog-Reference-Functions/blob/master/R/GScholarXScraper/GScholarXScraper

Original GSchloarScraper code is here: https://docs.google.com/document/d/1w_7niLqTUT0hmLxMfPEB7pGiA6MXoZBy6qPsKsEe_O0/edit?hl=en_US

Full code for just the XPath scraping function is here: https://github.com/tonybreyal/Blog-Reference-Functions/blob/master/R/googleScholarXScraper/googleScholarXScraper.R

Source:http://www.r-bloggers.com/gscholarxscraper-hacking-the-gscholarscraper-function-with-xpath/

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

Benefits of Predictive Analytics and Data Mining Services

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

Predictive analytics can be helpful in answering questions like:

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

Benefits of Data mining include:

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

Basic steps of Predictive Analytics are as follows:

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

Standard techniques used for it are:

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

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

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

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/

A quick guide on web scraping: Why and how

Web scraping, which is the collection and cleaning of online data, is the first step in any
data-driven project. Here’s a short video that explains what scraping is, and how to create
automated scraping jobs using a digital tool.

This is a 15-minute video created by an instructor at Ohio State University. In the first six
minutes, the instructor talks about why we need web scraping; he then shows how to use a
scraping tool, OutWit Hub, to collect data scattered in a large database.

FYI: read reviews by Reporters’ Lab of OutWit Hub and other web scraping tools.

Source: http://www.mulinblog.com/quick-guide-web-scraping/

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

Web Scraping’s 2013 Review – part 1

Here we are, almost having ended another year and having the chance to analyze the aspects of the Web scraping market over the last twelve months. First of all i want to underline all the buzzwords on the tech field as published in the Yahoo’s year in review article . According to Yahoo, the most searched items wore

    iPhone (including 4, 5, 5s, 5c, and 6)
    Samsung (including Galaxy, S4, S3, Note)
    Siri
    iPad Cases
    Snapchat
    Google Glass
    Apple iPad
    BlackBerry Z10
    Cloud Computing

It’s easy to see that none of this terms regards in any way with the field of data mining, and they rather focus on the gadgets and apps industry, which is just one of the ways technology can evolve to. Regarding actual data mining industry there were a lot of talks about it in this year’s MIT’s Engaging Data 2013 Conference. One of the speakers Noam Chomsky gave an acid speech relating data extraction and its connection to the Big Data phenomena that is also on everyone’s lips this year. He defined a good way to see if Big Data works by following a series of few simple factors: 1. It’s the analysis, not the raw data, that counts. 2. A picture is worth a thousand words 3. Make a big data portal (Not sure if Facebook is planning on dominating in cloud services some day) 4. Use a hybrid organizational model (We’re asleep already, soon)  let’s move 5. Train employees Other interesting declaration  was given by EETimes saying, “Data science will do more for medicine in the next 10 years than biological science.” which says a lot about the volume of required extracted data.

Because we want to cover as many as possible events about data mining this article will be a two parter, so don’t forget to check our blog tomorrow when the second part of this article will come up!

Source:http://thewebminer.com/blog/2013/12/