Data Extraction, Web Screen Scraping Tool, Mozenda Scraper

Web Scraping

Web scraping, also known as Web data extraction or Web harvesting, is a software method of extracting data from websites. Web scraping is closely related and similar to Web indexing, which indexes Web content. Web indexing is the method used by most search engines. The difference with Web scraping is that it focuses more on the translation of unstructured content on the Web, characteristically in rich text format like that of HTML, into controlled data that can be analyzed stored and in a spreadsheet or database. Web scraping also makes Web browsing more efficient and productive for users. For example, Web scraping automates weather data monitoring, online price comparison, and website change recognition and data integration. 

This clever method that uses specially coded software programs is also used by public agencies. Government operations and Law enforcement authorities use data scrape methods to develop information files useful against crime and evaluation of criminal behaviors. Medical industry researchers get the benefit and use of Web scraping to gather up data and analyze statistics concerning diseases such as AIDS and the most recent strain of influenza like the recent swine flu H1N1 epidemic.

Data scraping is an automatic task performed by a software program that extracts data output from another program, one that is more individual friendly. Data scraping is a helpful device for programmers who have to generate a line through a legacy system when it is no longer reachable with up to date hardware. The data generated with the use of data scraping takes information from something that was planned for use by an end user.

One of the top providers of Web Scraping software, Mozenda, is a Software as a Service company that provides many kinds of users the ability to affordably and simply extract and administer web data. Using Mozenda, individuals will be able to set up agents that regularly extract data then store this data and finally publish the data to numerous locations. Once data is in the Mozenda system, individuals may format and repurpose data and use it in other applications or just use it as intelligence. All data in the Mozenda system is safe and sound and is hosted in a class A data warehouses and may be accessed by users over the internet safely through the Mozenda Web Console.

One other comparative software is called the Djuggler. The Djuggler is used for creating web scrapers and harvesting competitive intelligence and marketing data sought out on the web. With Dijuggles, scripts from a Web scraper may be stored in a format ready for quick use. The adaptable actions supported by the Djuggler software allows for data extraction from all kinds of webpages including dynamic AJAX, pages tucked behind a login, complicated unstructured HTML pages, and much more. This software can also export the information to a variety of formats including Excel and other database programs.

Web scraping software is a ground-breaking device that makes gathering a large amount of information fairly trouble free. The program has many implications for any person or companies who have the need to search for comparable information from a variety of places on the web and place the data into a usable context. This method of finding widespread data in a short amount of time is relatively easy and very cost effective. Web scraping software is used every day for business applications, in the medical industry, for meteorology purposes, law enforcement, and government agencies.

Source:http://www.articlesbase.com/databases-articles/data-extraction-web-screen-scraping-tool-mozenda-scraper-3568330.html

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

What Kind of Legal Problems Can Web Scraping Cause

Web scraping software is readily available and has been used by many for legitimate purposes. It has also been used for illegal purposes. A website that engages in this practice should know the legal dangers of the activity.

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General Knowledge- VII

The idea of web scraping is not new. Search engines have used this type of software to determine which results appear when someone conducts a search. They use special software software to extract data from a website and this data is then used to calculate the rankings of the website. Websites work very hard to improve their ranking and their chance of being found by anyone making a search. This use of this practice is understood and is considered to be a legitimate use for the software. However, there are services that provide web scraping and screen scraping prevention services and help the webmaster to remain safe from the attack of bad bots.

The problem with duplicacy is that it is often used for less than legitimate reasons. Since the software responsible can collect all sorts of data from websites and store the information that is collected, it represents a danger to anyone who might be affected by it. The information that can be collected can be used for many practices that are not so legitimate and may even be illegal. Anyone who is involved in this practice of content duplicacy should be aware of the legal issues implicated with this practice. It may be wise for anyone who has a website to find ways to prevent a site from being scraped or to use professional services to block site scraping.

Legal problems

The first thing to worry about, if you have a website or are using web scraping software, is when you might run into legal problems. Some of the issues that web scraping can cause include:

•    Access. If the software is used to access sites it does not have the right to access and takes information that it is not entitled to, the owner of the web scarping software may find themselves in legal trouble.

•    Re-use. The software can collect and reuse information. If that information is copyrighted, that might be a legal problem. Any information that is reused without permission may create legal issues for anyone who uses it.

•    Robots. Some states have enacted laws that are designed to keep people from using scraping robots. These automatically search out information on websites and using them may be illegal in some states. It is up to the user of the web scraping software to comply with any laws in the state in which they are operating.

Who is Responsible

The laws and regulations surrounding this practice are not always clear. There are many grey areas that allow this practice to occur. The question is, who is responsible for determining whether the use of web scraping software is legal?

Websites collect the information, but they may not be the entity using the web scraping software. If they are using this type of software, it is not always enough to inform the website's visitors that this practice is occurring. Putting this information into the user agreement may or may not protect the website from legal problems.

It is also partly the responsibility of a site owner to prevent a site from being scraped. There is software that can be used that will do this for a website and will keep any information that is collected safe and secure. A website may or may not be held legally responsible for any web scraper that is able to collect information they have. It will depend on why the data was collected, how it was used, who collected it, and whether precautions were taken.

What to expect

The issue of content copying and the legal issues surrounding it will continue to evolve. As more courts take on this issue, the lines between legal and illegal web scraping will become clearer. Many of the cases that have been brought to court have occurred in civil court, although there are some that have been taken up in a criminal court. There will be times when such practice may actually be a felony.

Before you use spying software, you need to realize that the laws surrounding its use are not clear. If you operate a website, you need to know the legal issues that you may face if scraping software is used on your website. The best step is to use the software available to protect your website and stop web scraping and be honest on your site if web scraping is used.

Source: http://www.articlesbase.com/technology-articles/what-kind-of-legal-problems-can-web-scraping-cause-6780486.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