Important Aspects Of Web Data Scraping

Have you ever heard of "data scraping?" Scraping Data scraping technology to new technology and a successful businessman who made his fortune by making use of the data.

Sometimes website owners automated harvesting of your data can not be happy. Webmasters tools or methods that the content of websites to find block certain IP addresses from using their websites to disallow web scrapers have learned.  Allen are ultimately left with is blocked.

Venus is a modern solution to the problem. Proxy data scraping technology solves the problem by using proxy IP addresses. Every time your data scraping program performs an output of a website, the website thinks that it comes from a different IP address. The owner of this website, the proxy data scraping only a short period of increased traffic from all over the world looks like. They are very limited and boring ways of blocking such a script, but more importantly - most of the time, but they will not know they are scraped.

Now you might be asking yourself, "I can get for my project where data scraping proxy technology?" "Do it yourself" solution, but unfortunately, not. Need to mention. The proxy server you choose to rent consider hosting providers, but that option is fairly pricey, but definitely better than the alternative is incredibly dangerous (but) free public proxy servers.

But the trick is finding them. Many sites list hundreds of servers, but one that works to identify, access, and supports the type of protocol you need perseverance, trial and error, a lesson. Ten first, you do not know which server belongs to or what activities going on a server somewhere. Through a public proxy sensitive requests or to send data is a bad idea.

Proxy data scraping for a less risky scenario is to rent a rotating proxy connection along a large number of private IP addresses. www.webdatascraping.us companies scale anonymous proxy solutions, but often have a fairly hefty setup costs to get you going.

After performing a simple Google search, I quickly scrape using anonymous data for a company that has access to the proxy server biedt.kon finish.

Different techniques and processes for collecting and analyzing data, and has developed over time. Web scraping for business on the market recently. It is a process from various sources, such as databases and web sites with large amounts of data provides.

It's good to clear the air and people know that the data is the legal process to scrape. In this case, the main reason is because the information or data that is already available on the internet. It is important to know that this is a process to steal information, but there is a process of gathering reliable information. Most people considered unsavory behavior techniques.

So we collect data from a variety of websites and databases, web scraping define a process. A process either manually or through the use of software that can be achieved. Data mining companies to web-extraction and web crawling process to increase has led to greater use. The other important task of such enterprises for processing and analyzing the data are harvested. One of the important aspects about these companies is that they are experts in service.

Source:http://www.articlesbase.com/outsourcing-articles/important-aspects-of-web-data-scraping-6160374.html

How To Access Information About PDF Data Scraping?

Scraping a way that the output of data from another program to extract data is used by a computer program can be heard. Simply put, this is a process of automatically sorting the information from the Internet, even within an HTML file can be found in various sources, including PDF documents and others. There is also a collection of relevant information. This information to the database or spreadsheet, allowing users to retrieve them later will be included.

Most websites today can be viewed and written text in the source code is simple. However, there are other companies that currently use Adobe PDF or Portable Document Format to choose from. This file is a type known as just the free Adobe Acrobat to be viewed using the software. Supports virtually all operating software, said. There are many advantages when you choose to create PDF files. Those document you just the same, even if you put it in another computer, so you can see it look. Therefore, business documents or completes the data sheet. Of course there are drawbacks. One of these is included in the text is converted into an image. In this case, it is often the problem with this is that when it comes to copy and paste, and could be.

That's why some are starting to scrape the information PDF. It is often said that the only scraping process information in your PDF file PDF is like to get data. PDF to start scraping the information from you, choose a device specially designed for this process must benefit. However, you feel that you have the right tools too effectively scrape PDF will be able to perform is not easy to detect. This is because the equipment is exactly the same data access without having personal problems.

However, if you look good, you look at programs that you may encounter. You have to know programming; you do not need to use them. You can easily specify their preferences for the software you use will do the rest. There are companies out there that you contact them and they work because they have the right tools they can use to be. If you choose to do things yourself, you will find it really difficult and complicated compared to professionals working for you, they will at no time possible. PDF scraping of information is a process whereby information can be found on the Internet and not copyright infringement to collect.

Well I hope you now understand how to scrape data in various forms. If you do not understand then go for one of the sites I mention below in the box of the author. We offer a variety of data services, such as HTML scraping services, the crop Scraping Web Services Web Content, Email Id scraping, scraping data ownership, data Linkedin scraping, scraping data Hotels, pharmaceutical Scraping data, Business Contact Scraping, Data Scraping For University etc. If you have any doubts, please feel free to ask us without hesitation. We will certainly be useful for you. Thank you.

Source:http://www.articlesbase.com/outsourcing-articles/how-to-access-information-about-pdf-data-scraping-5293692.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

Data Mining Explained

Overview

Data mining is the crucial process of extracting implicit and possibly useful information from data. It uses analytical and visualization techniques to explore and present information in a format which is easily understandable by humans.

Data mining is widely used in a variety of profiling practices, such as fraud detection, marketing research, surveys and scientific discovery.

In this article I will briefly explain some of the fundamentals and its applications in the real world.

Herein I will not discuss related processes of any sorts, including Data Extraction and Data Structuring.

The Effort

Data Mining has found its application in various fields such as financial institutions, health-care & bio-informatics, business intelligence, social networks data research and many more.

Businesses use it to understand consumer behavior, analyze buying patterns of clients and expand its marketing efforts. Banks and financial institutions use it to detect credit card frauds by recognizing the patterns involved in fake transactions.

The Knack

There is definitely a knack to Data Mining, as there is with any other field of web research activities. That is why it is referred as a craft rather than a science. A craft is the skilled practicing of an occupation.

One point I would like to make here is that data mining solutions offers an analytical perspective into the performance of a company depending on the historical data but one need to consider unknown external events and deceitful activities. On the flip side it is more critical especially for Regulatory bodies to forecast such activities in advance and take necessary measures to prevent such events in future.

In Closing

There are many important niches of Web Data Research that this article has not covered. But I hope that this article will provide you a stage to drill down further into this subject, if you want to do so!

Should you have any queries, please feel free to mail me. I would be pleased to answer each of your queries in detail.

Source: http://ezinearticles.com/?Data-Mining-Explained&id=4341782

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

Affordable Tooth Extractions

In recent times, the cost of dental care has skyrocketed. This includes all types of dentistry including teeth cleaning, extractions, and dental surgery. For those who live in Denver, CO, there are many options to choose from when paying for routine or emergency dental care. In fact, having a tooth extraction Denver might just be more easily afforded than what some may be aware of.

The flat fee for a tooth extraction in Denver may vary between dental offices. The type of extraction can also cause a difference in the price. A simple extraction may cost between $60-$75, but a wisdom tooth extraction that requires more time and effort could cost much more.

One of the great aspects of having dental services performed in Denver is the variety of payment forms that many dental offices accept. Most dental offices in this area accept several different health insurance plans that will allow patients to only be required to pay a small copay at the time of service. If you have chosen an in-network dental provider for your plan, this copay can be even less.

Many dental offices also provide services to those who have state medicaid or medicare as well. While cosmetic dental work may not be covered by these forms of health care, extractions are covered because they are considered a necessary part of the patients good health. Yearly checkups and teeth cleanings are also normally covered as a preventative measure to avoid bad dental health.

For those who may not have any type of health insurance, dental insurance, or state provided health care plan, most dental offices will offer a payment plan. The total cost will be calculated and can be divided up over a few months to make dental care more easily affordable. This will need to be arranged before services and you may need to pay a percentage of the cost upfront before any dental work is performed.

So, if you live in the Denver area and need to have a tooth extraction or other dental care, do not fear that it is impossible to obtain. By calling each dental office and discussing the types of payment forms they accept, you may find a payment plan that fits your budget nicely. You can compare the prices and options of all dentists in your area so that you can make a well informed decision more easily.

Source:http://ezinearticles.com/?Affordable-Tooth-Extractions&id=3241427

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

Do blog scraping sites violate the blog owner's copyright?

I noticed that my blog has been posted on one of these website scraping sites. This is the kind of site that has no original content, but just repeats or scrapes content others have written and does it to get some small amount of ad income from ads on the scraping site. In essence the scraping site is taking advantage of the content of the originating site in order to make a few dollars from people who go to the site looking for something else. Some of these websites prey on misspelling. If you accidentally misspell the name of an original site, you just may end up with one of these patently commercial scraping sites.

Google defines scraping as follows:

•    Sites that copy and republish content from other sites without adding any original content or value
•    Sites that copy content from other sites, modify it slightly (for example, by substituting synonyms or using automated techniques), and republish it
•    Sites that reproduce content feeds from other sites without providing some type of unique organization or benefit to the user

My question, as set out in the title to this post, is whether or not scraping is a violation of copyright. It turns out that the answer is likely very complicated.  You have to look at the definition of a scraping site very carefully. Let me give you some hypotheticals to show what I mean.

Let's suppose that I write a blog and put a link in my blog post to your blog. Does that link violate your copyright? I can't imagine that anyone would think that there was problem with linking to another website on the Web. In this case, there is no content from the originating site, just a link.

But let's carry the hypothetical a little further. What if I put a link to your site and quote some of your content? Does this violate copyright law? If you are acquainted with any of the terminology of copyright law; think fair use. The issue here is whether or not the "quoted" material is a substantial reproduction of the entire original content? I would have the opinion that duplicating an entire blog post either with or without attribution would be a violation of the originator's copyright.

So is the scraping website protected by the "fair use" doctrine? Does the fact that the motivation for listing the original websites is to make money have anything to do with how you would decide if there was or was not a violation of the originator's copyright? By the way, the copyright does not make a distinction between a commercial and non-commercial use of the original constituting or not constituting a violation of copyright. The fact that the reproducing (scraping) party does not make money from the reproduction is not a factor in the issue of violation, although it may ultimately be an issue as to the amount of damages assessed.

Does the fact that the actions of the scraper annoy me, make any difference? I would answer, not in the least. Whether or not you are annoyed by the violation of the copyright makes no difference as to whether or not there is a violation. Likewise, you have no independent claims for your wounded feelings because of the copied content. Copyright is a statutory action (i.e. based on statutory law) and unless the cause of action is recognized by the law, there is no cause of action. Now, in an outrageous case, you may have  some kind of tort (personal injury) claim, but that is way outside of my hypothetical situation.

So what is the answer? Does scraping violate the originator's copyright? If only a small portion of the blog is copied (scraped) then I would have to have the opinion that it is not. Essentially, no matter what the motivation of the scrapper, there is not enough content copied to violate the fair use doctrine. Now, that is my opinion. Your's might differ. That is what makes lawsuits.

Do I think there are other reasons why scraping websites are objectionable? Certainly, but those reasons have nothing to do with copyright and they are probably the subject of another different blog post. So, if you are reading this from scraping website, bear in mind that there may be a serious problem with that type of website.

Source:http://genealogysstar.blogspot.in/2013/05/do-blog-scraping-sites-violate-blog.html

Scrape it – Save it – Get it

I imagine I’m talking to a load of developers. Which is odd seeing as I’m not a developer. In fact, I decided to lose my coding virginity by riding the ScraperWiki digger! I’m a journalist interested in data as a beat so all I need to do is scrape. All my programming will be done on ScraperWiki, as such this is the only coding home I know. So if you’re new to ScraperWiki and want to make the site a scraping home-away-from-home, here are the basics for scraping, saving and downloading your data:

With these three simple steps you can take advantage of what ScraperWiki has to offer – writing, running and debugging code in an easy to use editor; collaborative coding with chat and user viewing functions; a dashboard with all your scrapers in one place; examples, cheat sheets and documentation; a huge range of libraries at your disposal; a datastore with API callback; and email alerts to let you know when your scrapers break.

So give it a go and let us know what you think!

Source:https://blog.scraperwiki.com/2011/04/scrape-it-save-it-get-it/

Ethics in data journalism: mass data gathering – scraping, FOI and deception

Mass data gathering – scraping, FOI, deception and harm

The data journalism practice of ‘scraping’ – getting a computer to capture information from online sources – raises some ethical issues around deception and minimisation of harm. Some scrapers, for example, ‘pretend’ to be a particular web browser, or pace their scraping activity more slowly to avoid detection. But the deception is practised on another computer, not a human – so is it deception at all? And if the ‘victim’ is a computer, is there harm?

The tension here is between the ethics of virtue (“I do not deceive”) and teleological ethics (good or bad impact of actions). A scraper might include a small element of deception, but the act of scraping (as distinct from publishing the resulting information) harms no human. Most journalists can live with that.

The exception is where a scraper makes such excessive demands on a site that it impairs that site’s performance (because it is repetitively requesting so many pages in a small space of time). This not only negatively impacts on the experience of users of the site, but consequently the site’s publishers too (in many cases sites will block sources of heavy demand, breaking the scraper anyway).

Although the harm may be justified against a wider ‘public good’, it is unnecessary: a well designed scraper should not make such excessive demands, nor should it draw attention to itself by doing so. The person writing such a scraper should ensure that it does not run more often than is necessary, or that it runs more slowly to spread the demands on the site being scraped. Notably in this regard, ProPublica’s scraping project Upton “helps you be a good citizen [by avoiding] hitting the site you’re scraping with requests that are unnecessary because you’ve already downloaded a certain page” (Merrill, 2013).

Attempts to minimise that load can itself generate ethical concerns. The creator of seminal data journalism projects chicagocrime.org and Everyblock, Adrian Holovaty, addresses some of these in his series on ‘Sane data updates’ and urges being upfront about

    “which parts of the data might be out of date, how often it’s updated, which bits of the data are updated … and any other peculiarities about your process … Any application that repurposes data from another source has an obligation to explain how it gets the data … The more transparent you are about it, the better.” (Holovaty, 2013)

Publishing scraped data in full does raise legal issues around the copyright and database rights surrounding that information. The journalist should decide whether the story can be told accurately without publishing the full data.

Issues raised by scraping can also be applied to analogous methods using simple email technology, such as the mass-generation of Freedom of Information requests. Sending the same FOI request to dozens or hundreds of authorities results in a significant pressure on, and cost to, public authorities, so the public interest of the question must justify that, rather than its value as a story alone. Journalists must also check the information is not accessible through other means before embarking on a mass-email.

Source: http://onlinejournalismblog.com/2013/09/18/ethics-in-data-journalism-mass-data-gathering-scraping-foi-and-deception/

The Hubcast #4: A Guide to Boston, Scraping Local Leads, & Designers.Hubspot.com

The Hubcast Podcast Episode 004

Welcome back to The Hubcast folks! As mentioned last week, this will be a weekly podcast all about HubSpot news, tips, and tricks. Please also note the extensive show notes below including some new HubSpot video tutorials created by George Thomas.

Show Notes:

Inbound 2014

THE INSIDER’S GUIDE TO BOSTON
Boston Guide

On September 15-18, the Boston Convention & Exhibition Center will be filled with sales and marketing professionals for INBOUND 2014. Whether this will be your first time visiting Boston, you’ve visited Boston in the past, or you’ve lived in the city for years, The Insider’s Guide to Boston is your go-to guide for enjoying everything the city has to offer. Click on a persona below to get started.

Are you the The Brewmaster – The Workaholic – The Chillaxer?

Check out the guide here

HubSpot Tips & Tricks

Prospects Tool – Scrape Local Leads
Prospects Tool

This weeks tip / trick is how to silence some of the noise in your prospect tool. Sometimes you might have need to just look at local leads for calls or drop offs. We show you how to do that and much more with the HubSpot Prospects Tool.

Watch the tutorial here

HubSpot Strategy
Crack down on your sites copy.

We talk about how your home page and about pages are talking to your potential customers in all the wrong ways. Are you the me, me, me person at the digital party? Or are you letting people know how their problems can be solved by your products or services.

HubSpot Updates
(Each week on the Hubcast, George and Marcus will be looking at HubSpot’s newest updates to their software. And in this particular episode, we’ll be discussing 2 of their newest updates)
Default Contact Properties

You can now choose a default option on contact properties that sets a default value for that property that can be applied across your entire contacts database. When creating or editing a new contact property in Contacts Settings, you’ll see a new default option next to the labels on properties with field types “Dropdown,” “Radio Select” and “Single On/Off Checkbox”.

Default Contact Properties

When you set a contact property as “default”, all contacts who don’t have any value set for this property will adopt the default value you’ve selected. In the example above, we’re creating a property to track whether your contact uses a new feature. Initially, all of them would be “No,” and that’s the default property that will be applied database-wide. As a result, this’ll get stamped on each contact record the value wasn’t present on.

Now, when you want to apply a contact property across multiple contacts, you don’t have to create a list of those contacts and then create a workflow that stamps that contact property across those contacts. This new feature allows you to bypass those steps by using the “default” option on new contact properties you create.

Watch the tutorial here
RSS Module with Images

Now available is a new option within modules in the template builder that will allow you to easily add a featured image to an RSS module. This module will show a blog post’s featured image next to the feed of recent blog content. If you are a marketer, all you need to do is simply check the “Featured Image” box off in the RSS Listing module to display a list of recent COS blog posts with images on any page. No developers or code necessary to do this!

If you are a designer and want to add additional styling to an RSS module with images, you can do so using HubL tokens.

Here is documentation on how to get started.

Default Contact Properties
Watch the tutorial here

HubSpot Wishlist

 The HubSpot Keywords Tool

Why oh why!!!! Hubspot why can we only have 1,000 keywords in our keywords tool? We talk about how for many companies a 1,000 keywords dont just cut it. For example Yale applaince can easily blow through those keywords.

Source: http://www.thesaleslion.com/hubcast-podcast-004/

Web Scraping’s 2013 Review – part 2

As promised we came back with the second part of this year’s web scraping review. Today we will focus not only on events of 2013 that regarded web scraping but also Big data and what this year meant for this concept.

First of all, we could not talked about the conferences in which data mining was involved without talking about TED conferences. This year the speakers focused on the power of data analysis to help medicine and to prevent possible crises in third world countries. Regarding data mining, everyone agreed that this is one of the best ways to obtain virtual data.

Also a study by MeriTalk  a government IT networking group, ordered by NetApp showed this year that companies are not prepared to receive the informational revolution. The survey found that state and local IT pros are struggling to keep up with data demands. Just 59% of state and local agencies are analyzing the data they collect and less than half are using it to make strategic decisions. State and local agencies estimate that they have just 46% of the data storage and access, 42% of the computing power, and 35% of the personnel they need to successfully leverage large data sets.

Some economists argue that it is often difficult to estimate the true value of new technologies, and that Big Data may already be delivering benefits that are uncounted in official economic statistics. Cat videos and television programs on Hulu, for example, produce pleasure for Web surfers — so shouldn’t economists find a way to value such intangible activity, whether or not it moves the needle of the gross domestic product?

We will end this article with some numbers about the sumptuous growth of data available on the internet.  There were 30 billion gigabytes of video, e-mails, Web transactions and business-to-business analytics in 2005. The total is expected to reach more than 20 times that figure in 2013, with off-the-charts increases to follow in the years ahead, according to researches conducted by Cisco, so as you can see we have good premises to believe that 2014 will be at least as good as 2013.

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

Scraping R-bloggers with Python – Part 2

In my previous post I showed how to write a small simple python script to download the pages of R-bloggers.com. If you followed that post and ran the script, you should have a folder on your hard drive with 2409 .html files labeled post1.html , post2.html and so forth. The next step is to write a small script that extract the information we want from each page, and store that information in a .csv file that is easily read by R. In this post I will show how to extract the post title, author name and date of a given post and store it in a .csv file with a unique id.

To do this open a document in your favorite python editor (I like to use aquamacs) and name it: extraction.py. As in the previous post we start by importing the modules that we will use for the extraction:

from BeautifulSoup import BeautifulSoup

import os
import re

As in the previous post we will be using the BeautifulSoup module to extract the relevant information from the pages. The os module is used to get a list of file from the directory where we have saved the .html files, and finally the re module allows us to use regular expressions to format the titles that include a comma value or a newline value (\n). We need to remove these as they would mess up the formatting of the .csv file.

After having read in the modules, we need to get a list of files that we can iterate over. First we need to specify the path were the files are saved, and then we use the os module to get all the filenames in the specified directory:

path = "/Users/thomasjensen/Documents/RBloggersScrape/download"

listing = os.listdir(path)

It might be that there are other files in the given directory, hence we apply a filter, in shape of a list comprehension, to weed out any file names that do not match our naming scheme:

listing = [name for name in listing if re.search(r"post\d+\.html",name) != None]

Notice that a regular expression was used to determine whether a given name in the list matched our naming scheme. For more on regular expressions have a look at this site.

The final steps in preparing our extraction is to change the working directory to where we have our .html files, and create an empty dictionary:

os.chdir(path)
data = {}

Dictionaries are one of the great features of Python. Essentially a dictionary is a mapping of a key to a specific value, however the fact that dictionaries can be nested within each other, allows us to create data structures similar to R’s data frames.

Now we are ready to begin extracting information from our downloaded pages. Much as in the previous post, we will loop over all the file names, read each file into Python and create a BeautifulSoup object from the file:

for page in listing:
    site = open(page,"rb")
    soup = BeautifulSoup(site)

In order to store the values we extract from a given page, we update the dictionary with a unique key for the page. Since our naming scheme made sure that each file had a unique name, we simply remove the .html part from the page name, and use that as our key:

key = re.sub(".html","",page)

data.update({key:{}})

This will create a mapping between our key and an empty dictionary, nested within the data dictionary. Once this is done we can start extract information and store it in our newly created nested dictionary. The content we want is located in the main column, which has the id tag “leftcontent” in the html code. To get at this we use find() function on soup object created above:

content = soup.find("div", id = "leftcontent")

The first “h1” tag in our content object contains the title, so again we will use the find() function on the content object, to find the first “h1” tag:

title = content.findNext("h1").text

To get the text within the “h1” tag the .text had been added to our search with in the content object.

To find the author name, we are lucky that there is a class of “div” tags called “meta” which contain a link with the author name in it. To get the author name we simply find the meta div class and search for a link. Then we pull out the text of the link tag:

author = content.find("div",{"class":"meta"}).findNext("a").text

Getting the date is a simple matter as it is nested within div tag with the class “date”:

date = content.find("div",{"class":"date"}).text

Once we have the three variables we put them in dictionaries that are nested within the nested dictionary we created with the key:

data[key]["title"] = title
data[key]["author"] = author
data[key]["date"] = date

Once we have run the loop and gone through all posts, we need to write them in the right format to a .csv file. To begin with we open a .csv file names output:

output = open("/Users/thomasjensen/Documents/RBloggersScrape/output.csv","wb")

then we create a header that contain the variable names and write it to the output.csv file as the first row:

variables = unicode(",".join(["id","date","author","title"]))
header = variables + "\n"
output.write(header.encode("utf8"))

Next we pull out all the unique keys from our dictionary that represent individual posts:

keys = data.keys()

Now it is a simple matter of looping through all the keys, pull out the information associated with each key, and write that information to the output.csv file:

for key in keys:
    print key
    id = key
    date = re.sub(",","",data[key]["date"])
    author = data[key]["author"]
    title = re.sub(",","",data[key]["title"])
    title = re.sub("\\n","",title)
    linelist = [id,date,author,title]
    linestring = unicode(",".join(linelist))
    linestring = linestring + "\n"
    output.write(linestring.encode("utf-8"))

Notice that we first create four variables that contain the id, date, author and title information. With regards to the title we use two regular expressions to remove any commas and “\n” from the title, as these would create new columns or new line breaks in the output.csv file. Finally we put the variables together in a list, and turn the list into a string with the list items separated by a comma. Then a linebreak is added to the end of the string, and the string is written to the output.csv file. As a last step we close the file connection:

output.close()

And that is it. If you followed the steps you should now have a csv file in your directory with 2409 rows, and four variables – ready to be read into R. Stay tuned for the next post which will show how we can use this data to see how R-bloggers has developed since 2005. The full extraction script is shown below:

from BeautifulSoup import BeautifulSoup

import os
import re

 path = "/Users/thomasjensen/Documents/RBloggersScrape/download"
 listing = os.listdir(path)

listing = [name for name in listing if re.search(r"post\d+\.html",name) != None]
 os.chdir(path)
 data = {}
 for page in listing:
site = open(page,"rb")
soup = BeautifulSoup(site)
key = re.sub(".html","",page)
print key
data.update({key:{}})
 content = soup.find("div", id = "leftcontent")
title = content.findNext("h1").text
author = content.find("div",{"class":"meta"}).findNext("a").text
date = content.find("div",{"class":"date"}).text
data[key]["title"] = title
data[key]["author"] = author
data[key]["date"] = date

 output = open("/Users/thomasjensen/Documents/RBloggersScrape/output.csv","wb")

 keys = data.keys()
 variables = unicode(",".join(["id","date","author","title"]))
 header = variables + "\n"
 output.write(header.encode("utf8"))
 for key in keys:
print key
id = key
date = re.sub(",","",data[key]["date"])
author = data[key]["author"]
title = re.sub(",","",data[key]["title"])
title = re.sub("\\n","",title)
linelist = [id,date,author,title]
linestring = unicode(",".join(linelist))
linestring = linestring + "\n"
output.write(linestring.encode("utf-8"))
 output.close()

Source:http://www.r-bloggers.com/scraping-r-bloggers-with-python-part-2/

Data Mining and Frequent Datasets

I've been doing some work for my exams in a few days and I'm going through some past papers but unfortunately there are no corresponding answers. I've answered the question and I was wondering if someone could tell me if I am correct.

My question is

    (c) A transactional dataset, T, is given below:
    t1: Milk, Chicken, Beer
    t2: Chicken, Cheese
    t3: Cheese, Boots
    t4: Cheese, Chicken, Beer,
    t5: Chicken, Beer, Clothes, Cheese, Milk
    t6: Clothes, Beer, Milk
    t7: Beer, Milk, Clothes

    Assume that minimum support is 0.5 (minsup = 0.5).

    (i) Find all frequent itemsets.

Here is how I worked it out:

    Item : Amount
    Milk : 4
    Chicken : 4
    Beer : 5
    Cheese : 4
    Boots : 1
    Clothes : 3

Now because the minsup is 0.5 you eliminate boots and clothes and make a combo of the remaining giving:

    {items} : Amount
    {Milk, Chicken} : 2
    {Milk, Beer} : 4
    {Milk, Cheese} : 1
    {Chicken, Beer} : 3
    {Chicken, Cheese} : 3
    {Beer, Cheese} : 2

Which leaves milk and beer as the only frequent item set then as it is the only one above the minsup?

data mining

Nanor

3 Answers

There are two ways to solve the problem:

    using Apriori algorithm
    Using FP counting

Assuming that you are using Apriori, the answer you got is correct.

The algorithm is simple:

First you count frequent 1-item sets and exclude the item-sets below minimum support.

Then count frequent 2-item sets by combining frequent items from previous iteration and exclude the item-sets below support threshold.

The algorithm can go on until no item-sets are greater than threshold.

In the problem given to you, you only get 1 set of 2 items greater than threshold so you can't move further.

There is a solved example of further steps on Wikipedia here.

You can refer "Data Mining Concepts and Techniques" by Han and Kamber for more examples.

141

There is more than two algorithms to solve this problem. I will just mention a few of them: Apriori, FPGrowth, Eclat, HMine, DCI, Relim, AIM, etc. –  Phil Mar 5 '13 at 7:18

OK to start, you must first understand, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

Now, the amount of raw data stored in corporate databases is exploding. From trillions of point-of-sale transactions and credit card purchases to pixel-by-pixel images of galaxies, databases are now measured in gigabytes and terabytes. (One terabyte = one trillion bytes. A terabyte is equivalent to about 2 million books!) For instance, every day, Wal-Mart uploads 20 million point-of-sale transactions to an A&T massively parallel system with 483 processors running a centralized database.

Raw data by itself, however, does not provide much information. In today's fiercely competitive business environment, companies need to rapidly turn these terabytes of raw data into significant insights into their customers and markets to guide their marketing, investment, and management strategies.

Now you must understand that association rule mining is an important model in data mining. Its mining algorithms discover all item associations (or rules) in the data that satisfy the user-specified minimum support (minsup) and minimum confidence (minconf) constraints. Minsup controls the minimum number of data cases that a rule must cover. Minconf controls the predictive strength of the rule.

Since only one minsup is used for the whole database, the model implicitly assumes that all items in the data are of the same nature and/or have similar frequencies in the data. This is, however, seldom the case in real- life applications. In many applications, some items appear very frequently in the data, while others rarely appear. If minsup is set too high, those rules that involve rare items will not be found. To find rules that involve both frequent and rare items, minsup has to be set very low.

This may cause combinatorial explosion because those frequent items will be associated with one another in all possible ways. This dilemma is called the rare item problem. This paper proposes a novel technique to solve this problem. The technique allows the user to specify multiple minimum supports to reflect the natures of the items and their varied frequencies in the database. In rule mining, different rules may need to satisfy different minimum supports depending on what items are in the rules.

Given a set of transactions T (the database), the problem of mining association rules is to discover all association rules that have support and confidence greater than the user-specified minimum support (called minsup) and minimum confidence (called minconf).

I hope that once you understand the very basics of data mining that the answer to this question shall become apparent.

1

The Apriori algorithm is based on the idea that for a pair o items to be frequent, each individual item should also be frequent. If the hamburguer-ketchup pair is frequent, the hamburger itself must also appear frequently in the baskets. The same can be said about the ketchup.

So for the algorithm, it is established a "threshold X" to define what is or it is not frequent. If an item appears more than X times, it is considered frequent.

The first step of the algorithm is to pass for each item in each basket, and calculate their frequency (count how many time it appears). This can be done with a hash of size N, where the position y of the hash, refers to the frequency of Y.

If item y has a frequency greater than X, it is said to be frequent.

In the second step of the algorithm, we iterate through the items again, computing the frequency of pairs in the baskets. The catch is that we compute only for items that are individually frequent. So if item y and item z are frequent on itselves, we then compute the frequency of the pair. This condition greatly reduces the pairs to compute, and the amount of memory taken.

Once this is calculated, the frequencies greater than the threshold are said frequent itemset.

Source: http://stackoverflow.com/questions/14164853/data-mining-and-frequent-datasets?rq=1

4 Data Mining Tips to Scrap Real Estate Data; Innovative Way to Give Realty Business a boost!

Internet has become a huge source of data – in fact; it has turned into a goldmine for the marketers, from where they can easily dig the useful data!

    Web scraping has become a norm in today’s competitive era, where one with maximum and relevant information wins the race!

Real Estate Data Extraction and Scraping Service

It has helped many industries to carve a niche in the market; especially real estate – Scraping real estate data has been of great help for professionals to reach out to a large number of people and gather reliable property data. However, there are some people for whom web scraping is still an alien concept; most probably because most of its advantages are not discussed.

    There are institutions, companies and organizations, entrepreneurs, as well as just normal citizens generating an extraordinary amount of information every day. Property information extraction can be effectively used to get an idea about the customer psyche and even generate valuable lead to further the business.

In addition to this, data mining has also some of following uses making it an indispensable part of marketing.

Gather Properties Details from Different Geographical Locations

You are an estate agent and want to expand your business to the neighboring city or state. But, then you are short of information. You are completely aware of the properties in the vicinity and in your town; however, with data mining services will help you to get an idea about the properties in the other state. You can also approach probable clients and increase your database to offer extensive services.

Online Offers and Discounts are just a Click Away

Now, it is tough to deal with the clients, show them the property of their choice and again act as a mediator between the buyer and seller. In all this, it becomes almost difficult to take a look at some special discounts or offers. With the data mining services, you can get an insight into these amazing offers. Thus, you can plan a move or even provide your client an amazing deal.

What people are talking about – Easy Monitoring of your Online Reputation

Internet has become a melting pot where different people come together. In fact, it provides a huge platform where people discuss about their likes and dislikes. When you dig into such online forums, you can get an idea of reputation that you or your firm holds. You can know what people think about you and where you require to buck up and where you need to slow down.

A Chance to Know your Competitors Better!

Last, but not the least, you can keep an eye on the competitor.  Real Estate is getting more competitive; and therefore, it is important to have knowledge about your competitors to get an upper hand. It will help you to plan your moves and strategize with more ease. Moreover, you also know what is that “something” that your competitor does not have and you have, with can be subtly highlighted.

Property information extraction can prove to be the most fruitful method to get a cutting edge in the industry.

Source: http://www.hitechbposervices.com/blog/4-data-mining-tips-to-scrap-real-estate-data-innovative-way-to-give-realty-business-a-boost/

Web Scraping for Fun & Profit

There’s a number of ways to retrieve data from a backend system within mobile projects. In an ideal world, everything would have a RESTful JSON API – but often, this isn’t the case.Sometimes, SOAP is the language of the backend. Sometimes, it’s some proprietary protocol which might not even be HTTP-based. Then, there’s scraping.

Retrieving information from web sites as a human is easy. The page communicates information using stylistic elements like headings, tables and lists – this is the communication protocol of the web. Machines retrieve information with a focus on structure rather than style, typically using communication protocols like XML or JSON. Web scraping attempts to bridge this human protocol into a machine-readable format like JSON. This is what we try to achieve with web scraping.

As a means of getting to data, it don’t get much worse than web scraping. Scrapers were often built with Regular Expressions to retrieve the data from the page. Difficult to craft, impossible to maintain, this means of retrieval was far from ideal. The risks are many – even the slightest layout change on a web page can upset scraper code, and break the entire integration. It’s a fragile means for building integrations, but sometimes it’s the only way.

Having built a scraper service recently, the most interesting observation for me is how far we’ve come from these “dark days”. Node.js, and the massive ecosystem of community built modules has done much to change how these scraper services are built.

Effectively Scraping Information

Websites are built on the Document Object Model, or DOM. This is a tree structure, which represents the information on a page.By interpreting the source of a website as a DOM, we can retrieve information much more reliably than using methods like regular expression matching. The most popular method of querying the DOM is using jQuery, which enables us to build powerful and maintainable queries for information. The JSDom Node module allows us to use a DOM-like structure in serverside code.

For purpose of Illustration, we’re going to scrape the blog page of FeedHenry’s website. I’ve built a small code snippet that retrieves the contents of the blog, and translates it into a JSON API. To find the queries I need to run, first I need to look at the HTML of the page. To do this, in Chrome, I right-click the element I’m looking to inspect on the page, and click “Inspect Element”.

Screen Shot 2014-09-30 at 10.44.38

Articles on the FeedHenry blog are a series of ‘div’ elements with the ‘.itemContainer’ class

Searching for a pattern in the HTML to query all blog post elements, we construct the `div.itemContainer` query. In jQuery, we can iterate over these using the .each method:

var posts = [];

$('div.itemContainer').each(function(index, item){

  // Make JSON objects of every post in here, pushing to the posts[] array

});

From there, we pick off the heading, author and post summary using a child selector on the original post, querying the relevant semantic elements:

    Post Title, using jQuery:

    $(item).find('h3').text()trim() // trim, because titles have white space either side

    Post Author, using jQuery:

    $(item).find('.catItemAuthor a').text()

    Post Body, using jQuery:

    $(item).find('p').text()

Adding some JSDom magic to our snippet, and pulling together the above two concept (iterating through posts, and picking off info from each post), we get this snippet:

var request = require('request'),

jsdom = require('jsdom');

jsdom.env(

  "http://www.feedhenry.com/category/blog",

  ["http://code.jquery.com/jquery.js"],

  function (errors, window) {

    var $ = window.$, // Alias jQUery

    posts = [];

    $('div.itemContainer').each(function(index, item){

      item = $(item); // make queryable in JQ

      posts.push({

        heading : item.find('h3').text().trim(),

        author : item.find('.catItemAuthor a').text(),

        teaser : item.find('p').text()

      });

    });

    console.log(posts);

  }

);

A note on building CSS Queries

As with styling web sites with CSS, building effective CSS queries is equally as important when building a scraper. It’s important to build queries that are not too specific, or likely to break when the structure of the page changes. Equally important is to pick a query that is not too general, and likely to select extra data from the page you don’t want to retrieve.

A neat trick for generating the relevant selector statement is to use Chrome’s “CSS Path” feature in the inspector. After finding the element in the inspector panel, right click, and select “Copy CSS Path”. This method is good for individual items, but for picking repeating patterns (like blog posts), this doesn’t work though. Often, the path it gives is much too specific, making for a fragile binding. Any changes to the page’s structure will break the query.

Making a Re-usable Scraping Service

Now that we’ve retrieved information from a web page, and made some JSON, let’s build a reusable API from this. We’re going to make a FeedHenry Blog Scraper service in FeedHenry3. For those of you not familiar with service creation, see this video walkthrough.

We’re going to start by creating a “new mBaaS Service”, rather than selecting one of the off-the-shelf services. To do this, we modify the application.js file of our service to include one route, /blog, which includes our code snippet from earlier:

// just boilerplate scraper setup

var mbaasApi = require('fh-mbaas-api'),

express = require('express'),

mbaasExpress = mbaasApi.mbaasExpress(),

cors = require('cors'),

request = require('request'),

jsdom = require('jsdom');

var app = express();

app.use(cors());

app.use('/sys', mbaasExpress.sys([]));

app.use('/mbaas', mbaasExpress.mbaas);

app.use(mbaasExpress.fhmiddleware());

// Our /blog scraper route

app.get('/blog', function(req, res, next){

  jsdom.env(

    "http://www.feedhenry.com/category/blog",

    ["http://code.jquery.com/jquery.js"],

    function (errors, window) {

      var $ = window.$, // Alias jQUery

      posts = [];

      $('div.itemContainer').each(function(index, item){

        item = $(item); // make queryable in JQ

        posts.push({

          heading : item.find('h3').text().trim(),

          author : item.find('.catItemAuthor a').text(),

          teaser : item.find('p').text()

        });

      });

      return res.json(posts);

    }

  );

});

app.use(mbaasExpress.errorHandler());

var port = process.env.FH_PORT || process.env.VCAP_APP_PORT || 8001;

var server = app.listen(port, function() {});

We’re also going to write some documentation for our service, so we (and other developers) can interact with it using the FeedHenry discovery console. We’re going to modify the README.md file to document what we’ve just done using API Blueprint documentation format:

# FeedHenry Blog Web Scraper

This is a feedhenry blog scraper service. It uses the `JSDom` and `request` modules to retrieve the contents of the FeedHenry developer blog, and parse the content using jQuery.

# Group Scraper API Group

# blog [/blog]

Blog Endpoint

## blog [GET]

Get blog posts endpoint, returns JSON data.

+ Response 200 (application/json)

    + Body

            [{ blog post}, { blog post}, { blog post}]

We can now try out the scraper service in the studio, and see the response:

Scraping – The Ultimate in API Creation?

Now that I’ve described some modern techniques for effectively scraping data from web sites, it’s time for some major caveats. First,  WordPress blogs like ours already have feeds and APIs available to developers - there’s no need to ever scrape any of this content. Web Scraping is not a replacement for an API. It should be used only as a last resort, after every endeavour to discover an API has already been made. Using a web scraper in a commercial setting requires much time set aside to maintain the queries, and an agreement with the source data is being scraped on to alert developers in the event the page changes structure.

With all this in mind, it can be a useful tool to iterate quickly on an integration when waiting for an API, or as a fun hack project.

Source: http://www.feedhenry.com/web-scraping-fun-profit/

Get started with screenscraping using Google Chrome’s Scraper extension

How do you get information from a website to a Excel spreadsheet? The answer is screenscraping. There are a number of softwares and plattforms (such as OutWit Hub, Google Docs and Scraper Wiki) that helps you do this, but none of them are – in my opinion – as easy to use as the Google Chrome extension Scraper, which has become one of my absolutely favourite data tools.

What is a screenscraper?

I like to think of a screenscraper as a small robot that reads websites and extracts pieces of information. When you are able to unleash a scraper on hundreads, thousands or even more pages it can be an incredibly powerful tool.

In its most simple form, the one that we will look at in this blog post, it gathers information from one webpage only.

Google Chrome’s Scraper

Scraper is an Google Chrome extension that can be installed for free at Chrome Web Store.

Image

Now if you installed the extension correctly you should be able to see the option “Scrape similar” if you right-click any element on a webpage.

The Task: Scraping the contact details of all Swedish MPs

Image

This is the site we’ll be working with, a list of all Swedish MPs, including their contact details. Start by right-clicking the name of any person and chose Scrape similar. This should open the following window.

Understanding XPaths

At w3schools you’ll find a broader introduction to XPaths.

Before we move on to the actual scrape, let me briefly introduce XPaths. XPath is a language for finding information in an XML structure, for example an HTML file. It is a way to select tags (or rather “nodes”) of interest. In this case we use XPaths to define what parts of the webpage that we want to collect.

A typical XPath might look something like this:

    //div[@id="content"]/table[1]/tr

Which in plain English translates to:

    // - Search the whole document...

    div[@id="content"] - ...for the div tag with the id "content".

    table[1] -  Select the first table.

    tr - And in that table, grab all rows.

Over to Scraper then. I’m given the following suggested XPath:

    //section[1]/div/div/div/dl/dt/a

The results look pretty good, but it seems we only get names starting with an A. And we would also like to collect to phone numbers and party names. So let’s go back to the webpage and look at the HTML structure.

Right-click one of the MPs and chose Inspect element. We can see that each alphabetical list is contained in a section tag with the class “grid_6 alpha omega searchresult container clist”.

 And if we open the section tag we find the list of MPs in div tags.

We will do this scrape in two steps. Step one is to select the tags containing all information about the MPs with one XPath. Step two is to pick the specific pieces of data that we are interested in (name, e-mail, phone number, party) and place them in columns.

Writing our XPaths

In step one we want to try to get as deep into the HTML structure as possible without losing any of the elements we are interested in. Hover the tags in the Elements window to see what tags correspond to what elements on the page.

In our case this is the last tag that contains all the data we are looking for:

    //section[@class="grid_6 alpha omega searchresult container clist"]/div/div/div/dl

Click Scrape to test run the XPath. It should give you a list that looks something like this.

Scroll down the list to make sure it has 349 rows. That is the number of MPs in the Swedish parliament. The second step is to split this data into columns. Go back to the webpage and inspect the HTML code.

I have highlighted the parts that we want to extract. Grab them with the following XPaths:

    name: dt/a
    party: dd[1]
    region: dd[2]/span[1]
    seat: dd[2]/span[2]
    phone: dd[3]
    e-mail: dd[4]/span/a

Insert these paths in the Columns field and click Scrape to run the scraper.

Click Export to Google Docs to get the data into a spreadsheet.

Source: http://dataist.wordpress.com/2012/10/12/get-started-with-screenscraping-using-google-chromes-scraper-extension/

Future of Web Scraping

The Internet is large, complex and ever-evolving. Nearly 90% of all the data in the world has been generated over the last two years. In this vast ocean of data, how does one get to the relevant piece of information? This is where web scraping takes over.

Web scrapers attach themselves, like a leech, to this beast and ride the waves by extracting information form websites at will. Granted “scraping” doesn’t have a lot of positive connotations, yet it happens to be the only way to access data or content from a web site without RSS or an open API.

Future of Web Scraping

Web scraping faces testing times ahead. We outline why there may be some serious challenges to its future.

With rise in data, redundancies in web scraping are rising. No more is web scraping a domain of the coders; in fact, companies now offer customized scraping tools to clients which they can use to get the data they want. The outcome of everyone equipped to crawl, scrape, and extract, is unnecessary waste of precious man-power. Collaborative scraping could well heal this hurt. Here, where one web crawler does a broad scraping, the others scrape data off an API. An extension of the problem is that text retrieval attracts more attention than multimedia; and with websites becoming more complex, this enforces limited scraping capacity.

Easily, the biggest challenge to web scraping technology is Privacy concerns. With data freely available (most of it voluntary, much of it involuntary), the call for stricter legislation rings loudest. Unintended users can easily target a company and take advantage of the business using web scraping. The disdain with which “do not scrape” policies are treated and terms of usage violated, tells us that even legal restrictions are not enough. This begs to ask an age-old question: is scraping legal?

Is Crawling Legal? from PromptCloud

The flipside to this argument is that if technological barriers replace legal clauses, then web scraping will see a steady, and sure, decline. This is a distinct possibility since the only way scraping activity thrives is on the grid, and if the very means are taken away and programs no longer have access to website information, then web scraping by itself will be wiped out.

Building the Future

On the same thought is the growing trend of accepting “open data”. The open data policy, while long mused hasn’t been used at the scale it should be. The old way was to believe that closed data is the edge over competitors. But that mindset is changing. Increasingly, websites are beginning to offer APIs and embracing open data. But what’s the advantage of doing so?

Selling APIs not only brings in the money, but also is useful in driving back traffic to the sites! APIs are also a more controlled, cleaner way of turning sites into services. Steadily many successful sites like Twitter, LinkedIn etc. are offering access to their APIs with paid services and actively blocking scraper and bots.

Yet, beyond these obvious challenges, there’s a glimmer of hope for web scraping. And this is based on a singular factor: the growing need for data!

With Internet & web technology spreading, massive amounts of data will be accessible on the web. Particularly with increased adoption of mobile internet. According to one report, by 2020, the number of mobile internet users will hit 3.8 billion, or around half of the world’s population!

Since ‘big data’ can be both, structured & unstructured; web scraping tools will only get sharper and incisive. There is fierce competition between those who provide web scraping solutions. With the rise of open source languages like Python, R & Ruby, Customized scraping tools will only flourish bringing in a new wave of data collection and aggregation methods.

Source: https://www.promptcloud.com/blog/Future-of-Web-Scraping

3 Reasons to Up Your Web Scraping Game

If you aren’t using a machine-learning-driven intelligent Web scraping solution yet, here are three reasons why you might want to abandon that entry-level Web-scraping software or cut your high-cost script-writing approach.

    You need to keep an eye on a large number of web sources that get updated frequently.
    Understanding what’s changed is at least as critical as the data itself.
    You don’t want maintenance and scheduling to drag you down.

Here’s what an intelligent Web-scraping solution can deliver – and why:

1. Better data monitoring of an ever-shifting Web

If you need to keep a watch over hundreds, thousands or even tens of thousands of sites, an intelligent Web scraper is a must, because:

    It can scale – easily adding new websites, coordinating extraction routines, and automating the normalization of data across different websites.

    It can navigate and extract data from websites efficiently. Script-based approaches typically only can view a Web page in isolation, making it difficult to optimize navigation across unique pages of a targeted site. More intelligent approaches can be trained to bypass unnecessary links and leave a lighter footprint on the sites you need to access. And, they can monitor millions of precise Web data points quickly. This means you can monitor more pages on more sites with more frequent updates.

2. Critical alerts to Web data changes

A key sales executive suddenly drops off of the management page of your main competitor. That can mean big shakeup in the entire organization, which your sales team can jump on.

An intelligent Web scraper can alert you to this personnel shift because it can be set to monitor for just the changes; less powerful technologies or script-based approaches can’t. Whether you’re tracking price shifts, people moves, or product changes (or more) intelligent Web scraping delivers more profound insights.

3. Maintenance may become your biggest nightmare

You’ve purchased an entry-level tool and built out scrapers for a few hundred sites.  At first, everything seems fine. But, within weeks you begin to notice that your data is incomplete and not being updated as you’d expected. Why did your data deliveries disappear?

Reality is that these low-cost tools are simply not designed for mission-critical business applications – on the surface they look helpful and easy to use, but underneath the surface they are script-based and highly dependent upon the HTML of a website. But websites change, and entry-level web scraping tools are simply not engineered to adapt to those changes.

And, most of these tools are simply not designed for enterprise use. They have limited reporting, if any, so the only way to know whether they’re successfully completing their tasks is by finding gaps in the data – often when it’s too late.

An intelligent web scraping approach doesn’t rely upon the HTML of a web page. It uses machine learning algorithms which view the web the same way a user might. A typical reader doesn’t get confused when a font or color is changed on a website, and neither do these algorithms. But simple approaches to web scraping are highly dependent on the specific HTML to help it understand the content of a page. So, when websites have design changes (on average once every 18 months), the software fails.

While entry-level web scraping software can be an easy solution for simple, one-time web scraping projects, the scripts they generate are fragile and the resources required for tracking and maintenance can become overwhelming when you need to regularly extract data from multiple sites.

Case in point: Shopzilla assimilates data five times faster than outsourced Web scrapers

To demonstrate the power of intelligent Web scraping, here’s a real-life example from Shopzilla.  Shopzilla manages a premier portfolio of online shopping brands in the United States and Europe, connecting more than 40 million shoppers each month with millions of products from retailers worldwide. With the explosive growth of retail data on the Web, Shopzilla’s outsourced, custom-built approach, based on scripting, could not add the product lines of new retailers to its site in a timely fashion. It was taking up to two weeks to write the scripts needed to make a single site accessible.

By deploying Connotate’s intelligent web scraping platform on site, Shopzilla gained the ability to harness Web data’s rapid growth and keep up to date. Today, new sources are added in days, not weeks.  The platform continually monitors Web content from thousands of sites, delivering high volumes of data every day in a structured format. The result: 500 percent more data from new retailers. An added bonus: the company has reduced IT maintenance costs and its dependence on outsourced development timetables. Case in point: Deep competitor intelligence in two languages

A global manufacturer needed to monitor competitors’ technology improvements in a field where market leadership hinges on an ability to quickly leverage these advances. That meant accessing scholarly journals and niche sites in multiple languages. Using the Connotate solution, it was able to access highly-targeted, keyword-driven university and industry research journals and blogs in German and English that are hard to reach because they do not support RSS feeds. Our solution also incorporated semantic analysis to tag and categorize data and help identify new technologies and products not currently in the keyword list. The firm enhanced its competitive edge with the up-to-the-minute, precise data it needed.

Is your Web scraping intelligent enough?

See what intelligent agents through an automated Web data extraction and monitoring solution can bring to your business. Contact us and speak with one of experts.

Source:http://www.connotate.com/3-reasons-web-scraping-game-6579#.VGMjH2f4EuQ

Data Scraping vs. Data Crawling

One of our favorite quotes has been- ‘If a problem changes by an order, it becomes a totally different problem’ and in this lies the answer to- what’s the difference between scraping and crawling?

Crawling usually refers to dealing with large data-sets where you develop your own crawlers (or bots) which crawl to the deepest of the web pages. Data scraping on the other hand refers to retrieving information from any source (not necessarily the web). It’s more often the case that irrespective of the approaches involved, we refer to extracting data from the web as scraping (or harvesting) and that’s a serious misconception.

=>Below are some differences in our opinion- both evident and subtle
1.    Scraping data does not necessarily involve the web. Data scraping could refer to extracting information from a local machine, a database, or even if it is from the internet, a mere “Save as” link on the page is also a subset of the data scraping universe. Crawling on the other hand differs immensely in scale as well as in range. Firstly, crawling = web crawling which means on the web, we can only “crawl” data. Programs that perform this incredible job are called crawl agents or bots or spiders (please leave the other spider in spiderman’s world). Some web spiders are algorithmically designed to reach the maximum depth of a page and crawl them iteratively (did we ever say scrape?).

2.    Web is an open world and the quintessential practising platform of our right to freedom. Thus a lot of content gets created and then duplicated. For instance, the same blog might be posted on different pages and our spiders don’t understand that. Hence, data de-duplication (affectionately dedup) is an integral part of data crawling. This is done to achieve two things- keep our clients happy by not flooding their machines with the same data more than once, and saving our own servers some space. However, dedup is not necessarily a part of data scraping.

3.    One of the most challenging things in the web crawling space is to deal with coordination of successive crawls. Our spiders have to be polite with the servers that they hit so that they don’t piss them off and this creates an interesting situation to handle. Over a period of time, our intelligent spiders have to get more intelligent (and not crazy!) and learn to know when and how much to hit a server in order to crawl data on its web pages while complying with its politeness policies.

4.    Finally, different crawl agents are used to crawl different websites and hence you need to ensure they don’t conflict with each other in the process. This situation never arises when you intend to just scrape data.

On a concluding note, scraping represents a very superficial node of crawling which we call extraction and that again requires few algorithms and some automation in place.

Source:https://www.promptcloud.com/blog/data-scraping-vs-data-crawling/

Web Scraping the Solution to Data Harvesting

The internet is the number one information provider in the world and it is of course the largest in the same course. Web scraping is meant to extract and harvest useful information from the internet. It can be regarded as a multidisciplinary process that involves statistics, databases, data harvesting and data retrieval.

There has been noted a rapid expansion of the web and therefore causing an enormous growth of information. This has led to increased difficulty in the extraction of useful and potential information. Web scraping therefore confronts this problem by harvesting explicit information from a number of websites for knowledge discovery and easy access. It is important to realize that query interfaces of web databases are prone to sharing of same building blocks. It is therefore important to realize that the web offers unprecedented challenge and opportunity to data harvesting.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/web-scraping-solution-data-harvesting/

Application of Web Data Mining in CRM

The process of improvising the customer relations and interactions and making them more amicable may be termed as Customer relationship management (CRM). Since web data mining is used in the utilization of the various modeling and data analysis methods in detecting given patterns and relationships in the data, it can be used as an effective tool in CRM. By the effectively using web data mining you are able to understand what your customers what.

It is important to note that web data mining can be used effectively in searching for the right and potential customers to be offered the right products at the right time. The result of this in any business is the increase in the revenue generated. This is made possible as you are able to respond to each customer in an effective and efficient way. The method further utilizes very few resources and can be therefore termed as an economical method.

In the next paragraphs we discuss the basic process of customer relationship management and its integration with web data mining service. The following are the basic process that should be used in understanding what your customers need, sending them the right offers and products, and reducing the resources used in managing your customers.

Defining the business objective. Web data mining can be used to define and inform your customers your business objective. By doing research you can be able to determine whether your business objective is communicated well to your customers and clients. Does your business objective take interest in the customers? Your business goal must be clearly outlined in your business CRM. By having a more precise and defined goal is the possible way of ensuring success in the customer relationship management.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/application-web-data-mining-crm/

Scraping webdata from a website that loads data in a streaming fashion

I'm trying to scrape some data off of the FEC.gov website using python for a project of mine. Normally I use python

mechanize and beautifulsoup to do the scraping.

I've been able to figure out most of the issues but can't seem to get around a problem. It seems like the data is

streamed into the table and mechanize.Browser() just stops listening.

So here's the issue: If you visit http://query.nictusa.com/cgi-bin/can_ind/2011_P80003338/1/A ... you get the first 500

contributors whose last name starts with A and have given money to candidate P80003338 ... however, if you use

browser.open() at that url all you get is the first ~5 rows.

I'm guessing its because mechanize isn't letting the page fully load before the .read() is executed. I tried putting a

time.sleep(10) between the .open() and .read() but that didn't make much difference.

And I checked, there's no javascript or AJAX in the website (or at least none are visible when you use the 'view-

source'). SO I don't think its a javascript issue.

Any thoughts or suggestions? I could use selenium or something similar but that's something that I'm trying to avoid.

-Will

2 Answers

Why not use an html parser like lxml with xpath expressions.

I tried

>>> import lxml.html as lh
>>> data = lh.parse('http://query.nictusa.com/cgi-bin/can_ind/2011_P80003338/1/A')
>>> name = data.xpath('/html/body/table[2]/tr[5]/td[1]/a/text()')
>>> name
[' AABY, TRYGVE']
>>> name = data.xpath('//table[2]/*/td[1]/a/text()')
>>> len(name)
500
>>> name[499]
' AHMED, ASHFAQ'
>>>



Similarly, you can create xpath expression of your choice to work with.


Source: http://stackoverflow.com/questions/9435512/scraping-webdata-from-a-website-that-loads-data-in-a-streaming-

fashion

How can I circumvent page view limits when scraping web data using Python?

I am using Python to scrape US postal code population data from http:/www.city-data.com, through this directory: http://www.city-data.com/zipDir.html. The specific pages I am trying to scrape are individual postal code pages with URLs like this: http://www.city-data.com/zips/01001.html. All of the individual zip code pages I need to access have this same URL Format, so my script simply does the following for postal_code in range:

    Creates URL given postal code
    Tries to get response from URL
    If (2), Check the HTTP of that URL
    If HTTP is 200, retrieves the HTML and scrapes the data into a list
    If HTTP is not 200, pass and count error (not a valid postal code/URL)
    If no response from URL because of error, pass that postal code and count error
    At end of script, print counter variables and timestamp

The problem is that I run the script and it works fine for ~500 postal codes, then suddenly stops working and returns repeated timeout errors. My suspicion is that the site's server is limiting the page views coming from my IP address, preventing me from completing the amount of scraping that I need to do (all 100,000 potential postal codes).

My question is as follows: Is there a way to confuse the site's server, for example using a proxy of some kind, so that it will not limit my page views and I can scrape all of the data I need?

Thanks for the help! Here is the code:

##POSTAL CODE POPULATION SCRAPER##

import requests

import re

import datetime

def zip_population_scrape():

    """
    This script will scrape population data for postal codes in range
    from city-data.com.
    """
    postal_code_data = [['zip','population']] #list for storing scraped data

    #Counters for keeping track:
    total_scraped = 0
    total_invalid = 0
    errors = 0


    for postal_code in range(1001,5000):

        #This if statement is necessary because the postal code can't start
        #with 0 in order for the for statement to interate successfully
        if postal_code <10000:
            postal_code_string = str(0)+str(postal_code)
        else:
            postal_code_string = str(postal_code)

        #all postal code URLs have the same format on this site
        url = 'http://www.city-data.com/zips/' + postal_code_string + '.html'

        #try to get current URL
        try:
            response = requests.get(url, timeout = 5)
            http = response.status_code

            #print current for logging purposes
            print url +" - HTTP:  " + str(http)

            #if valid webpage:
            if http == 200:

                #save html as text
                html = response.text

                #extra print statement for status updates
                print "HTML ready"

                #try to find two substrings in HTML text
                #add the substring in between them to list w/ postal code
                try:           

                    found = re.search('population in 2011:</b> (.*)<br>', html).group(1)

                    #add to # scraped counter
                    total_scraped +=1

                    postal_code_data.append([postal_code_string,found])

                    #print statement for logging
                    print postal_code_string + ": " + str(found) + ". Data scrape successful. " + str(total_scraped) + " total zips scraped."
                #if substrings not found, try searching for others
                #and doing the same as above   
                except AttributeError:
                    found = re.search('population in 2010:</b> (.*)<br>', html).group(1)

                    total_scraped +=1

                    postal_code_data.append([postal_code_string,found])
                    print postal_code_string + ": " + str(found) + ". Data scrape successful. " + str(total_scraped) + " total zips scraped."

            #if http =404, zip is not valid. Add to counter and print log        
            elif http == 404:
                total_invalid +=1

                print postal_code_string + ": Not a valid zip code. " + str(total_invalid) + " total invalid zips."

            #other http codes: add to error counter and print log
            else:
                errors +=1

                print postal_code_string + ": HTTP Code Error. " + str(errors) + " total errors."

        #if get url fails by connnection error, add to error count & pass
        except requests.exceptions.ConnectionError:
            errors +=1
            print postal_code_string + ": Connection Error. " + str(errors) + " total errors."
            pass

        #if get url fails by timeout error, add to error count & pass
        except requests.exceptions.Timeout:
            errors +=1
            print postal_code_string + ": Timeout Error. " + str(errors) + " total errors."
            pass


    #print final log/counter data, along with timestamp finished
    now= datetime.datetime.now()
    print now.strftime("%Y-%m-%d %H:%M")
    print str(total_scraped) + " total zips scraped."
    print str(total_invalid) + " total unavailable zips."
    print str(errors) + " total errors."



Source: http://stackoverflow.com/questions/25452798/how-can-i-circumvent-page-view-limits-when-scraping-web-data-using-python