What is Data Mining? Why Data Mining is Important?

What is Data Mining? Why Data Mining is Important?

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

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

Here is an overview of data mining services inclusion:

* Market research, product research, survey and analysis
* Collection information about investors, funds and investments
* Forums, blogs and other resources for customer views/opinions
* Scanning large volumes of data
* Information extraction
* Pre-processing of data from the data warehouse
* Meta data extraction
* Web data online mining services
* data online mining research
* Online newspaper and news sources information research
* Excel sheet presentation of data collected from online sources
* Competitor analysis
* data mining books
* Information interpretation
* Updating collected data

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

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

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

Data Mining and Financial Data Analysis

Data Mining and Financial Data Analysis

Introduction:

Most marketers understand the value of collecting financial data, but also realize the challenges of leveraging this knowledge to create intelligent, proactive pathways back to the customer. Data mining - technologies and techniques for recognizing and tracking patterns within data - helps businesses sift through layers of seemingly unrelated data for meaningful relationships, where they can anticipate, rather than simply react to, customer needs as well as financial need. In this accessible introduction, we provides a business and technological overview of data mining and outlines how, along with sound business processes and complementary technologies, data mining can reinforce and redefine for financial analysis.

Objective:

1. The main objective of mining techniques is to discuss how customized data mining tools should be developed for financial data analysis.

2. Usage pattern, in terms of the purpose can be categories as per the need for financial analysis.

3. Develop a tool for financial analysis through data mining techniques.

Data mining:

Data mining is the procedure for extracting or mining knowledge for the large quantity of data or we can say data mining is "knowledge mining for data" or also we can say Knowledge Discovery in Database (KDD). Means data mining is : data collection , database creation, data management, data analysis and understanding.

There are some steps in the process of knowledge discovery in database, such as

1. Data cleaning. (To remove nose and inconsistent data)

2. Data integration. (Where multiple data source may be combined.)

3. Data selection. (Where data relevant to the analysis task are retrieved from the database.)

4. Data transformation. (Where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance)

5. Data mining. (An essential process where intelligent methods are applied in order to extract data patterns.)

6. Pattern evaluation. (To identify the truly interesting patterns representing knowledge based on some interesting measures.)

7. Knowledge presentation.(Where visualization and knowledge representation techniques are used to present the mined knowledge to the user.)

Data Warehouse:

A data warehouse is a repository of information collected from multiple sources, stored under a unified schema and which usually resides at a single site.

Text:

Most of the banks and financial institutions offer a wide verity of banking services such as checking, savings, business and individual customer transactions, credit and investment services like mutual funds etc. Some also offer insurance services and stock investment services.

There are different types of analysis available, but in this case we want to give one analysis known as "Evolution Analysis".

Data evolution analysis is used for the object whose behavior changes over time. Although this may include characterization, discrimination, association, classification, or clustering of time related data, means we can say this evolution analysis is done through the time series data analysis, sequence or periodicity pattern matching and similarity based data analysis.

Data collect from banking and financial sectors are often relatively complete, reliable and high quality, which gives the facility for analysis and data mining. Here we discuss few cases such as,

Eg, 1. Suppose we have stock market data of the last few years available. And we would like to invest in shares of best companies. A data mining study of stock exchange data may identify stock evolution regularities for overall stocks and for the stocks of particular companies. Such regularities may help predict future trends in stock market prices, contributing our decision making regarding stock investments.

Eg, 2. One may like to view the debt and revenue change by month, by region and by other factors along with minimum, maximum, total, average, and other statistical information. Data ware houses, give the facility for comparative analysis and outlier analysis all are play important roles in financial data analysis and mining.

Eg, 3. Loan payment prediction and customer credit analysis are critical to the business of the bank. There are many factors can strongly influence loan payment performance and customer credit rating. Data mining may help identify important factors and eliminate irrelevant one.

Factors related to the risk of loan payments like term of the loan, debt ratio, payment to income ratio, credit history and many more. The banks than decide whose profile shows relatively low risks according to the critical factor analysis.

We can perform the task faster and create a more sophisticated presentation with financial analysis software. These products condense complex data analyses into easy-to-understand graphic presentations. And there's a bonus: Such software can vault our practice to a more advanced business consulting level and help we attract new clients.

To help us find a program that best fits our needs-and our budget-we examined some of the leading packages that represent, by vendors' estimates, more than 90% of the market. Although all the packages are marketed as financial analysis software, they don't all perform every function needed for full-spectrum analyses. It should allow us to provide a unique service to clients.

The Products:

ACCPAC CFO (Comprehensive Financial Optimizer) is designed for small and medium-size enterprises and can help make business-planning decisions by modeling the impact of various options. This is accomplished by demonstrating the what-if outcomes of small changes. A roll forward feature prepares budgets or forecast reports in minutes. The program also generates a financial scorecard of key financial information and indicators.

Customized Financial Analysis by BizBench provides financial benchmarking to determine how a company compares to others in its industry by using the Risk Management Association (RMA) database. It also highlights key ratios that need improvement and year-to-year trend analysis. A unique function, Back Calculation, calculates the profit targets or the appropriate asset base to support existing sales and profitability. Its DuPont Model Analysis demonstrates how each ratio affects return on equity.

Financial Analysis CS reviews and compares a client's financial position with business peers or industry standards. It also can compare multiple locations of a single business to determine which are most profitable. Users who subscribe to the RMA option can integrate with Financial Analysis CS, which then lets them provide aggregated financial indicators of peers or industry standards, showing clients how their businesses compare.

iLumen regularly collects a client's financial information to provide ongoing analysis. It also provides benchmarking information, comparing the client's financial performance with industry peers. The system is Web-based and can monitor a client's performance on a monthly, quarterly and annual basis. The network can upload a trial balance file directly from any accounting software program and provide charts, graphs and ratios that demonstrate a company's performance for the period. Analysis tools are viewed through customized dashboards.

PlanGuru by New Horizon Technologies can generate client-ready integrated balance sheets, income statements and cash-flow statements. The program includes tools for analyzing data, making projections, forecasting and budgeting. It also supports multiple resulting scenarios. The system can calculate up to 21 financial ratios as well as the breakeven point. PlanGuru uses a spreadsheet-style interface and wizards that guide users through data entry. It can import from Excel, QuickBooks, Peachtree and plain text files. It comes in professional and consultant editions. An add-on, called the Business Analyzer, calculates benchmarks.

ProfitCents by Sageworks is Web-based, so it requires no software or updates. It integrates with QuickBooks, CCH, Caseware, Creative Solutions and Best Software applications. It also provides a wide variety of businesses analyses for nonprofits and sole proprietorships. The company offers free consulting, training and customer support. It's also available in Spanish.

ProfitSystem fx Profit Driver by CCH Tax and Accounting provides a wide range of financial diagnostics and analytics. It provides data in spreadsheet form and can calculate benchmarking against industry standards. The program can track up to 40 periods.

Source : http://ezinearticles.com/?Data-Mining-and-Financial-Data-Analysis&id=2752017

Importance of Data Mining Services in Business

Importance of Data Mining Services in Business

Data mining is used in re-establishment of hidden information of the data of the algorithms. It helps to extract the useful information starting from the data, which can be useful to make practical interpretations for the decision making.
It can be technically defined as automated extraction of hidden information of great databases for the predictive analysis. In other words, it is the retrieval of useful information from large masses of data, which is also presented in an analyzed form for specific decision-making. Although data mining is a relatively new term, the technology is not. It is thus also known as Knowledge discovery in databases since it grip searching for implied information in large databases.
It is primarily used today by companies with a strong customer focus - retail, financial, communication and marketing organizations. It is having lot of importance because of its huge applicability. It is being used increasingly in business applications for understanding and then predicting valuable data, like consumer buying actions and buying tendency, profiles of customers, industry analysis, etc. It is used in several applications like market research, consumer behavior, direct marketing, bioinformatics, genetics, text analysis, e-commerce, customer relationship management and financial services.

However, the use of some advanced technologies makes it a decision making tool as well. It is used in market research, industry research and for competitor analysis. It has applications in major industries like direct marketing, e-commerce, customer relationship management, scientific tests, genetics, financial services and utilities.

Data mining consists of major elements:

    Extract and load operation data onto the data store system.
    Store and manage the data in a multidimensional database system.
    Provide data access to business analysts and information technology professionals.
    Analyze the data by application software.
    Present the data in a useful format, such as a graph or table.

The use of data mining in business makes the data more related in application. There are several kinds of data mining: text mining, web mining, relational databases, graphic data mining, audio mining and video mining, which are all used in business intelligence applications. Data mining software is used to analyze consumer data and trends in banking as well as many other industries.

Outsourcing Web Research offer complete Data Mining Services and Solutions to quickly collective data and information from multiple Internet sources for your Business needs in a cost efficient manner.

Sourec : http://ezinearticles.com/?Importance-of-Data-Mining-Services-in-Business&id=2601221

Data Extraction Services - A Helpful Hand For Large Organization

Data Extraction Services - A Helpful Hand For Large Organization

The data extraction is the way to extract and to structure data from not structured and semi-structured electronic documents, as found on the web and in various data warehouses. Data extraction is extremely useful for the huge organizations which deal with considerable amounts of data, daily, which must be transformed into significant information and be stored for the use this later on.

Your company with tons of data but it is difficult to control and convert the data into useful information. Without right information at the right time and based on half of accurate information, decision makers with a company waste time by making wrong strategic decisions. In high competing world of businesses, the essential statistics such as information customer, the operational figures of the competitor and the sales figures inter-members play a big role in the manufacture of the strategic decisions. It can help you to take strategic business decisions that can shape your business' goals..

Outsourcing companies provide custom made services to the client's requirements. A few of the areas where it can be used to generate better sales leads, extract and harvest product pricing data, capture financial data, acquire real estate data, conduct market research , survey and analysis, conduct product research and analysis and duplicate an online database..

The different types of Data Extraction Services:

    Database Extraction:
    Reorganized data from multiple databases such as statistics about competitor's products, pricing and latest offers and customer opinion and reviews can be extracted and stored as per the requirement of company.

    Web Data Extraction:
    Web Data Extraction is also known as data Extraction which is usually referred to the practice of extract or reading text data from a targeted website.

Businesses have now realized about the huge benefits they can get by outsourcing their services. Then outsourcing is profitable option for business. Since all projects are custom based to suit the exact needs of the customer, huge savings in terms of time, money and infrastructure are among the many advantages that outsourcing brings.

Advantages of Outsourcing Data Extraction Services:

    Improved technology scalability
    Skilled and qualified technical staff who are proficient in English
    Advanced infrastructure resources
    Quick turnaround time
    Cost-effective prices
    Secure Network systems to ensure data safety
    Increased market coverage

By outsourcing, you can definitely increase your competitive advantages. Outsourcing of services helps businesses to manage their data effectively, which in turn would enable them to experience an increase in profits.

Outsourcing Web Research offer complete Data Extraction Services and Solutions to quickly collective data and information from multiple Internet sources for your Business needs in a cost efficient manner. For more info please visit us at: http://www.webscrapingexpert.com/ or directly send your requirements at: info@webscrapingexpert.com

Source:http://ezinearticles.com/?Data-Extraction-Services---A-Helpful-Hand-For-Large-Organization&id=2477589

Increasing Accessibility by Scraping Information From PDF

Increasing Accessibility by Scraping Information From PDF

You may have heard about data scraping which is a method that is being used by computer programs in extracting data from an output that comes from another program. To put it simply, this is a process which involves the automatic sorting of information that can be found on different resources including the internet which is inside an html file, PDF or any other documents. In addition to that, there is the collection of pertinent information. These pieces of information will be contained into the databases or spreadsheets so that the users can retrieve them later.

Most of the websites today have text that can be accessed and written easily in the source code. However, there are now other businesses nowadays that choose to make use of Adobe PDF files or Portable Document Format. This is a type of file that can be viewed by simply using the free software known as the Adobe Acrobat. Almost any operating system supports the said software. There are many advantages when you choose to utilize PDF files. Among them is that the document that you have looks exactly the same even if you put it in another computer so that you can view it. Therefore, this makes it ideal for business documents or even specification sheets. Of course there are disadvantages as well. One of which is that the text that is contained in the file is converted into an image. In this case, it is often that you may have problems with this when it comes to the copying and pasting.

This is why there are some that start scraping information from PDF. This is often called PDF scraping in which this is the process that is just like data scraping only that you will be getting information that is contained in your PDF files. In order for you to begin scraping information from PDF, you must choose and exploit a tool that is specifically designed for this process. However, you will find that it is not easy to locate the right tool that will enable you to perform PDF scraping effectively. This is because most of the tools today have problems in obtaining exactly the same data that you want without personalizing them.

Nevertheless, if you search well enough, you will be able to encounter the program that you are looking for. There is no need for you to have programming language knowledge in order for you to use them. You can easily specify your own preferences and the software will do the rest of the work for you. There are also companies out there that you can contact and they will perform the task since they have the right tools that they can use. If you choose to do things manually, you will find that this is indeed tedious and complicated whereas if you compare this to having professionals do the job for you, they will be able to finish it in no time at all. Scraping information from PDF is a process where you collect the information that can be found on the internet and this does not infringe copyright laws.

Source:http://ezinearticles.com/?Increasing-Accessibility-by-Scraping-Information-From-PDF&id=4593863

Data Discovery vs. Data Extraction

Data Discovery vs. Data Extraction

Looking at screen-scraping at a simplified level, there are two primary stages involved: data discovery and data extraction. Data discovery deals with navigating a web site to arrive at the pages containing the data you want, and data extraction deals with actually pulling that data off of those pages. Generally when people think of screen-scraping they focus on the data extraction portion of the process, but my experience has been that data discovery is often the more difficult of the two.

The data discovery step in screen-scraping might be as simple as requesting a single URL. For example, you might just need to go to the home page of a site and extract out the latest news headlines. On the other side of the spectrum, data discovery may involve logging in to a web site, traversing a series of pages in order to get needed cookies, submitting a POST request on a search form, traversing through search results pages, and finally following all of the "details" links within the search results pages to get to the data you're actually after. In cases of the former a simple Perl script would often work just fine. For anything much more complex than that, though, a commercial screen-scraping tool can be an incredible time-saver. Especially for sites that require logging in, writing code to handle screen-scraping can be a nightmare when it comes to dealing with cookies and such.

In the data extraction phase you've already arrived at the page containing the data you're interested in, and you now need to pull it out of the HTML. Traditionally this has typically involved creating a series of regular expressions that match the pieces of the page you want (e.g., URL's and link titles). Regular expressions can be a bit complex to deal with, so most screen-scraping applications will hide these details from you, even though they may use regular expressions behind the scenes.

As an addendum, I should probably mention a third phase that is often ignored, and that is, what do you do with the data once you've extracted it? Common examples include writing the data to a CSV or XML file, or saving it to a database. In the case of a live web site you might even scrape the information and display it in the user's web browser in real-time. When shopping around for a screen-scraping tool you should make sure that it gives you the flexibility you need to work with the data once it's been extracted.

Source: http://ezinearticles.com/?Data-Discovery-vs.-Data-Extraction&id=165396

Tool Review: WebScraper

Tool Review: WebScraper

Usually when I have any scraping to do I ask Thej  if he can do it and then take a nap. However, Thej is on vacation so I was stuck either waiting for him to come back or I could try to do it myself. It was basic text, not much html, no images, and a few pages, so I went for it with some non coder tools.

I checked the School of Data scraping section for some tools and they have a nice little section on using browser based scraping tools. I did a chrome store search and came across WebScraper.

I glanced through the video sort of paying attention got the gist of it and started to play with the tool.  It took awhile for me to figure out.  I highly recommend very carefully going through the tutorials.  The videos take you through the process but are not very clear for complete newbies like me so it took a few views to understand the hierarchy concept and how to adapt their example to the site I was scraping.

I got the hang of doing one page and then figuring out how to tell it to go to another page, again I had to spend quite a bit of time rewatching the tutorial. At the end of the day I got the data in neat columns in CSV without too much trouble.  I would recommend WebScraper for people who want to do some basic scraping.

It is as visual as you can get though the terminology is still very technical.   You have to do into the developer tools folder which can feel intimidating but ultimately satisfying in the end.

Source: http://schoolofdata.org/2014/10/13/tool-review-webscraper/

Scrape amazon and price your product the right way – A use case

Scrape amazon and price your product the right way – A use case

So you built a product that you want to sell through Amazon.

How do you price your product? 

Amazon is the world’s largest online retailer. Millions of products are sold through amazon.  a lot of people make their living selling through Amazon. One of the biggest mistake people do in Amazon is that they price their product the wrong way. Sometimes they sell overpriced products, sometimes they sell the underpriced product. Both situations are toxic for the business.

We recently worked with a company that helps small businesses sell the products efficiently through amazon and other marketplaces. One of the key things they are doing is helping people with pricing their product the right way.

What I learned from them is that price is a relative term and a lot of people does not understand it. Pricing is a function of the positioning of  your product in the market.

We need to collect the data using  a technique called web scraping to understand how to position the product. You can get the  data in a CSV file which can be used for analysis.

1) What is the average price of a comparable product?

Understanding the pricing  strategy of your competitors products  is the first step in solving the problem. This can give you a range in which you can price your product. You can get the pricing data by scraping amazon

2) Is this a premium product?

People always pay a premium price for a premium product. What makes a product premium? – A product is considered premium only when the customer believe it is worth the price. Excellent marketing and branding are the ways to position your product as a premium product. You can get the relevant data by scraping amazon.

3) What are the problems with your competitor products?

Your competitor products might be having some defects. Or they might not be addressing a relevant problem. You have every chance of success If you are solving a problem that your competitor doesn’t. You can find these problems by analyzing the product reviews of your competitors. You can get review data by scraping amazon.

By analyzing data you can reach at a point where your profit margin looks healthy and pricing looks sensible. Buyers buy the value, not your product. Differentiate your product and position it as a superior product. Give people a reason to buy and that is the only way to succeed.

Source: http://blog.datahut.co/scrape-amazon-and-price-your-product-the-right-way-a-use-case/

Data Mining Process - Why Outsource Data Mining Service?

Data Mining Process - Why Outsource Data Mining Service?

Overview of Data Mining and Process:

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

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

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

* Retrieving Data

* Analyzing Data

* Extracting Data

* Transforming Data

* Loading Data

* Managing Databases

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

Why Outsource Data Online Mining Service?

Outsourcing advantages of data mining services:
o Almost save 60% operating cost
o High quality analysis processes ensuring accuracy levels of almost 99.98%
o Guaranteed risk free outsourcing experience ensured by inflexible information security policies and practices
o Get your project done within a quick turnaround time
o You can measure highly skilled and expertise by taking benefits of Free Trial Program.
o Get the gathered information presented in a simple and easy to access format

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

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

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

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

How Web Scraping Affects your Revenue Growth

How Web Scraping Affects your Revenue Growth

Web scraping is an indispensable resource when it comes to gaining an edge in the competition with the help of business intelligence. As more and more data gets created on the world wide web, the complexity of extracting it intensifies. Web scraping is a technology that demands an extensive tech stack, high end resources and technically skilled labour. Given this resource hungry nature, many businesses prefer outsourcing it to doing the scraping in-house. Here is a brief walk-through of web scraping so that you can get a grip on the whole process and understand how it could affect your revenue growth as a business.

Business intelligence

The competition among online businesses is at its peak. This has more to do with the ready availability of insightful data. When data acquisition at this scale wasn’t possible in the past, businesses made hit-or-miss decisions upon instincts. Now that every activity can be recorded, extracted as data and analysed to arrive at the best business decisions, companies are making the most of it to boost their revenue. This includes monitoring the activity of competitors on social media, price intelligence, sentiment analysis, gathering data for market research and much more. The use cases of web scraping in business is almost infinite. Business intelligence is extremely helpful for the survival of companies in a market that fluctuates often. Implementing a business intelligence strategy powered by web scraping can definitely give a boost to your revenue growth.
Cost centres involved in in-house Web Scraping

Web scraping, despite being a robust solution for extracting data from the web, is not going to be an easy path if your company is not technically rich already. It involves setting up resources like a tech stack and servers that can run the web crawler by a technically skilled team. Following are the primary cost centres involved in the web scraping process.

1. High end servers

Web scraping is a resource intensive process. Considering the importance of uptime here, the crawlers cannot be run on average performance machines. To have the optimum uptime and avoid crashes, the crawler has to be run on high performing servers located in different parts of the world. The quality of servers is crucial to the consistency of the process. Not to mention, these high end servers makeup for a significant amount of the cost involved in web scraping.

2. Technically skilled labour

Scanning through the source code to identify appropriate tags that hold the required data points and creating a program that can automatically fetch these data points from similar pages’ at large scale requires deep programming skills. It goes without saying that employing skilled people would incur cost that could take a hit on your revenue. Ideally, you will need a team of at least 10 to run a web scraping setup in-house.       

3. An extensive tech stack

Although most of the software being used for web scraping are open source, you will find yourself investing in paid software to make certain things easier or faster. Dealing with open source software might not be as user friendly as the paid ones. In any case, having a tech stack with a lot of options is a necessary aspect of web scraping that would incur additional cost.   

4. Maintenance

Building and running the web scraping setup is only half of the story. Since websites undergo changes often, there is a possibility of the crawler setup breaking from time to time. To avoid or solve this at the earliest, a monitoring system that involves both machines and humans is necessary. Monitoring and maintenance contribute to a considerable cost in the web scraping process.
Data as a service

If data for business is your requirement, a better way to acquire it would be to depend on a company that can deliver it via the data as a service route. Web scraping companies have already set up high-end resources required to run the web crawlers that you can utilize to avail web scraping at a much lower cost than what you would incur by doing it on your own. With this, you can also save yourself from the complications and maintenance headache associated with web scraping. Moreover, with a web scraping service, you can enjoy a much higher return on investment owing to the lowered cost of data acquisition. You can use our ROI calculator to compare between the cost of going with an in-house web scraping setup and a hosted solution.

Source: https://www.promptcloud.com/blog/web-scraping-affects-revenue-growth

Data Discovery vs. Data Extraction

Looking at screen-scraping at a simplified level, there are two primary stages involved: data discovery and data extraction. Data discovery deals with navigating a web site to arrive at the pages containing the data you want, and data extraction deals with actually pulling that data off of those pages. Generally when people think of screen-scraping they focus on the data extraction portion of the process, but my experience has been that data discovery is often the more difficult of the two.

The data discovery step in screen-scraping might be as simple as requesting a single URL. For example, you might just need to go to the home page of a site and extract out the latest news headlines. On the other side of the spectrum, data discovery may involve logging in to a web site, traversing a series of pages in order to get needed cookies, submitting a POST request on a search form, traversing through search results pages, and finally following all of the "details" links within the search results pages to get to the data you're actually after. In cases of the former a simple Perl script would often work just fine. For anything much more complex than that, though, a commercial screen-scraping tool can be an incredible time-saver. Especially for sites that require logging in, writing code to handle screen-scraping can be a nightmare when it comes to dealing with cookies and such.

In the data extraction phase you've already arrived at the page containing the data you're interested in, and you now need to pull it out of the HTML. Traditionally this has typically involved creating a series of regular expressions that match the pieces of the page you want (e.g., URL's and link titles). Regular expressions can be a bit complex to deal with, so most screen-scraping applications will hide these details from you, even though they may use regular expressions behind the scenes.

As an addendum, I should probably mention a third phase that is often ignored, and that is, what do you do with the data once you've extracted it? Common examples include writing the data to a CSV or XML file, or saving it to a database. In the case of a live web site you might even scrape the information and display it in the user's web browser in real-time. When shopping around for a screen-scraping tool you should make sure that it gives you the flexibility you need to work with the data once it's been extracted.

Source: http://ezinearticles.com/?Data-Discovery-vs.-Data-Extraction&id=165396   

Web Data Extraction Services and Data Collection Form Website Pages

For any business market research and surveys plays crucial role in strategic decision making. Web scrapping and data extraction techniques help you find relevant information and data for your business or personal use. Most of the time professionals manually copy-paste data from web pages or download a whole website resulting in waste of time and efforts.

Instead, consider using web scraping techniques that crawls through thousands of website pages to extract specific information and simultaneously save this information into a database, CSV file, XML file or any other custom format for future reference.

Examples of web data extraction process include:
• Spider a government portal, extracting names of citizens for a survey
• Crawl competitor websites for product pricing and feature data
• Use web scraping to download images from a stock photography site for website design

Automated Data Collection
Web scraping also allows you to monitor website data changes over stipulated period and collect these data on a scheduled basis automatically. Automated data collection helps you discover market trends, determine user behavior and predict how data will change in near future.

Examples of automated data collection include:
• Monitor price information for select stocks on hourly basis
• Collect mortgage rates from various financial firms on daily basis
• Check whether reports on constant basis as and when required

Using web data extraction services you can mine any data related to your business objective, download them into a spreadsheet so that they can be analyzed and compared with ease.

In this way you get accurate and quicker results saving hundreds of man-hours and money!

With web data extraction services you can easily fetch product pricing information, sales leads, mailing database, competitors data, profile data and many more on a consistent basis.

Source: http://ezinearticles.com/?Web-Data-Extraction-Services-and-Data-Collection-Form-Website-Pages&id=4860417

Easy Web Scraping using PHP Simple HTML DOM Parser Library

Easy Web Scraping using PHP Simple HTML DOM Parser Library

Web scraping is only way to get data from website when  website don’t provide API to access it’s data. Web scraping involves following steps to get data:

    Make request to web page
    Parse/Extract data that you want to scrape from website.
    Store data for final output (excel, csv,mysql database etc).

Web scraping can be implemented in any language like PHP, Java, .Net, Python and any language that allows to make web request to get web page content (HTML text) in to variable. In this article I will show you how to use Simple HTML DOM PHP library to do web scraping using PHP.
PHP Simple HTML DOM Parser

Simple HTML DOM is a PHP library to parse data from webpages, in short you can use this library to do web scraping using PHP and even store data to MySQL database.  Simple HTML DOM has following features:

    The parser library is written in PHP 5+
    It requires PHP 5+ to run
    Parser supports invalid HTML parsing.
    It allows to select html tags like Jquery way.
    Supports Xpath and CSS path based web extraction
    Provides both the way – Object oriented way and procedure way to write code

Scrape All Links

<?php
include "simple_html_dom.php";

//create object
$html=new simple_html_dom();

//load specific URL
$html->load_file("http://www.google.com");

// This will Find all links
foreach($html->find('a') as $element)
   echo $element->href . '<br>';

?>

Scrape images

<?php
include "simple_html_dom.php";

//create object
$html=new simple_html_dom();

//load specific url
$html->load_file("http://www.google.com");

// This will Find all links
foreach($html->find('img') as $element)
   echo $element->src . '<br>';

?>

This is just little idea how you can do web scraping using PHP.Keep in mind that Xpath can make your job simple and fast. You can find all methods available in SimpleHTMLDom documentation page.

Source: http://webdata-scraping.com/web-scraping-using-php-simple-html-dom-parser-library/

Powerful Web Scraping Software – Content Grabber Review

Powerful Web Scraping Software – Content Grabber Review

There are many web scraping software and cloud based web scraping services available in the market for extracting data from the websites. They vary widely in cost and features. In this article, I am going to introduce one such advanced web scraping tool “Content Grabber”, which is widely used and the best web scraping software in the market.

Content Grabber is used for web extraction, web scraping and web automation. It can extract content from complex websites and export it as structured data in a variety of formats like Excel Spreadsheets, XML, CSV and databases. Content Grabber can also extract data from highly dynamic websites. It can extract from AJAX-enabled websites, submit forms repeatedly to cover all possible input values, and manage website logins.

Content Grabber is designed to be reliable, scalable and customizable. It is specifically designed for users with a critical reliance on web scraping and web data extraction. It also enables you to make standalone web scraping agents which you can market and sell as your own royalty free web scraping software.

Applications of Content Grabber:

The following are the few applications of Content Grabber:

•     Data aggregation – for example news aggregation.
•     Competitive pricing and monitoring e.g. monitor dealers for price compliance.
•     Financial and Market Research e.g. Make proactive buying and selling decisions by continuously receiving corporate operational data.
•     Content Integration i.e. integration of data from various sources at one place.
•     Business Directory Scraping – for example: yellow pages scraping, yelp scraping, superpages scraping etc.
•     Extracting company data from yellow pages for scraping common data fields like Business Name, Address, Telephone, Fax, Email, Website and Category of Business.
•     Extracting eBay auction data like: eBay Product Name, Store Information, Buy it Now prices, Product Price, List Price, Seller Price and many more.
•     Extracting Amazon product data: Information such as Product title, cost, description, details, availability, shipping info, ASIN, rating, rank, etc can be extracted.

Content Grabber Features:

The following section highlights some of the key features of Content Grabber:

1. Point and Click Interface

The Content Grabber editor has an easy to use point and click interface that provides easy point and click configuration. One simply needs to click on web elements to configure website navigation and content capture.

2. Easy to Use

The Content Grabber point and click interface is so simple to use that it can easily be used by beginners and non-programmers. There is certain built in facilities that automatically detect and configure all commands. It will automatically create a list of links, lists of content, manage pagination, handle web pages, download or upload files and capture any action you perform on a web page. You can also manually configure the agent commands, so Content Grabber gives you both simplicity and control.

3. Reliable and Scalable

Content Grabber’s powerful features like testing and debugging, solid error handling and error recovery, allows agent to run in the most difficult scenarios. It easily handles and scrapes dynamic websites built with JavaScript and AJAX. Content Grabber’s Intelligent agents don’t break with most site structure changes. These features enable us to build reliable web scraping agents. There are various configurations and performance tuning options that makes Content Grabber scalable. You can build as many web scraping agents as you want with Content Grabber.

4. High Performance

Multi-threading is used to increase the performance in Content Grabber. Content Grabber uses optimized web browsers. It uses static browsers for static web pages and dynamic browsers for dynamic web pages. It has an ultra-fast HTML5 parser for ultra-fast web scraping. One can use many web browsers concurrently to boost performance.

5. Debugging, Logging and Error Handling

Content Grabber has robust support for debugging, error handling and logging. Using a debugger, you can test and debug the web scraping agents which helps you to build reliable and error free web scraping solutions because most of the issues are addressed at design time. Content Grabber allows agent logging with three detail levels: Log URLs, Log raw HTML, Log to database or file. Logs can be useful to identify problems that occurred during execution of a web scraping agent. Content Grabber supports automatic error handling and custom error handling through scripting. Error status reports can also be mailed to administrators.

6. Scripting

Content Grabber comes with a built in script editor with IntelliSense that one can use in case of some unusual requirements or to fine tune some process. Scripting can be used to control agent behaviour, content transformation, customize data export and delivery and to generate data inputs for agent.

7. Unlimited Web Scraping Agents

Content Grabber allows building an unlimited number of Self-Contained Web Scraping Agents. Self-Contained agents are a standalone executable that can be run independently, branded as your own and distributed royalty free. Content Grabber provides an easy to use and effective GUI to manage all the agents. One can view status and logs of all the agents or run and schedule the agents in one centralized location.

8. Automation

Require data on a schedule? Weekly? Everyday? Each hour? Content Grabber allows automating and publishing extracted data. Configure Content Grabber by telling what data you want once, and then schedule it to run automatically.

And much more

There are too many features that Content Grabber provides, but here are a few more that may be useful and interest you.

•     Schedule agents
•     Manage proxies
•     Custom notification criteria and messages
•     Email notifications
•     Handle websites logins
•     Capture Screenshots of web elements or entire web page or save as PDF.
•     Capture hidden content on web page.
•     Crawl entire website
•     Input data from almost any data source.
•     Auto scroll to load dynamic data
•     Handle complex JAVASCRIPT and AJAX actions
•     XPATH support
•     Convert Images to Text
•     CAPTCHA handling
•     Extract data from non-HTML documents like PDF and Word Documents
•     Multi-threading and multiple web browsers
•     Run agent from command line.

The above features come with the Professional edition license. Content Grabber’s Premium edition license is available with the following extra features:

1. Visual Studio 2013 integration

One can integrate Content Grabber to Visual Studio and take advantages of extra powerful script editing, debugging, and unit testing.

2. Remove Content Grabber branding

One can remove Content Grabber branding from the Content Grabber agents and distribute the executable.

3. Custom Design Templates

One can customize the Content Grabber agent user interface design with custom HTML templates – e.g. add your own company branding.

4. Royalty free distribution

One can distribute the Content Grabber agent to anybody without paying royalty fees and can run agents from the command line anywhere.

5. Programming Interface

Programming interfaces like Desktop API, Web API and windows service for building and editing agents.

6. Custom Web Scraping Application Development:

Content Grabber provides API and Visual Studio Integration which developer can use to build custom web scraping applications. It provides full control of the user interface and export functionality. One can develop both Desktop as well as Web based custom web scraping applications using the Content Grabber programming interface. It is a great tool and provides opportunity for developers to build general web scraping applications and sell those to generate revenue.

Are you looking for web scraping services? Do you need any assistance related to Content Grabber? We can probably help you to achieve your scraping-based project goals. We would be more than happy to hear from you.

Source: http://webdata-scraping.com/powerful-web-scraping-software-content-grabber/

Calculate your ROI on Web Scraping using our ROI Calculator

Calculate your ROI on Web Scraping using our ROI Calculator

Staying atop the competition is a vital thing for the survival and growth of businesses these days. Ever since big data came into the picture, web scraping has become something businesses from every industry has to invest in. If your company is not in a technically advanced industry, web scraping could even be a nightmare to start with. Wondering if going with in-house web scraping is right for you? In house or outsourcing, in the end it’s all about the returns on investment.

ROI Calculator

Considering the numerous factors that determine how much web scraping can cost you, it’s not easy to calculate the ROI on your in-house web scraping.

In house web scraping is certainly a challenging process. If you plan on going down this way, here is a brief list of prerequisites.

Engineers

Technically skilled labour is an essential requirement for web scraping. Since, web scraping techniques are complicated, it needs good programming skills to write, run and maintain the scraping bots. The cost of labour can be one of the drawbacks with doing in house web scraping.

Hardware Resources

Web scraping is a resource hungry process which requires high end servers and lots of bandwidth. Without the adequate resources, you might end up losing important data. The cost of quality servers could easily make you want to reconsider doing web scraping on your own. Not to mention the doubling up of these resources in order to keep the data intact, espcially if you’re looking at large scale.

Maintainability and ukeep of your tech stack

Once you have your servers and other technical components setup, the real deal only starts. You have to ensure availability of your servers, data backups, restoring previous states, failovers, among many other complications associated with managing servers and fixing them up when something goes wrong. You need to allocate resources (both people and hardware) to take care of the above.

Time

Time is something that we cannot really include in the equation when it comes to calculating the returns. But it is definitely a factor that defines if web scraping in house is worth it. Although web scraping is the fastest way to acquire data, the initial setup and maintenance are time consuming and complicated. This could easily lead to conflicts when you have to distribute your time between web scraping and other business activities that are crucial for your company.

Try the ROI Calculator

We came up with an ROI calculator to easily calculate your returns on investment with our web scraping services. Using this, you could easily compare the cost of in house web scraping with PromptCloud’s dedicated web scraping services. Find out how much you can save by going the PromptCloud way.

Source: https://www.promptcloud.com/blog/calculate-roi-on-web-scraping

Why is a Web scraping service better than Scraping tools

Why is a Web scraping service better than Scraping tools

Web scraping has been making ripples across various industries in the last few years. Newer businesses can employ web scraping to gain quick market insights and equip themselves to take on their competitors. This works like clockwork if you know how to do the analysis right. Before we jump into that, there is the technical aspect of web scraping. Should your company use a scraping tool to get the required data from the web? Although this sounds like an easy solution, there is more to it than what meets the eye. We explain why it’s better to go with a dedicated web scraping service to cover your data acquisition needs rather than going by the scraping tool route.

Cost is lowered

Although this might come as a surprise, the cost of getting data from employing a data scraping tool along with an IT personnel who can get it done would exceed the cost of a good subscription based web scraping service. Not every company has the necessary resources needed to run web scraping in-house. By depending on a Data service provider, you will save the cost of software, resources and labour required to run web crawling in the firm. Besides, you will also end up having more time and less worries. More of your time and effort can therefore go into the analysis part which is crucial to you as a business owner.

Accessibility is high with a service

Multifaceted websites make it difficult for the scraping tools to extract data. A good web scraping service on the other hand can easily deal with bottlenecks in the scraping process when it may arise. Websites to be scraped often undergo changes in their structure which calls for modification of the crawler accordingly. Unlike a scraping tool, a dedicated service will be able to extract data from complex sites that use Ajax, Javascript and the like. By going with a subscription based service, you are doing yourself the favour of not being involved in this constant headache.

Accuracy in results

A DIY scraping tool might be able to get you data, but the accuracy and relevance of the acquired data will vary. You might be able to get it right with a particular website, but that might not be the case with another. This gives uncertainty to the results of your data acquisition and could even be disastrous for your business. On the other hand, a good scraping service will give you highly refined data which is in a ready to consume form.

Outcomes are instant with a service

Considering the high resource requirements of the web scraping process, your scraping tool is likely to be much slower than a reputed service that has got the right infrastructure and resources to scrape data from the web efficiently. It might not be feasible for your firm to acquire and manage the same setup since that could affect the focus of your business.

Tidying up of Data is an exhausting process

Web scrapers collect data into a dump file which would be huge in size. You will have to do a lot of tidying up in this to get data in a usable format. With the scraping tools route, you would be looking for more tools to clean up the data collected. This is a waste of time and effort that you could use in much better aspects of your business. Whereas with a web scraping service, you won’t have to worry about cleaning up of the data as it comes with the service. You get the data in a plug and use format which gives you more time to do better things.

Many sites have policies for data scraping

Sometimes, websites that you want to scrape data from might have policies discouraging the act. You wouldn’t want to act against their policies being ignorant of their existence and get into legal trouble. With a web scraping service, you don’t have to worry about these. A well-established data scraping provider will definitely follow the rules and policies set by the website. This would mean you can be relieved of such worries and go ahead with finding trends and ideas from the data that they provide.

More time to analyse the data

This is so far the best advantage of going with a scraping service rather than a tool. Since all the things related to data acquisition is dealt by the scraping service provider, you would have more time for analysing and deriving useful business decisions from this data. Being the business owner, analysing the data with care should be your highest priority. Since using a scraping tool to acquire data will cost you more time and effort, the analysis part is definitely going to suffer which defies your whole purpose.

Bottom line

It is up to you to choose between a web scraping tool and a dedicated scraping service. Being the business owner, it i s much better for you to stay away from the technical aspects of web scraping and focus on deriving a better business strategy from the data. When you have made up your mind to go with a data scraping service, it is important to choose the right web scraping service for maximum benefits.

Source: https://www.promptcloud.com/blog/web-scraping-services-better-than-scraping-tools

ERP Data Conversions - Best Practices and Steps

ERP Data Conversions - Best Practices and Steps

Every company who has gone through an ERP project has gone through the painful process of getting the data ready for the new system. The process of executing this typically goes through the following steps:

(1) Extract or define

(2) Clean and transform

(3) Load

(4) Validate and verify

This process is typically executed multiple times (2 - 5+ times depending on complexity) through an ERP project to ensure that the good data ends up in the new system. If the data is either incorrect, not well enough cleaned or adjusted or loaded incorrectly in to the new system it can cause serious problems as the new system is launched.

(1) Extract or define

This involves extracting the data from legacy systems, which are to be decommissioned. In some cases the data may not exist in a legacy system, as the old process may be spreadsheet-based and has to be created from scratch. Typically this involves creating some extraction programs or leveraging existing reports to get the data in to a format which can be put in to a spreadsheet or a data management application.

(2) Data cleansing

Once extracted it normally reviewed is for accuracy by the business, supported by the IT team, and/or adjusted if incorrect or in a structure which the new ERP system does not understand. Depending on the level of change and data quality this can represent a significant effort involving many business stakeholders and required to go through multiple cycles.

(3) Load data to new system

As the data gets structured to a format which the receiving ERP system can handle the load programs may also be build to handle certain changes as part of the process of getting the data converted in to the new system. Data is loaded in to interface tables and loaded in to the new system's core master data and transactions tables.

When loading the data in to the new system the inter-dependency of the different data elements is key to consider and validate the cross dependencies. Exceptions are dealt with and go in to lessons learned and to modify extracts, data cleansing or load process in to the next cycle.

(4) Validate and verify

The final phase of the data conversion process is to verify the converted data through extracts, reports or manually to ensure that all the data went in correctly. This may also include both internal and external audit groups and all the key data owners. Part of the testing will also include attempting to transact using the converted data successfully.

The topmost success factors or best practices to execute a successful conversion I would prioritize as follows:

(1) Start the data conversion early enough by assessing the quality of the data. Starting too late can result in either costly project delays or decisions to load garbage and "deal with it later" resulting in an increase in problems as the new system is launched.

(2) Identify and assign data owners and customers (often forgotten) for the different elements. Ensure that not only the data owners sign-off on the data conversions but that also the key users of the data are involved in reviewing the selection criteria's, data cleansing process and load verification.

(3) Run sufficient enough rounds of testing of the data, including not only validating the loads but also transacting with the converted data.

(4) Depending on the complexity, evaluate possible tools beyond spreadsheets and custom programming to help with the data conversion process for cleansing, transformation and load process.

(5) Don't under-estimate the effort in cleansing and validating the converted data.

(6) Define processes and consider other tools to help how the accuracy of the data will be maintained after the system goes live.

Source: http://ezinearticles.com/?ERP-Data-Conversions---Best-Practices-and-Steps&id=7263314

How to Scrape a Website into Excel without programming

How to Scrape a Website into Excel without programming

This web scraping tutorial will teach you visually step by step how to scrape or extract or pull data from websites using import.io(Free Tool) without programming skills into Excel.

Personally, I use web scraping for analysing my competitors’ best-performing blog posts or content such as what blog posts or content received most comments or social media shares.

In this tutorial,We will scrape the following data from a blog:

    All blog posts URLs.
    Authors names for each post.
    Blog posts titles.
    The number of social media shares each post received.

Then we will use the extracted data to determine what are the popular blog posts and their authors,which posts received much engagement from users through social media shares and on page comments.

Let’s get started.

Step 1:Install import.io app

The first step is to install import.io app.A free web scraping tool and one of the best web scraping software.It is available for Windows,Mac and Linux platforms.Import.io offers advanced data extraction features without coding by allowing you to create custom APIs or crawl entire websites.

After installation, you will need to sign up for an account.It is completely free so don’t worry.I will not cover the installation process.Once everything is set correctly you will see something similar to the window below after your first login.

Step 2:Choose how to scrape data using import.io extractor

With import.io you can do data extraction by creating custom APIs or crawling the entire websites.It comes equipped with different tools for data extraction such as magic,extractor,crawler and connector.

In this tutorial,I will use a tool called “extractor” to create a custom API for our data extraction process.

To get started click the “new” red button on the right top of the page and then click “Start Extractor” button on the pop-up window.

After clicking  “Start Extractor” the Import.io app internal browser window will open as shown below.

Step 3:Data scraping process

Now after the import.io browser is open navigate to the blog URL you want to scrape data from. Then once you already navigated to the target blog URL turn on extraction.In this tutorial,I will use this blog URL bongo5.com  for data extraction.

You can see from the window below I already navigated to www.bongo5.com but extraction switch is still off.

Turn extraction switch “ON” as shown in the window below and move to the next step.

Step 4:Training the “columns” or specifying the data we want to scrape

In this step,I will specify exactly what kind of data I want to scrape from the blog.On import.io app specifying the data you want to scrape is referred to as “training the columns”.Columns represent the data set I want to scrape(post titles,authors’ names and posts URLs).

In order to understand this step, you need to know the difference between a blog page and a blog post.A page might have a single post or multiple posts depending on the blog configuration.

A blog might have several blog posts,even hundreds or thousands of posts.But I will take only one session to train the “extractor” about the data I want to extract.I will do so by using an import.io visual highlighter.Once the data extraction is turned on the-the highlighter will appear by default.

I will do the training session for a single post in a single blog page with multiple posts then the extractor will extract data automatically for the remaining posts on the “same” blog page.
Step 4a:Creating “post_title” column

I will start by renaming “my_column” into the name of the data I want to scrape.Our goal in this tutorial is to scrape the blog posts titles,posts URLs,authors names and get social statistics later so I will create columns for posts titles,posts URLs,authors names.Later on, I will teach you how to get social statistics for the post URLs.

After editing “my_column” into “post_title” then point the mouse cursor over to any of the Posts title on the same blog page and the visual highlighter will automatically appear.Using the highlighter I can select the data I want to extract.

You can see below I selected one of the blog post titles on the page.The rectangular box with orange border is the visual highlighter.

The app will ask you how is the data arranged on the page.Since I have more than one post in a single page then you have rows of repeating data.This blog is having 25 posts per page.So you will select “many rows”.Sometimes you might have a single post on a page for that case you need to select “Just one row”.

Source: http://nocodewebscraping.com/web-scraping-for-dummies-tutorial-with-import-io-without-coding/

Invest in Data Extraction to Grow Your Business

Invest in Data Extraction to Grow Your Business

Automating your employees’ processes can help you increase productivity while keeping the cost of used resources at a minimum. This can help you focus your time and money in much needed areas of your company so that you can thrive in your industry. Data extraction can help you achieve automation by targeting online data sources (websites, blogs, forums, etc) for information and data that can be useful to your business. By using software rather than your employees, you can oftentimes get more accurate data and more thorough information that people may miss. The software can handle the volume that you need and will deliver the results that you desire to help your company.
See the Power of Data Extraction Online

To see all of the ways that data extraction tools and software can benefit your business, visit Connotate.com. There you can read about the features of the software, practical uses for businesses and also schedule a demo before you buy.

Source: http://www.connotate.com/invest-in-data-extraction-to-grow-your-business/

Scraping LinkedIn Public Profiles for Fun and Profit

Scraping LinkedIn Public Profiles for Fun and Profit

Reconnaissance and Information Gathering is a part of almost every penetration testing engagement. Often, the tester will only perform network reconnaissance in an attempt to disclose and learn the company's network infrastructure (i.e. IP addresses, domain names, and etc), but there are other types of reconnaissance to conduct, and no, I'm not talking about dumpster diving. Thanks to social networks like LinkedIn, OSINT/WEBINT is now yielding more information. This information can then be used to help the tester test anything from social engineering to weak passwords.

In this blog post I will show you how to use Pythonect to easily generate potential passwords from LinkedIn public profiles. If you haven't heard about Pythonect yet, it is a new, experimental, general-purpose dataflow programming language based on the Python programming language. Pythonect is most suitable for creating applications that are themselves focused on the "flow" of the data. An application that generates passwords from the employees public LinkedIn profiles of a given company - have a coherence and clear dataflow:

(1) Find all the employees public LinkedIn profiles → (2) Scrap all the employees public LinkedIn profiles → (3) Crunch all the data into potential passwords

Now that we have the general concept and high-level overview out of the way, let's dive in to the details.

Finding all the employees public LinkedIn profiles will be done via Google Custom Search Engine, a free service by Google that allows anyone to create their own search engine by themselves. The idea is to create a search engine that when searching for a given company name - will return all the employees public LinkedIn profiles. How? When creating a Google Custom Search Engine it's possible to refine the search results to a specific site (i.e. 'Sites to search'), and we're going to limit ours to: linkedin.com. It's also possible to fine-tune the search results even further, e.g. uk.linkedin.com to find only employees from United Kingdom.

The access to the newly created Google Custom Search Engine will be made using a free API key obtained from Google API Console. Why go through the Google API? because it allows automation (No CAPTCHA's), and it also means that the search-result pages will be returned as JSON (as oppose to HTML). The only catch with using the free API key is that it's limited to 100 queries per day, but it's possible to buy an API key that will not be limited.

Scraping the profiles is a matter of iterating all over the hCards in all the search-result pages, and extracting the employee name from each hCard. Whats is a hCard? hCard is a micro format for publishing the contact details of people, companies, organizations, and places. hCard is also supported by social networks such as Facebook, Google+, LinkedIn and etc. for exporting public profiles. Google (when indexing) parses hCard, and when relevant, uses them in search-result pages. In other words, when search-result pages include LinkedIn public profiles, it will appear as hCards, and could be easily parsed.

Let's see the implementation of the above:

#!/usr/bin/python
#
# Copyright (C) 2012 Itzik Kotler
#
# scraper.py is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# scraper.py is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with scraper.py.  If not, see <http://www.gnu.org/licenses/>.

"""Simple LinkedIn public profiles scraper that uses Google Custom Search"""

import urllib
import simplejson


BASE_URL = "https://www.googleapis.com/customsearch/v1?key=<YOUR GOOGLE API KEY>&cx=<YOUR GOOGLE SEARCH ENGINE CX>"


def __get_all_hcards_from_query(query, index=0, hcards={}):

    url = query

    if index != 0:

        url = url + '&start=%d' % (index)

    json = simplejson.loads(urllib.urlopen(url).read())

    if json.has_key('error'):

        print "Stopping at %s due to Error!" % (url)

        print json

    else:

        for item in json['items']:

            try:

                hcards[item['pagemap']['hcard'][0]['fn']] = item['pagemap']['hcard'][0]['title']

            except KeyError as e:

                pass

        if json['queries'].has_key('nextPage'):

            return __get_all_hcards_from_query(query, json['queries']['nextPage'][0]['startIndex'], hcards)

    return hcards


def get_all_employees_by_company_via_linkedin(company):

    queries = ['"at %s" inurl:"in"', '"at %s" inurl:"pub"']

    result = {}

    for query in queries:

        _query = query % company

        result.update(__get_all_hcards_from_query(BASE_URL + '&q=' + _query))

    return list(result)

Replace <YOUR GOOGLE API KEY> and <YOUR GOOGLE SEARCH ENGINE CX> in the code above with your Google API Key and Google Search Engine CX respectively, save it to a file called scraper.py, and you're ready!

To kick-start, here is a simple program in Pythonect (that utilizes the scraper module) that searchs and prints all the Pythonect company employees full names:

"Pythonect" -> scraper.get_all_employees_by_company_via_linkedin -> print

The output should be:

Itzik Kotler

In my LinkedIn Profile, I have listed Pythonect as a company that I work for, and since no one else is working there, when searching for all the employees of Pythonect company - only my LinkedIn profile comes up.
For demonstration purposes I will keep using this example (i.e. "Pythonect" company, and "Itzik Kotler" employee), but go ahead and replace Pythonect with other, more popular, companies names and see the results.

Now that we have a working skeleton, let's take its output and start crunching it. Keep in mind that every "password generation forumla" is merely a guess. The examples below are only a sampling of what can be done. There are, obviously many more possibilities and you are encouraged to experiment. But first, let's normalize the output - this way it's going to be consistent before operations are performed on it:

"Pythonect" -> scraper.get_all_employees_by_company_via_linkedin -> string.lower(''.join(_.split()))

The normalization procedure is short and simple: convert the string to lowercase and remove any spaces, and so the output should be now:

itzikkotler

As for data manipulation, out of the box (Thanks to The Python Standard Library) we've got itertools and it's combinatoric generators. Let's start by applying itertools.product:

"Pythonect" -> scraper.get_all_employees_by_company_via_linkedin -> string.lower(''.join(_.split())) -> itertools.product(_, repeat=4) -> print

The code above will generate and print every 4 characters password from the letters: i, t, z, k, o, t, l , e, r. However, it won't cover passwords with uppercase letters in it. And so, here's a simple and straightforward implementation of a cycle_uppercase function that cycles the input letters yields a copy of the input with letter in uppercase:

def cycle_uppercase(i):
    s = ''.join(i)
    for idx in xrange(0, len(s)):
        yield s[:idx] + s[idx].upper() + s[idx+1:]

To use it, save it to a file called itertools2.py, and then simply add it to the Pythonect program after the itertools.product(_, repeat=4) block, as follows:

"Pythonect" -> scraper.get_all_employees_by_company_via_linkedin \
    -> string.lower(''.join(_.split())) \
        -> itertools.product(_, repeat=4) \
            -> itertools2.cycle_uppercase \
                -> print

Now, the program will also cover passwords that include a single uppercase letter in it. Moving on with the data manipulation, sometimes the password might contain symbols that are not found within the scrapped data. In this case, it is necessary to build a generator that will take the input and add symbols to it. Here is a short and simple generator implemented as a Generator Expression:

[_ + postfix for postfix in ['123','!','$']]

To use it, simply add it to the Pythonect program after the itertools2.cycle_uppercase block, as follows:

"Pythonect" -> scraper.get_all_employees_by_company_via_linkedin \
    -> string.lower(''.join(_.split())) \
        -> itertools.product(_, repeat=4) \
            -> itertools2.cycle_uppercase \
                -> [_ + postfix for postfix in ['123','!','$']] \
                    -> print

The result is that now the program adds the strings: '123', '!', and '$' to every generated password, which increases the chances of guessing the user's right password, or not, depends on the password :)

To summarize, it's possible to take OSINT/WEBINT data on a given person or company and use it to generate potential passwords, and it's easy to do with Pythonect. There are, of course, many different ways to manipulate the data into passwords and many programs and filters that can be used. In this aspect, Pythonect being a flow-oriented language makes it easy to experiment and research with different modules and programs in a "plug and play" manner.

Source:http://blog.ikotler.org/2012/12/scraping-linkedin-public-profiles-for.html

Python 3 web-scraping examples with public data

Someone on the NICAR-L listserv asked for advice on the best Python libraries for web scraping. My advice below includes what I did for last spring’s Computational Journalism class, specifically, the Search-Script-Scrape project, which involved 101-web-scraping exercises in Python.

Best Python libraries for web scraping

For the remainder of this post, I assume you’re using Python 3.x, though the code examples will be virtually the same for 2.x. For my class last year, I had everyone install the Anaconda Python distribution, which comes with all the libraries needed to complete the Search-Script-Scrape exercises, including the ones mentioned specifically below:
The best package for general web requests, such as downloading a file or submitting a POST request to a form, is the simply-named requests library (“HTTP for Humans”).

Here’s an overly verbose example:

import requests
base_url = 'http://maps.googleapis.com/maps/api/geocode/json'
my_params = {'address': '100 Broadway, New York, NY, U.S.A',
             'language': 'ca'}
response = requests.get(base_url, params = my_params)
results = response.json()['results']
x_geo = results[0]['geometry']['location']
print(x_geo['lng'], x_geo['lat'])
# -74.01110299999999 40.7079445

For the parsing of HTML and XML, Beautiful Soup 4 seems to be the most frequently recommended. I never got around to using it because it was malfunctioning on my particular installation of Anaconda on OS X.
But I’ve found lxml to be perfectly fine. I believe both lxml and bs4 have similar capabilities – you can even specify lxml to be the parser for bs4. I think bs4 might have a friendlier syntax, but again, I don’t know, as I’ve gotten by with lxml just fine:

import requests
from lxml import html
page = requests.get("http://www.example.com").text
doc = html.fromstring(page)
link = doc.cssselect("a")[0]
print(link.text_content())
# More information...
print(link.attrib['href'])
# http://www.iana.org/domains/example

The standard urllib package also has a lot of useful utilities – I frequently use the methods from urllib.parse. Python 2 also has urllib but the methods are arranged differently.

Here’s an example of using the urljoin method to resolve the relative links on the California state data for high school test scores. The use of os.path.basename is simply for saving the each spreadsheet to your local hard drive:

from os.path import basename
from urllib.parse import urljoin
from lxml import html
import requests
base_url = 'http://www.cde.ca.gov/ds/sp/ai/'
page = requests.get(base_url).text
doc = html.fromstring(page)
hrefs = [a.attrib['href'] for a in doc.cssselect('a')]
xls_hrefs = [href for href in hrefs if 'xls' in href]
for href in xls_hrefs:
  print(href) # e.g. documents/sat02.xls
  url = urljoin(base_url, href)
  with open("/tmp/" + basename(url), 'wb') as f:
    print("Downloading", url)
    # Downloading http://www.cde.ca.gov/ds/sp/ai/documents/sat02.xls
    data = requests.get(url).content
    f.write(data)

And that’s about all you need for the majority of web-scraping work – at least the part that involves reading HTML and downloading files.
Examples of sites to scrape

The 101 scraping exercises didn’t go so great, as I didn’t give enough specifics about what the exact answers should be (e.g. round the numbers? Use complete sentences?) or even where the data files actually were – as it so happens, not everyone Googles things the same way I do. And I should’ve made them do it on a weekly basis, rather than waiting till the end of the quarter to try to cram them in before finals week.

The Github repo lists each exercise with the solution code, the relevant URL, and the number of lines in the solution code.

The exercises run the gamut of simple parsing of static HTML, to inspecting AJAX-heavy sites in which knowledge of the network panel is required to discover the JSON files to grab. In many of these exercises, the HTML-parsing is the trivial part – just a few lines to parse the HTML to dynamically find the URL for the zip or Excel file to download (via requests)…and then 40 to 50 lines of unzipping/reading/filtering to get the answer. That part is beyond what typically considered “web-scraping” and falls more into “data wrangling”.

I didn’t sort the exercises on the list by difficulty, and many of the solutions are not particulary great code. Sometimes I wrote the solution as if I were teaching it to a beginner. But other times I solved the problem using the style in the most randomly bizarre way relative to how I would normally solve it – hey, writing 100+ scrapers gets boring.

But here are a few representative exercises with some explanation:
1. Number of datasets currently listed on data.gov

I think data.gov actually has an API, but this script relies on finding the easiest tag to grab from the front page and extracting the text, i.e. the 186,569 from the text string, "186,569 datasets found". This is obviously not a very robust script, as it will break when data.gov is redesigned. But it serves as a quick and easy HTML-parsing example.
29. Number of days until Texas’s next scheduled execution

Texas’s death penalty site is probably one of the best places to practice web scraping, as the HTML is pretty straightforward on the main landing pages (there are several, for scheduled and past executions, and current inmate roster), which have enough interesting tabular data to collect. But you can make it more complex by traversing the links to collect inmate data, mugshots, and final words. This script just finds the first person on the scheduled list and does some math to print the number of days until the execution (I probably made the datetime handling more convoluted than it needs to be in the provided solution)
3. The number of people who visited a U.S. government website using Internet Explorer 6.0 in the last 90 days

The analytics.usa.gov site is a great place to practice AJAX-data scraping. It’s a very simple and robust site, but either you are aware of AJAX and know how to use the network panel (and in this case, locate ie.json, or you will have no clue how to scrape even a single number on this webpage. I think the difference between static HTML and AJAX sites is one of the tougher things to teach novices. But they pretty much have to learn the difference given how many of today’s websites use both static and dynamically-rendered pages.
6. From 2010 to 2013, the change in median cost of health, dental, and vision coverage for California city employees

There’s actually no HTML parsing if you assume the URLs for the data files can be hard coded. So besides the nominal use of the requests library, this ends up being a data-wrangling exercise: download two specific zip files, unzip them, read the CSV files, filter the dictionaries, then do some math.
90. The currently serving U.S. congressmember with the most Twitter followers

Another example with no HTML parsing, but probably the most complicated example. You have to download and parse Sunlight Foundation’s CSV of Congressmember data to get all the Twitter usernames. Then authenticate with Twitter’s API, then perform mulitple batch lookups to get the data for all 500+ of the Congressional Twitter usernames. Then join the sorted result with the actual Congressmember identity. I probably shouldn’t have assigned this one.
HTML is not necessary

I included no-HTML exercises because there are plenty of data programming exercises that don’t have to deal with the specific nitty-gritty of the Web, such as understanding HTTP and/or HTML. It’s not just that a lot of public data has moved to JSON (e.g. the FEC API) – but that much of the best public data is found in bulk CSV and database files. These files can be programmatically fetched with simple usage of the requests library.

It’s not that parsing HTML isn’t a whole boatload of fun – and being able to do so is a useful skill if you want to build websites. But I believe novices have more than enough to learn from in sorting/filtering dictionaries and lists without worrying about learning how a website works.

Besides analytics.usa.gov, the data.usajobs.gov API, which lists federal job openings, is a great one to explore, because its data structure is simple and the site is robust. Here’s a Python exercise with the USAJobs API; and here’s one in Bash.

There’s also the Google Maps geocoding API, which can be hit up for a bit before you run into rate limits, and you get the bonus of teaching geocoding concepts. The NYTimes API requires creating an account, but you not only get good APIs for some political data, but for content data (i.e. articles, bestselling books) that is interesting fodder for journalism-related analysis.

But if you want to scrape HTML, then the Texas death penalty pages are the way to go, because of the simplicity of the HTML and the numerous ways you can traverse the pages and collect interesting data points. Besides the previously mentioned Texas Python scraping exercise, here’s one for Florida’s list of executions. And here’s a Bash exercise that scrapes data from Texas, Florida, and California and does a simple demographic analysis.

If you want more interesting public datasets – most of which require only a minimal of HTML-parsing to fetch – check out the list I talked about in last week’s info session on Stanford’s Computational Journalism Lab.

Source URL :  http://blog.danwin.com/examples-of-web-scraping-in-python-3-x-for-data-journalists/

How to Avoid the Most Common Traps in Web Scraping?

A lot of industries are successfully using web scraping for creating massive data banks of applicable and actionable data which can be used on every day basis for further business interests as well as offer superior services to the customers. However, web scraping does have its own roadblocks and problems.

Using automated scraping, you could face many common problems. The web scraping spiders or programs present a definite picture to their targeted websites. Then, they use this behavior for making out between the human users as well as web scraping spiders. According to those details, a website can employ a certain web scraping traps for stopping your efforts. Here are some of the most common traps:

How Can You Avoid These Traps?

Some measures, which you can use to make sure that you avoid general web scraping traps include:

• Begin with caching pages, which you already have crawled and make sure that you are not required to load them again.
• Find out if any particular website, which you try to scratch has any particular dislikes towards the web scraping tools.
• Handle scraping in moderate phases as well as take the content required.
• Take things slower and do not overflow the website through many parallel requests, which put strain on the resources.
• Try to minimize the weight on every sole website, which you visit to scrape.
• Use a superior web scraping tool that can save and test data, patterns and URLs.
• Use several IP addresses to scrape efforts or taking benefits of VPN services and proxy servers. It will assist to decrease the dangers of having trapped as well as blacklisted through a website.

Source URL :http://www.3idatascraping.com/category/web-data-scraping

ECJ clarifies Database Directive scope in screen scraping case

EC on the legal protection of databases (Database Directive) in a case concerning the extraction of data from a third party’s website by means of automated systems or software for commercial purposes (so called 'screen scraping').

Flight data extracted

The case, Ryanair Ltd vs. PR Aviation BV, C-30/14, is of interest to a range of companies such as price comparison websites. It stemmed from  Dutch company PR Aviation operation of a website where consumers can search through flight data of low-cost airlines  (including Ryanair), compare prices and, on payment of a commission, book a flight. The relevant flight data is extracted from third-parties’ websites by means of ‘screen scraping’ practices.

Ryanair claimed that PR Aviation’s activity:

• amounted to infringement of copyright (relating to the structure and architecture of the database) and of the so-called sui generis database right (i.e. the right granted to the ‘maker’ of the database where certain investments have been made to obtain, verify, or present the contents of a database) under the Netherlands law implementing the Database Directive;

• constituted breach of contract. In this respect, Ryanair claimed that a contract existed with PR Aviation for the use of its website. Access to the latter requires acceptance, by clicking a box, of the airline’s general terms and conditions which, amongst others, prohibit unauthorized ‘screen scraping’ practices for commercial purposes.

Ryanair asked Dutch courts to prohibit the infringement and order damages. In recent years the company has been engaged in several legal cases against web scrapers across Europe.

The Local Court, Utrecht, and the Court of Appeals of Amsterdam dismissed Ryanair’s claims on different grounds. The Court of Appeals, in particular, cited PR Aviation’s screen scraping of Ryanair’s website as amounting to a “normal use” of said website within the meaning of the lawful user exceptions under Sections 6 and 8 of the Database Directive, which cannot be derogated by contract (Section 15).

Ryanair appealed

Ryanair appealed the decision before the Netherlands Supreme Court (Hoge Raad der Nederlanden), which decided to refer the following question to the ECJ for a preliminary ruling: “Does the application of [Directive 96/9] also extend to online databases which are not protected by copyright on the basis of Chapter II of said directive or by a sui generis right on the basis of Chapter III, in the sense that the freedom to use such databases through the (whether or not analogous) application of Article[s] 6(1) and 8, in conjunction with Article 15 [of Directive 96/9] may not be limited contractually?.”

The ECJ’s ruling

The ECJ (without the need of the opinion of the advocate general) ruled that the Database Directive is not applicable to databases which are not protected either by copyright or by the sui generis database right. Therefore, exceptions to restricted acts set forth by Sections 6 and 8 of the Directive do not prevent the database owner from establishing contractual limitations on its use by third parties. In other words, restrictions to the freedom to contract set forth by the Database Directive do not apply in cases of unprotected databases. Whether Ryanair’s website may be entitled to copyright or sui generis database right protection needs to be determined by the competent national court.

The ECJ’s decision is not particularly striking from a legal standpoint. Yet, it could have a significant impact on the business model of price comparison websites, aggregators, and similar businesses. Owners of databases that could not rely on intellectual property protection may contractually prevent extraction and use (“scraping”) of content from their online databases. Thus, unprotected databases could receive greater protection than the one granted by IP law.

Antitrust implications

However, the lawfulness of contractual restrictions prohibiting access and reuse of data through screen scraping practices should be assessed under an antitrust perspective. In this respect, in 2013 the Court of Milan ruled that Ryanair’s refusal to grant access to its database to the online travel agency Viaggiare S.r.l. amounted to an abuse of dominant position in the downstream market of information and intermediation on flights (decision of June 4, 2013 Viaggiare S.r.l. vs Ryanair Ltd). Indeed, a balance should be struck between the need to compensate the efforts and investments made by the creator of the database with the interest of third parties to be granted with access to information (especially in those cases where the latter are not entitled to copyright protection).

Additionally, web scraping triggers other issues which have not been considered by the ECJ’s ruling. These include, but are not limited to trademark law (i.e., whether the use of a company’s names/logos by the web scraper without consent may amount to trademark infringement), data protection (e.g., in case the scraping involves personal data), or unfair competition.


Source URL :http://yellowpagesdatascraping.blogspot.in/2015/07/ecj-clarifies-database-directive-scope.html