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 For Better Outputs in Your Business

Data Extraction Services For Better Outputs in Your Business

Data Extraction can be defined as the process of retrieving data from an unstructured source in order to process it further or store it. It is very useful for large organizations who deal with large amount of data on a daily basis that need to be processed into meaningful information and stored for later use. The data extraction is a systematic way to extract and structure data from scattered and semi-structured electronic documents, as found on the web and in various data warehouses.

In today's highly competitive business world, vital business information such as customer statistics, competitor's operational figures and inter-company sales figures play an important role in making strategic decisions. By signing on this service provider, you will be get access to critivcal data from various sources like websites, databases, images and documents.

It can help you take strategic business decisions that can shape your business' goals. Whether you need customer information, nuggets into your competitor's operations and figure out your organization's performance, it is highly critical to have data at your fingertips as and when you want it. Your company may be crippled with tons of data and it may prove a headache to control and convert the data into useful information. Data extraction services enable you get data quickly and in the right format.

Few areas where Data Extraction can help you are:

    Capturing financial data
    Generating better sales leads
    Conducting market research, survey and analysis
    Conducting product research and analysis
    Track, extract and harvest product pricing data
    Searching for specific job postings
    Duplicating an online database
    Acquiring real estate data
    Processing auction information
    Searching online newspapers for latest pricing information
    Extracting and summarize news stories from online news sources

Outsourcing companies provide custom made data extraction services to the client's requirements. The different types of data extraction services;

    Web extraction
    Database extraction

Outsourcing is the beneficial option for large organizations seeking to manage large information. Outsourcing this services helps businesses in managing their data effectively, which in turn enables business to experience an increase in profits. By outsourcing, you can certainly increase your competitive edge and save costs too!

This article is courtesy of Web Scraping Expert - an executive at Outsourcing Web Research offer high quality and time bound comprehensive range of data extraction services at affordable rates. 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-For-Better-Outputs-in-Your-Business&id=2760257

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

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/

Tapping The Mining Services Goldmine

Tapping The Mining Services Goldmine

In Australia, resources booms tend to come and go. In a recent speech, Reserve Bank Deputy Governor Ric Battellino identified five major booms over the last two hundred years - from the gold rush of the 1850s, to our current minerals and energy boom.

Many have argued that the current boom is different from anything we've experienced before, with the modernisation of the Chinese and Indian economies likely to keep demand high for decades. That's led some analysts to talk of a resources supercycle. And yet a supercycle is still a cycle.

By definition, cycles are uneven, with commodity prices ebbing and flowing in response to demand, economic conditions and market sentiment. And the share prices of resources companies tend to move with them.

Which raises the question: what's the best way for investors to tap into the potential of the mining boom, without the heart-stopping volatility that mining stocks sometimes deliver?
Invest in the store that sells the spade

Legend has it that the people who really profited from Australia's gold rush weren't the miners who flocked to the fields, but the store-owners who sold them their spades and pans. You can put the same principle to work today by investing in mining services and engineering companies.

Here are five reasons to consider giving mining services companies a place in your portfolio:

1. Growing demand

In November, the Australian Bureau of Agricultural and Resource Economics reported that mining and energy companies plan to invest a record $132.9bn in new projects, a 58% increase from the previous year. That includes 72 projects at an advanced stage of development, such as the $43bn Gorgon LNG project and the $20bn Olympic dam expansion. The mining services sector is poised to benefit from all of them.

The sector also stands to benefit from Australia's worsening skills shortage, with more companies looking to contractors to provide essential services in remote locations.

2. Less volatility

Resource stocks tend to fluctuate with commodity prices, which are subject to international economic forces and market sentiment beyond the control of any individual company. As a result, they are among the most volatile companies on the Australian sharemarket. But mining services stocks, while still exposed to the commodities cycle, tend to be more stable.

3. More predictable cash flow

One reason for the comparative volatility of commodity companies is that their cash flow can be very variable. In the development phase, they need to make significant capital expenditure, often leading to negative cash flows. And while they enjoy healthy revenues in the production phase, that revenue may diminish as a resource is exhausted, unless they make further investments in exploration and development.
In contrast, mining services companies require comparatively little capital investment, with more predictable cash flows over the long-term.

4. Higher dividends

Predictable cash flows and lower capital expenditures often allow services companies to pay out more of their earnings as dividends, making them more appealing for income-oriented investors.

5. No need to pick winners

Many miners are highly leveraged to demand for a single commodity, whether it's gold, coal, copper or iron ore. Some are reliant on a single mine or field. Whereas services companies generally have a more diversified customer base.

Source: http://ezinearticles.com/?Tapping-The-Mining-Services-Goldmine&id=5924837

How to do data scraping from PDF files using PHP?

How to do data scraping from PDF files using PHP?

Situations arise when you want to scrap data from PDF or want to search PDF files for matching text. Suppose you have website where users uploads PDF files and you want to give search functionality to user which searches all uploaded PDF file content for matching text and show all PDFs that contains matching search keywords.

Or you might have all London real estate properties details in PDF report file and you want to quickly grab scrape data from PDF reports then you might need PDF scraping library.

To integrate such functionality to web application is not similar to normal search functionality that we do with database search.

Here is the straight solution for this problem. This involves PDF Data Scraping to plain text and match search terms. I have written this post for the people who want to do PDF data scraping or want to make their PDF files to be Searchable.

We are going to use class named class.pdf2text.php which converts PDF text to into ASCII text, so the class is known for PDF extraction. This PHP class ignores anything in PDF that is not a text.

Let’s see very basic example (Taken from author’s file):

<?php

include "class.pdf2text.php";

$a = new PDF2Text();
$a->setFilename('web-scraping-service.pdf'); //grab the pdf file reside in folder where PHP files resides.

$a->decodePDF();//converts PDF content to text
echo $a->output();

?>

“Web Scraping is a technique using which programmer can automate the copy paste manual work and save the time. This is PDF w eb scraping using PHP. We at Web Data Scraping offer Web Scraping and Data Scraping Service. Vist our website www.webdata-scraping.com”

For more complex extraction you can apply regular expression on the text you get and can parse text that you want from PDF. But keep in mind this has limitation and do not work with all types of PDF extraction.

But the wonderful use of this class is to make utility that allow user to search inside PDF when they search on web search bar. Last but not least, You can also find many PDF scraping software available in market that can do complex scraping from PDF files.

Source: http://webdata-scraping.com/data-scraping-pdf-files-using-php/

Run Code Template – New Feature Added to Fminer Web Scraping Tool

Run Code Template – New Feature Added to Fminer Web Scraping Tool

Fminer is one of the powerful web scraping software, I already given brief of all the Fminer features in previous post. In this post I am going to introduce one of the interesting feature of fminer which is Run Code Template that is recently added to Fminer, this feature is similar to “Fminer Run Code” action but it’s different in a way you can use it. The Run Code Action you can use inside the data scraping flow and python code get executed when scraper start running.

While Run Code Templates are the saved python code snippets that you can run on the data tables after scraping completes. Assume if you get white space in scraped data then you can easily trim this left and right spaces by just executing “strip_column” template, see the code of that template below.

'''Strip all data of a column in data table
Remove the blank of data in the head and the tail.
'''

tabName = '[%table1|data table%]'
colName = '[%table1.column1|table column for strip%]'

tab = tables[tabName]
for i, row in enumerate(tab):
    row[colName] = row[colName].strip()   
    tab.edit_row(i, row)

This template comes with Fminer and few other template like “merge_tables_with_same_columns”.  Below are the steps how you can execute template python code on scraped data.

Step 1: Click on second icon from right that says “Run Code” under the Data section

Step 2: One popup will appear, you need to click on “Templates” icon and choose the template you want to execute and then click on Ok.

Step 3: Now the window will appear for configuration that will ask you to choose the table and column under that table on which you want to execute the code. Now click on Ok again.

Step 4: Now you can see the code of that template, now you can click on execute icon and script will start running, based on number of records it will take time to finish execution.

In many web scraping projects I found this template code very handy for cleaning data and making life easy. Templates are stored at following path so you can create your own template with customized code.

C:\Program Files (x86)\FMiner\templates

I have created one template which I use to remove HTML code that comes while scraping badly organized HTML pages. Below is the code of template for stripping html:

'''Strip HTML will remove all html tags of a column in data table.
'''
import re
tabName = '[%table1|data table%]'
colName = '[%table1.column1|table column for substring%]'
colNew = '[%table1.column1|table column to add new data%]'
tab = tables[tabName]
for i, row in enumerate(tab):
    cleanr =re.compile('<.*?>')
    cleantext = re.sub(cleanr,'', row[colName])
    row[colNew] = cleantext 
    tab.edit_row(i, row)

Stay connected as I am going to post more code templates that will make your web scraping life easy and manipulate data on fly.

Source: http://webdata-scraping.com/run-code-template-new-feature-added-fminer-web-scraping-tool/

Benefits of Ruby over Python & R for Web Scraping

Benefits of Ruby over Python & R for Web Scraping

In this data driven world, you need to be constantly vigilant, as information and key data for an organization keeps changing all the while. If you get the right data at the right time in an efficient manner, you can stay ahead of competition. Hence, web scraping is an essential way of getting the right data. This data is crucial for many organizations, and scraping technique will help them keep an eye on the data and get the information that will benefit them further.

Web scraping involves both crawling the web for data and extracting the data from the page. There are several languages which programmers prefer for web scraping, the top ones are Ruby, Python & R. Each language has its own pros and cons over the other, but if you want the best results and a smooth flow, Ruby is what you should be looking for.

Ruby is very good at production deployments and using Ruby, Redis & Chef have proven to be a great combination. String manipulation in Ruby is very easy because it is based on Perl syntax. Also, Ruby is great for analyzing web pages using  one of the very powerful gems called Nokogiri. Nokogiri is much easier to use as compared to other packages and libraries used by R and Python respectively. Nokogiri can deal with broken HTML / HTML fragments easily. Ruby also has many extensions, such as Sanitize and Loofah, that can help clean up broken HTML.

Python programmers widely use a library called Beautiful Soup for pulling data out of HTML & XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work. R programmers have a new package called rvest that makes it easy to scrape data from html web pages, by libraries like beautiful soup. It is designed to work with magrittr so that you can express complex operations as elegant pipelines composed of simple, easily understood pieces.

To help you understand it more effectively, below is a comprehensive infographic for the same.

Ruby is far ahead of Python & R for cloud development and deployments.  The Ruby Bundler system is just great for managing and deploying packages from Github. Using Chef, you can start up and tear down nodes on EC2, at will, and monitor for failures,  scale up or down, reset your IP addresses, etc. Ruby also has great testing frameworks like Fakeweb and Capybara, making it almost trivial to build a great suite of unit tests and to include advanced features, like crawling  and scraping using webkit / selenium. 

The only disadvantage to Ruby is lack of machine learning and NLP toolkits, making it much harder to emulate the capacity of a tool like Pattern.  It can still be done, however, since most of the heavy lifting can be done asynchronously using Unix tools like liblinear or vowpal wabbit.

Conclusion

Each language has its plus point and you can pick the one which you are most comfortable with. But if you are looking for smooth web scraping experience, then Ruby is the best option. That has been our choice too for years at PromptCloud for the best web scraping results. If you have any further questions about this, then feel free to get in touch with us.

Source: https://www.promptcloud.com/blog/benefits-of-ruby-for-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/

Three Common Methods For Web Data Extraction

Three Common Methods For Web Data Extraction


Probably the most common technique used traditionally to extract data from web pages this is to cook up some regular expressions that match the pieces you want (e.g., URL's and link titles). Our screen-scraper software actually started out as an application written in Perl for this very reason. In addition to regular expressions, you might also use some code written in something like Java or Active Server Pages to parse out larger chunks of text. Using raw regular expressions to pull out the data can be a little intimidating to the uninitiated, and can get a bit messy when a script contains a lot of them. At the same time, if you're already familiar with regular expressions, and your scraping project is relatively small, they can be a great solution.

Other techniques for getting the data out can get very sophisticated as algorithms that make use of artificial intelligence and such are applied to the page. Some programs will actually analyze the semantic content of an HTML page, then intelligently pull out the pieces that are of interest. Still other approaches deal with developing "ontologies", or hierarchical vocabularies intended to represent the content domain.

There are a number of companies (including our own) that offer commercial applications specifically intended to do screen-scraping. The applications vary quite a bit, but for medium to large-sized projects they're often a good solution. Each one will have its own learning curve, so you should plan on taking time to learn the ins and outs of a new application. Especially if you plan on doing a fair amount of screen-scraping it's probably a good idea to at least shop around for a screen-scraping application, as it will likely save you time and money in the long run.

So what's the best approach to data extraction? It really depends on what your needs are, and what resources you have at your disposal. Here are some of the pros and cons of the various approaches, as well as suggestions on when you might use each one:

Raw regular expressions and code

Advantages:

- If you're already familiar with regular expressions and at least one programming language, this can be a quick solution.

- Regular expressions allow for a fair amount of "fuzziness" in the matching such that minor changes to the content won't break them.

- You likely don't need to learn any new languages or tools (again, assuming you're already familiar with regular expressions and a programming language).

- Regular expressions are supported in almost all modern programming languages. Heck, even VBScript has a regular expression engine. It's also nice because the various regular expression implementations don't vary too significantly in their syntax.

Disadvantages:

- They can be complex for those that don't have a lot of experience with them. Learning regular expressions isn't like going from Perl to Java. It's more like going from Perl to XSLT, where you have to wrap your mind around a completely different way of viewing the problem.

- They're often confusing to analyze. Take a look through some of the regular expressions people have created to match something as simple as an email address and you'll see what I mean.

- If the content you're trying to match changes (e.g., they change the web page by adding a new "font" tag) you'll likely need to update your regular expressions to account for the change.

- The data discovery portion of the process (traversing various web pages to get to the page containing the data you want) will still need to be handled, and can get fairly complex if you need to deal with cookies and such.

When to use this approach: You'll most likely use straight regular expressions in screen-scraping when you have a small job you want to get done quickly. Especially if you already know regular expressions, there's no sense in getting into other tools if all you need to do is pull some news headlines off of a site.

Ontologies and artificial intelligence

Advantages:

- You create it once and it can more or less extract the data from any page within the content domain you're targeting.

- The data model is generally built in. For example, if you're extracting data about cars from web sites the extraction engine already knows what the make, model, and price are, so it can easily map them to existing data structures (e.g., insert the data into the correct locations in your database).

- There is relatively little long-term maintenance required. As web sites change you likely will need to do very little to your extraction engine in order to account for the changes.

Disadvantages:

- It's relatively complex to create and work with such an engine. The level of expertise required to even understand an extraction engine that uses artificial intelligence and ontologies is much higher than what is required to deal with regular expressions.

- These types of engines are expensive to build. There are commercial offerings that will give you the basis for doing this type of data extraction, but you still need to configure them to work with the specific content domain you're targeting.

- You still have to deal with the data discovery portion of the process, which may not fit as well with this approach (meaning you may have to create an entirely separate engine to handle data discovery). Data discovery is the process of crawling web sites such that you arrive at the pages where you want to extract data.

When to use this approach: Typically you'll only get into ontologies and artificial intelligence when you're planning on extracting information from a very large number of sources. It also makes sense to do this when the data you're trying to extract is in a very unstructured format (e.g., newspaper classified ads). In cases where the data is very structured (meaning there are clear labels identifying the various data fields), it may make more sense to go with regular expressions or a screen-scraping application.

Screen-scraping software

Advantages:

- Abstracts most of the complicated stuff away. You can do some pretty sophisticated things in most screen-scraping applications without knowing anything about regular expressions, HTTP, or cookies.

- Dramatically reduces the amount of time required to set up a site to be scraped. Once you learn a particular screen-scraping application the amount of time it requires to scrape sites vs. other methods is significantly lowered.

- Support from a commercial company. If you run into trouble while using a commercial screen-scraping application, chances are there are support forums and help lines where you can get assistance.

Disadvantages:

- The learning curve. Each screen-scraping application has its own way of going about things. This may imply learning a new scripting language in addition to familiarizing yourself with how the core application works.

- A potential cost. Most ready-to-go screen-scraping applications are commercial, so you'll likely be paying in dollars as well as time for this solution.

- A proprietary approach. Any time you use a proprietary application to solve a computing problem (and proprietary is obviously a matter of degree) you're locking yourself into using that approach. This may or may not be a big deal, but you should at least consider how well the application you're using will integrate with other software applications you currently have. For example, once the screen-scraping application has extracted the data how easy is it for you to get to that data from your own code?

When to use this approach: Screen-scraping applications vary widely in their ease-of-use, price, and suitability to tackle a broad range of scenarios. Chances are, though, that if you don't mind paying a bit, you can save yourself a significant amount of time by using one. If you're doing a quick scrape of a single page you can use just about any language with regular expressions. If you want to extract data from hundreds of web sites that are all formatted differently you're probably better off investing in a complex system that uses ontologies and/or artificial intelligence. For just about everything else, though, you may want to consider investing in an application specifically designed for screen-scraping.

As an aside, I thought I should also mention a recent project we've been involved with that has actually required a hybrid approach of two of the aforementioned methods. We're currently working on a project that deals with extracting newspaper classified ads. The data in classifieds is about as unstructured as you can get. For example, in a real estate ad the term "number of bedrooms" can be written about 25 different ways. The data extraction portion of the process is one that lends itself well to an ontologies-based approach, which is what we've done. However, we still had to handle the data discovery portion. We decided to use screen-scraper for that, and it's handling it just great. The basic process is that screen-scraper traverses the various pages of the site, pulling out raw chunks of data that constitute the classified ads. These ads then get passed to code we've written that uses ontologies in order to extract out the individual pieces we're after. Once the data has been extracted we then insert it into a database.

Source: http://ezinearticles.com/?Three-Common-Methods-For-Web-Data-Extraction&id=165416

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

Web Scraping Best Practices

Extracting data from the World Wide Web has several challenges as more webmasters are working day and night to lower cases of scraping and crawling of their data in order to survive in the competitive world. There are various other problems you may face when web scraping and most of them can be avoided by adapting and implementing certain web scraping best practices as discussed in this article.

Have knowledge of the scraping tools

Acquiring adequate knowledge of hurdles that may be encountered during web scraping, you will be able to have a smooth web scraping experience and be on the safe side of the law. Conduct a thorough research on the types of tools you will use for scraping and crawling. Firsthand knowledge on these tools will help you find the data you need without being blocked.

Proper proxy software that acts as the middle party works well when you know how to work around HTTP and HTML protocols. Use tools that can change crawling patterns, URLs and data retrieved even when you are crawling on one domain. This will help you abide to the rules and regulations that come with web scraping activities and escaping any legal issues.

Conduct your scraping activities during off-peak hours

You may opt to extract data during times that less people have access for instance over the weekends, during late night hours, public holidays among others. Visiting a website on several instances to retrieve the same type of data is a waste of bandwidth. It is always advisable to download the entire site content to your computer and thereafter you can access it whenever need arises.

Hide your scrapping activities

There is a thin line between ethical and unethical crawling hence you should completely evade being on the top user list of a particular website. Cover up your track as best as you can by making use of proxy IPs to avoid any legal problems. You may also use multiple IP addresses or VPN services to conceal your scrapping activities and lower chances of landing on a website’s blacklist.

Website owners today are very protective of their data and any other information existing under their unique url. Be keen when going through the terms and conditions indicated by websites as they may consider crawling as an infringement of their privacy. Simple etiquette goes a long way. Your web scraping efforts will be fruitful if the site owner supports the idea of sharing data.

Keep record of your activities

Web scraping involves large amount of data.Due to this you may not always remember each and every piece of information you have acquired, gathering statistics will help you monitor your activities.

Load data in phases

Web scraping demands a lot of patience from you when using the crawlers to get needed information. Take the process in a slow manner by loading data one piece at a time. Several parallel request to the same domain can crush the entire site or retrace the scrapping attempts back to your local machine.

Loading data small bits will save you the hustle of scrapping afresh in case that your activity has been interrupted because you will have already stored part of the data required. You can reduce the loading data on an individual domain through various techniques such as caching pages that you have scrapped to escape redundancy occurrences. Use auto throttling mechanisms to increase the amount of traffic to the website and pause for breaks between requests to prevent getting banned.

Conclusion

Through these few mentioned web scraping best practices you will be able to work around website and gather the data required as per clients’ request without major hurdles along the way. The ultimate goal of every web scraper is to be able to access vital information and at the same time remain on the good side of the law.

Source URl : http://nocodewebscraping.com/web-scraping-best-practices/

Web Data Scraping: Practical Uses

Whether in the form of media, text or data in diverse other formats—the internet serves to be a huge storehouse of the world’s information. While browsing for commercial or business needs alike, users are exposed to numerous web pages that contain data in just about every form. Even though access to such data is extremely critical for garnering success in the contemporary world, unfortunately most of it is not open. More often than not, business websites restrict the accessibility options to such data and do not allow visitors to save or display them for reuse on their local storage devices, or onto their own websites.  This is where web data extraction tools come in handy.

Read on for a closer look into some of the common areas of data scraping usage.

• Gathering of data from diverse sources for analysis: In case a business necessitates the collection and analysis of data specific to certain categories from multiple websites, then it helps refer to web data integration experts or those related to the field of data scraping linked with categories like industrial equipment, real estate, automobiles, marketing, business contacts, electronic gadgets and so forth.

• Collection of data in different formats: Different websites are known to publish information and structured data in different formats. So, it may not be possible for organizations to see all the required data a one place, at any given time. Data scrapers allow the extraction of information spanning across multiple pages under various sections, on to a single database or spreadsheet.  This makes it easy for users to analyze (or visualize) the data.

• Helps Research: Data is an important and integral part of all kinds of research – marketing, academic or scientific. A data scraper helps in gathering structured data with ease.

• Market analysis for businesses: Companies that cater to products or services connected to specific domains require comprehensive data of products and services that are of similar kind, and which have a tendency of appearing in the market on a daily basis.

Web scraping software solutions from reputed companies are successful in keeping a constant watch on this kind of data and allow users to get access required information from diverse sources – all at the click of a button.
Go for data extraction to take your business to the next levels of success – you will not be disappointed.

Source URL : http://www.3idatascraping.com/web-data-scraping-practical-uses.php

Web Scraping Services : Making Modern File Formats More Accessible

Data scraping is the process of automatically sorting through information contained on the internet inside html, PDF or other documents and collecting relevant information to into databases and spreadsheets for later retrieval. On most websites, the text is easily and accessibly written in the source code but an increasing number of businesses are using Adobe PDF format (Portable Document Format: A format which can be viewed by the free Adobe Acrobat software on almost any operating system. See below for a link.). The advantage of PDF format is that the document looks exactly the same no matter which computer you view it from making it ideal for business forms, specification sheets, etc.; the disadvantage is that the text is converted into an image from which you often cannot easily copy and paste. PDF Scraping is the process of data scraping information contained in PDF files. To PDF scrape a PDF document, you must employ a more diverse set of tools.

There are two main types of PDF files: those built from a text file and those built from an image (likely scanned in). Adobe's own software is capable of PDF scraping from text-based PDF files but special tools are needed for PDF scraping text from image-based PDF files. The primary tool for PDF scraping is the OCR program. OCR, or Optical Character Recognition, programs scan a document for small pictures that they can separate into letters. These pictures are then compared to actual letters and if matches are found, the letters are copied into a file. OCR programs can perform PDF scraping of image-based PDF files quite accurately but they are not perfect.

Once the OCR program or Adobe program has finished PDF scraping a document, you can search through the data to find the parts you are most interested in. This information can then be stored into your favorite database or spreadsheet program. Some PDF scraping programs can sort the data into databases and/or spreadsheets automatically making your job that much easier.

Quite often you will not find a PDF scraping program that will obtain exactly the data you want without customization. Surprisingly a search on Google only turned up one business, that will create a customized PDF scraping utility for your project. A handful of off the shelf utilities claim to be customizable, but seem to require a bit of programming knowledge and time commitment to use effectively. Obtaining the data yourself with one of these tools may be possible but will likely prove quite tedious and time consuming. It may be advisable to contract a company that specializes in PDF scraping to do it for you quickly and professionally.

Let's explore some real world examples of the uses of PDF scraping technology. A group at Cornell University wanted to improve a database of technical documents in PDF format by taking the old PDF file where the links and references were just images of text and changing the links and references into working clickable links thus making the database easy to navigate and cross-reference. They employed a PDF scraping utility to deconstruct the PDF files and figure out where the links were. They then could create a simple script to re-create the PDF files with working links replacing the old text image.

A computer hardware vendor wanted to display specifications data for his hardware on his website. He hired a company to perform PDF scraping of the hardware documentation on the manufacturers' website and save the PDF scraped data into a database he could use to update his webpage automatically.

PDF Scraping is just collecting information that is available on the public internet. PDF Scraping does not violate copyright laws.

PDF Scraping is a great new technology that can significantly reduce your workload if it involves retrieving information from PDF files. Applications exist that can help you with smaller, easier PDF Scraping projects but companies exist that will create custom applications for larger or more intricate PDF Scraping jobs.

Source URL :  http://yellowpagesdatascraping.blogspot.in/2015/06/web-scraping-services-making-modern.html

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

Beginner’s guide to Web Scraping in Python (using Beautiful Soup)

Introduction

The need and importance of extracting data from the web is becoming increasingly loud and clear. Every few weeks, I find myself in a situation where we need to extract data from the web. For example, last week we were thinking of creating an index of hotness and sentiment about various data science courses available on the internet. This would not only require finding out new courses, but also scrape the web for their reviews and then summarizing them in a few metrics! This is one of the problems / products, whose efficacy depends more on web scrapping and information extraction (data collection) than the techniques used to summarize the data.

Ways to extract information from web

There are several ways to extract information from the web. Use of APIs being probably the best way to extract data from a website. Almost all large websites like Twitter, Facebook, Google, Twitter, StackOverflow provide APIs to access their data in a more structured manner. If you can get what you need through an API, it is almost always preferred approach over web scrapping. This is because if you are getting access to structured data from the provider, why would you want to create an engine to extract the same information.

Sadly, not all websites provide an API. Some do it because they do not want the readers to extract huge information in structured way, while others don’t provide APIs due to lack of technical knowledge. What do you do in these cases? Well, we need to scrape the website to fetch the information.

There might be a few other ways like RSS feeds, but they are limited in their use and hence I am not including them in the discussion here.

What is Web Scraping?

Web scraping is a computer software technique of extracting information from websites. This technique mostly focuses on the transformation of unstructured data (HTML format) on the web into structured data (database or spreadsheet).

You can perform web scrapping in various ways, including use of Google Docs to almost every programming language. I would resort to Python because of its ease and rich eocsystem. It has a library known as ‘Beautiful Soup’ which assists this task. In this article, I’ll show you the easiest way to learn web scraping using python programming.

For those of you, who need a non-programming way to extract information out of web pages, you can also look at import.io . It provides a GUI driven interface to perform all basic web scraping operations. The hackers can continue to read this article!

Libraries required for web scraping

As we know, python is a open source programming language. You may find many libraries to perform one function. Hence, it is necessary to find the best to use library. I prefer Beautiful Soup (python library), since it is easy and intuitive to work on. Precisely, I’ll use two Python modules for scraping data:

Urllib2: It is a Python module which can be used for fetching URLs. It defines functions and classes to help with URL actions (basic and digest authentication, redirections, cookies, etc). For more detail refer to the documentation page.

Beautiful Soup: It is an incredible tool for pulling out information from a webpage. You can use it to extract tables, lists, paragraph and you can also put filters to extract information from web pages. In this article, we will use latest version Beautiful Soup 4. You can look at the installation instruction in its documentation page.

Beautiful Soup does not fetch the web page for us. That’s why, I use urllib2 in combination with the BeautifulSoup library.

Python has several other options for HTML scraping in addition to Beatiful Soup. Here are some others:

    -mechanize
    -scrapemark
    -scrapy

Basics – Get familiar with HTML (Tags)

While performing web scarping, we deal with html tags. Thus, we must have good understanding of them.                      
 you already know basics of HTML, you can skip this section. Below is the basic syntax of HTML:
  This syntax has various tags as elaborated below:

    <!DOCTYPE html> : HTML documents must start with a type declaration
      HTML document is contained between <html> and </html>
      The visible part of the HTML document is between <body> and </body>
       HTML headings are defined with the <h1> to <h6> tags
       HTML paragraphs are defined with the <

Scrapping a web Page using Beautiful Soup

Here, I am scraping data from a Wikipedia page. Our final goal is to extract list of state, union territory capitals in India. And some basic detail like establishment, former capital and others form this wikipedia page. Let’s learn with doing this project step wise step:

Import necessary libraries:

#import the library used to query a website
import urllib2
#specify the url
wiki = "https://en.wikipedia.org/wiki/List_of_state_and_union_territory_capitals_in_India"
#Query the website and return the html to the variable 'page'
page = urllib2.urlopen(wiki)
#import the Beautiful soup functions to parse the data returned from the website
from bs4 import Beautiful Soup
#Parse the html in the 'page' variable, and store it in Beautiful Soup format
soup = Beautiful Soup(page)

Use function “prettify” to look at nested structure of HTML page

Above, you can see that structure of the HTML tags. This will help you to know about different available tags and how can you play with these to extract information.

Work with HTML tags

    soup.<tag>: Return content between opening and closing tag including tag.
    In[30]:soup.title
    Out[30]:<title>List of state and union territory capitals in India - Wikipedia, the free encyclopedia</title>
    soup.<tag>.string: Return string within given tag
    In [38]:soup.title.string
    Out[38]:u'List of state and union territory capitals in India - Wikipedia, the free encyclopedia'

Find all the links within page’s <a> tags::  We know that, we can tag a link using tag “<a>”. So, we should go with option soup.a and it should return the links available in the web page. Let’s do it.

    In [40]:soup.a
    Out[40]:<a id="top"></a>

Above, you can see that, we have only one output. Now to extract all the links within <a>, we will use

Above, it is showing all links including titles, links and other information.  Now to show only links, we need to iterate over each a tag and then return the link using attribute “href” with get.

Find the right table: As we are seeking a table to extract information about state capitals, we should identify the right table first. Let’s write the command to extract information within all table tags.

all_tables=soup.find_all('table')

Now to identify the right table, we will use attribute “class” of table and use it to filter the right table. In chrome, you can check the class name by right click on the required table of web page –> Inspect element –> Copy the class name OR go through the output of above command find the class name of right table.

right_table=soup.find('table', class_='wikitable sortable plainrowheaders')

\right_table

Extract the information to DataFrame: Here, we need to iterate through each row (tr) and then assign each element of tr (td) to a variable and append it to a list. Let’s first look at the HTML structure of the table (I am not going to extract information for table heading <th>)
Above, you can notice that second element of <tr> is within tag <th> not <td> so we need to take care for this. Now to access value of each element, we will use “find(text=True)” option with each element.  Let’s look at the code

#Generate lists

A=[]
B=[]
C=[]
D=[]
E=[]
F=[]
G=[]
for row in right_table.findAll("tr"):

    cells = row.findAll('td')
    states=row.findAll('th') #To store second column data
    if len(cells)==6: #Only extract table body not heading
        A.append(cells[0].find(text=True))
        B.append(states[0].find(text=True))
        C.append(cells[1].find(text=True))
        D.append(cells[2].find(text=True))
        E.append(cells[3].find(text=True))
        F.append(cells[4].find(text=True))
        G.append(cells[5].find(text=True))

#import pandas to convert list to data frame

import pandas as pd
df=pd.DataFrame(A,columns=['Number'])
df['State/UT']=B
df['Admin_Capital']=C
df['Legislative_Capital']=D
df['Judiciary_Capital']=E
df['Year_Capital']=F
df['Former_Capital']=G
df

Similarly, you can perform various other types of web scraping using “Beautiful Soup“. This will reduce your manual efforts to collect data from web pages. You can also look at the other attributes like .parent, .contents, .descendants and .next_sibling, .prev_sibling and various attributes to navigate using tag name. These will help you to scrap the web pages effectively.-

But, why can’t I just use Regular Expressions?

Now, if you know regular expressions, you might be thinking that you can write code using regular expression which can do the same thing for you. I definitely had this question. In my experience with Beautiful Soup and Regular expressions to do same thing I found out:

Code written in Beautiful Soup is usually more robust than the one written using regular expressions. Codes written with regular expressions need to be altered with any changes in pages. Even Beautiful Soup needs that in some cases, it is just that Beautiful Soup is relatively better.

Regular expressions are much faster than Beautiful Soup, usually by a factor of 100 in giving the same outcome.

So, it boils down to speed vs. robustness of the code and there is no universal winner here. If the information you are looking for can be extracted with simple regex statements, you should go ahead and use them. For almost any complex work, I usually recommend BeautifulSoup more than regex.

End Note

In this article, we looked at web scraping methods using “Beautiful Soup” and “urllib2” in Python. We also looked at the basics of HTML and perform the web scraping step by step while solving a challenge. I’d recommend you to practice this and use it for collecting data from web pages.


 Source : http://www.analyticsvidhya.com/blog/2015/10/beginner-guide-web-scraping-beautiful-soup-python/

Web scraping in under 60 seconds: the magic of import.io

This post was written by Rubén Moya, School of Data fellow in Mexico, and originally posted on Escuela de Datos.

Import.io is a very powerful and easy-to-use tool for data extraction that has the aim of getting data from any website in a structured way.
It is meant for non-programmers that need data (and for programmers who don’t want to overcomplicate their lives).

I almost forgot!! Apart from everything, it is also a free tool (o_O)

The purpose of this post is to teach you how to scrape a website and make a dataset and/or API in under 60 seconds. Are you ready?

It’s very simple. You just have to go to http://magic.import.io; post the URL of the site you want to scrape, and push the “GET DATA” button.
Yes! It is that simple! No plugins, downloads, previous knowledge or registration are necessary. You can do this from any browser; it even
works on tablets and smartphones.

For example: if we want to have a table with the information on all items related to Chewbacca on MercadoLibre (a Latin American version
of eBay), we just need to go to that site and make a search – then copy and paste the link (http://listado.mercadolibre.com.mx/chewbacca)
on Import.io, and push the “GET DATA” button.

You’ll notice that now you have all the information on a table, and all you need to do is remove the columns you don’t need. To do this, just
place the mouse pointer on top of the column you want to delete, and an “X” will appear.

Finally, it’s enough for you to click on “download” to get it in a csv file.
In our example, we have 373 pages with 48 articles each. So this option will be very useful for us.

Good news for those of us who are a bit more technically-oriented! There is a button that says “GET API” and this one is good to, well,
generate an API that will update the data on each request. For this you need to create an account (which is also free of cost).

As you saw, we can scrape any website in under 60 seconds, even if it includes tons of results pages. This truly is magic, no? For more
complex things that require logins, entering subwebs, automatized searches, et cetera, there is downloadable import.io software… But I’ll
explain that in a different post.

Source : http://schoolofdata.org/2014/12/09/web-scraping-in-under-60-seconds-the-magic-of-import-io/