How Data Mining is Useful to Companies?

How Data Mining is Useful to Companies?

Every business, organization and government bodies are collecting large amount of data for research and development. Such huge database can make them to have the information on hand when required. But most important is that it takes much time to find important information from the data. "If you want to grow rapidly, you must take quick and accurate decisions to grab timely available opportunities."

By applying the process of data mining, you can easily extract and filter required information from data. It is a processing of refining data and extracting important information. This process is mainly divided into 3 sections; pre-processing, mining and validation. In pre-processing, large amount of relevant data are collected. The mining section includes data classification, clustering, error correction and linking information. The last but important is validate without which you can not make trust on information. In short, data mining is a process of converting data into authentic information.

Let's have look on how data mining is useful to companies.

Fast and Feasible Decisions: To search information from huge bundle of data require more time. It also irritates a person who is doing such. With annoyed mind one can not take accurate decisions that's for sure. By having help of data mining, one can easily get information and make fast decisions. It also helps to compare information with various factors so the decisions become more reliable. Data mining is helpful in every decision to make it quick and feasible.

Powerful Strategies: After data mining, information becomes precise and easy to understand. While making strategies, one can easily analyze information in various dimensions. This analysis helps to get real idea about the strategy implementation. Management bodies can implement powerful strategies effectively to expand business boundaries.

Competitive Advantage: Information is easily available and precise so that one can compare it with competitors' information. It is very much required that you must compare the data otherwise you will have to suffer in business. After doing competitive analysis, one can make corrective decisions to go ahead from competitors. This way company can gain competitive advantage.

Your business can get all the benefits of data mining at cutting rates through outsourcing.

Source : http://ezinearticles.com/?How-Data-Mining-is-Useful-to-Companies?&id=2835042

One of the Main Differences Between Statistical Analysis and Data Mining

One of the Main Differences Between Statistical Analysis and Data Mining

Two methods of analyzing data that are common in both academic and commercial fields are statistical analysis and data mining. While statistical analysis has a long scientific history, data mining is a more recent method of data analysis that has arisen from Computer Science. In this article I want to give an introduction to these methods and outline what I believe is one of the main differences between the two fields of analysis.

Statistical analysis commonly involves an analyst formulating a hypothesis and then testing the validity of this hypothesis by running statistical tests on data that may have been collected for the purpose. For example, if an analyst was studying the relationship between income level and the ability to get a loan, the analyst may hypothesis that there will be a correlation between income level and the amount of credit someone may qualify for.

The analyst could then test this hypothesis with the use of a data set that contains a number of people along with their income levels and the credit available to them. A test could be run that indicates for example that there may be a high degree of confidence that there is indeed a correlation between income and available credit. The main point here is that the analyst has formulated a hypothesis and then used a statistical test along with a data set to provide evidence in support or against that hypothesis.

Data mining is another area of data analysis that has arisen more recently from computer science that has a number of differences to traditional statistical analysis. Firstly, many data mining techniques are designed to be applied to very large data sets, while statistical analysis techniques are often designed to form evidence in support or against a hypothesis from a more limited set of data.

Probably the mist significant difference here, however, is that data mining techniques are not used so much to form confidence in a hypothesis, but rather extract unknown relationships may be present in the data set. This is probably best illustrated with an example. Rather than in the above case where a statistician may form a hypothesis between income levels and an applicants ability to get a loan, in data mining, there is not typically an initial hypothesis. A data mining analyst may have a large data set on loans that have been given to people along with demographic information of these people such as their income level, their age, any existing debts they have and if they have ever defaulted on a loan before.

A data mining technique may then search through this large data set and extract a previously unknown relationship between income levels, peoples existing debt and their ability to get a loan.

While there are quite a few differences between statistical analysis and data mining, I believe this difference is at the heart of the issue. A lot of statistical analysis is about analyzing data to either form confidence for or against a stated hypothesis while data mining is often more about applying an algorithm to a data set to extract previously unforeseen relationships.

Source:http://ezinearticles.com/?One-of-the-Main-Differences-Between-Statistical-Analysis-and-Data-Mining&id=4578250

Data Extraction - A Guideline to Use Scrapping Tools Effectively

Data Extraction - A Guideline to Use Scrapping Tools Effectively

So many people around the world do not have much knowledge about these scrapping tools. In their views, mining means extracting resources from the earth. In these internet technology days, the new mined resource is data. There are so many data mining software tools are available in the internet to extract specific data from the web. Every company in the world has been dealing with tons of data, managing and converting this data into a useful form is a real hectic work for them. If this right information is not available at the right time a company will lose valuable time to making strategic decisions on this accurate information.

This type of situation will break opportunities in the present competitive market. However, in these situations, the data extraction and data mining tools will help you to take the strategic decisions in right time to reach your goals in this competitive business. There are so many advantages with these tools that you can store customer information in a sequential manner, you can know the operations of your competitors, and also you can figure out your company performance. And it is a critical job to every company to have this information at fingertips when they need this information.

To survive in this competitive business world, this data extraction and data mining are critical in operations of the company. There is a powerful tool called Website scraper used in online digital mining. With this toll, you can filter the data in internet and retrieves the information for specific needs. This scrapping tool is used in various fields and types are numerous. Research, surveillance, and the harvesting of direct marketing leads is just a few ways the website scraper assists professionals in the workplace.

Screen scrapping tool is another tool which useful to extract the data from the web. This is much helpful when you work on the internet to mine data to your local hard disks. It provides a graphical interface allowing you to designate Universal Resource Locator, data elements to be extracted, and scripting logic to traverse pages and work with mined data. You can use this tool as periodical intervals. By using this tool, you can download the database in internet to you spread sheets. The important one in scrapping tools is Data mining software, it will extract the large amount of information from the web, and it will compare that date into a useful format. This tool is used in various sectors of business, especially, for those who are creating leads, budget establishing seeing the competitors charges and analysis the trends in online. With this tool, the information is gathered and immediately uses for your business needs.

Another best scrapping tool is e mailing scrapping tool, this tool crawls the public email addresses from various web sites. You can easily from a large mailing list with this tool. You can use these mailing lists to promote your product through online and proposals sending an offer for related business and many more to do. With this toll, you can find the targeted customers towards your product or potential business parents. This will allows you to expand your business in the online market.

There are so many well established and esteemed organizations are providing these features free of cost as the trial offer to customers. If you want permanent services, you need to pay nominal fees. You can download these services from their valuable web sites also.

Source:http://ezinearticles.com/?Data-Extraction---A-Guideline-to-Use-Scrapping-Tools-Effectively&id=3600918

Scraping in PDF Files - Improving Accessibility

Scraping in PDF Files - Improving Accessibility

Scraping of data is one procedure where mechanically information is sorted out that is contained on the Net in HTML, PDF and various other documents. It is also about collecting relevant data and saving it in spreadsheets or databases for retrieval purposes. On a majority of sites, text content can be easily accessed in the source code however a good number of business houses are making use of Portable Document Format. This format had been launched by Adobe and documents in this format can be easily viewed on almost any operating system. Some people convert documents from word to PDF when they need sending files over the Net and many convert PDF to word so that they could edit their documents. The best benefit that one gets for making use of it is that documents look a replica of the original and there is no form of disturbance in viewing them as they appear organized and same on almost all operating systems. The downside of the format is that text in such files is converted into a picture or image and then copying and pasting it is not possible any more.

Scraping in this format is a procedure where data is scraped that is available in such files. Most diverse of the tools is needed in order to carry out scraping in a document that is created in this format. You'd find two main forms of PDF files where one is built from a text file and the other firm is where it is built from some image. There is software brought by Adobe itself which can capably do scraping in text based files. For files that are image-based, there is a need to make use of special application for the task.

OCR program is one primary tool to be used for such a matter. Optical Recognition Program is capable in scanning documents for small picture that can be segregated into letters. The pictures are compared with actual letters and given they match well; the letters get copied into one file. These programs are able to do scraping in an apt way in image-based files pretty much aptly however it cannot be said that they are perfect. Once the procedure is done you could search through data so as to find those areas and parts which you had been looking for. More often than not it is difficult to find a utility that can obtain exact data that is needed without proper customization. But if thoroughly checked, you cou

Source: http://ezinearticles.com/?Scraping-in-PDF-Files---Improving-Accessibility&id=6108439

Web Data Extraction Services and Data Collection Form Website Pages

Web Data Extraction Services and Data Collection Form Website Pages

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

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

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

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

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

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

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

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

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

How Xpath Plays Vital Role In Web Scraping

How Xpath Plays Vital Role In Web Scraping

XPath is a language for finding information in structured documents like XML or HTML. You can say that XPath is (sort of) SQL for XML or HTML files. XPath is used to navigate through elements and attributes in an XML or HTML document.

To understand XPath we must be clear about elements and nodes which are the building blocks of XML and HTML. Let’s talk about them. Here is an example element in an HTML document:

   <a class=”hyperlink” href=http://www.google.com>google</a>

Copy the above text to a file, name it as sample.html and open it in a browser. This will end up as a text link displaying the words “google” and it will take you to www.google.com. For each element there are three main parts: The type, the attributes, andthe text. They are listed below:

 a                                 Type
class,  href                Attributes
google                       Text

Let’s grab some XPath developer tools. I am on Firebug for Firefox or you can use Chrome’s developer tools. We will now form some XPath expressions to extract data from the above element. We will also verify the XPath by using Firebug Console.

For extracting the text “google”:

   //a[@href]/text()   

   //a[@class=”hyperlink”]/text()
 
For extracting the hyperlink i.e. ”www.google.com” :

   //a/@href
//a[@class=”hyperlink”]/@href

That’s all with a single element but in reality, you need to deal with more complex forms.

Let’s proceed to the idea of nodes, and its familial relationship of HTML elements. Look at this example code:

 <div title=”Section1″>

   <table id=”Search”>

       <tr class=”Yahoo”>Yahoo Search</tr>

       <tr class=”Google”>Google Search</tr>

   </table>

</div>

 Notice the </div> at the bottom? That means the table and tr elements are contained within the div. These other elements are considered descendants of the div. The table is a child, and the tr is a grandchild (and so on and so forth). The two tr elements are considered siblings each other. This is vital, as XPath uses these relationships to find your element.

So suppose you want to find the Google item. Any of the following expressions will work:

   //tr[@class=’Google’]
   //div/table/tr[2]
  //div[@title=”Section1″]//tr

So let’s analyze the expressions. We start at the top element (also known as a node). The // means to search all descendants, / means to just look at the current element’s children. So //div means look through all descendants for a div element. The brackets [] specify something about that element. So we can look for an attribute with the @ symbol, or look for text with the text() function. We can chain as many of these together as we can.

Here is a quick reference:

   //             Search all descendant elements
   /              Search all child elements
   []             The predicate (specifies something about the element you are looking for)
   @           Specifies an element attribute. (For example, @title)
   
   .               Specifies the current node (useful when you want to look for an element’s children in the predicate)
   ..              Specifies the parent node
  text()       Gets the text of the element.
   
In the context of web scraping, XPath is a nice tool to have in your belt, as it allows you to write specifications of document locations more flexibly than CSS selectors.

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Source: http://blog.datahut.co/how-xpath-plays-vital-role-in-web-scraping/

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

Web Scraping with Python: A Beginner’s Guide

Web Scraping with Python: A Beginner’s Guide

In the Big Data world, Web Scraping or Data extraction services are the primary requisites for Big Data Analytics. Pulling up data from the web has become almost inevitable for companies to stay in business. Next question that comes up is how to go about web scraping as a beginner.

Data can be extracted or scraped from a web source using a number of methods. Popular websites like Google, Facebook, or Twitter offer APIs to view and extract the available data in a structured manner.  This prevents the use of other methods that may not be preferred by the API provider. However, the demand to scrape a website arises when the information is not readily offered by the website. Python, an open source programming language is often used for Web Scraping due to its simple and rich ecosystem. It contains a library called “BeautifulSoup” which carries on this task. Let’s take a deeper look into web scraping using python.

Setting up a Python Environment:

To carry out web scraping using Python, you will first have to install the Python Environment, which enables to run code written in the python language. The libraries perform data scraping;

Beautiful Soup is a convenient-to-use python library. It is one of the finest tools for extracting information from a webpage. Professionals can scrape information from web pages in the form of tables, lists, or paragraphs. Urllib2 is another library that can be used in combination with the BeautifulSoup library for fetching the web pages. Filters can be added to extract specific information from web pages. Urllib2 is a Python module that can fetch URLs.

For MAC OSX :

To install Python libraries on MAC OSX, users need to open a terminal win and type in the following commands, single command at a time:

sudoeasy_install pip

pip install BeautifulSoup4

pip install lxml

For Windows 7 & 8 users:

Windows 7 & 8 users need to ensure that the python environment gets installed first. Once, the environment is installed, open the command prompt and find the way to root C:/ directory and type in the following commands:

easy_install BeautifulSoup4

easy_installlxml

Once the libraries are installed, it is time to write data scraping code.

Running Python:

Data scraping must be done for a distinct objective such as to scrape current stock of a retail store. First, a web browser is required to navigate the website that contains this data. After identifying the table, right click anywhere on it and then select inspect element from the dropdown menu list. This will cause a window to pop-up on the bottom or side of your screen displaying the website’s html code. The rankings appear in a table. You might need to scan through the HTML data until you find the line of code that highlights the table on the webpage.

Python offers some other alternatives for HTML scraping apart from BeautifulSoup. They include:

    Scrapy
    Scrapemark
    Mechanize

 Web scraping converts unstructured data from HTML code into structured form such as tabular data in an Excel worksheet. Web scraping can be done in many ways ranging from the use of Google Docs to programming languages. For people who do not have any programming knowledge or technical competencies, it is possible to acquire web data by using web scraping services that provide ready to use data from websites of your preference.

HTML Tags:

To perform web scraping, users must have a sound knowledge of HTML tags. It might help a lot to know that HTML links are defined using anchor tag i.e. <a> tag, “<a href=“http://…”>The link needs to be here </a>”. An HTML list comprises <ul> (unordered) and <ol> (ordered) list. The item of list starts with <li>.

HTML tables are defined with<Table>, row as <tr> and columns are divided into data as <td>;

    <!DOCTYPE html> : A HTML document starts with a document type declaration
    The main part of the HTML document in unformatted, plain text is defined by <body> and </body> tags
    The headings in HTML are defined using the heading tags from <h1> to <h5>
    Paragraphs are defined with the <p> tag in HTML
    An entire HTML document is contained between <html> and </html>

Using BeautifulSoup in Scraping:

While scraping a webpage using BeautifulSoup, the main concern is to identify the final objective. For instance, if you would like to extract a list from webpage, a step wise approach is required:

    First and foremost step is to import the required libraries:

 #import the library used to query a website

import urllib2

#specify the url wiki = “https://”

#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 BeautifulSoup

#Parse the html in the ‘page’ variable, and store it in Beautiful Soup format

soup = BeautifulSoup(page)

    Use function “prettify” to visualize nested structure of HTML page
    Working with Soup tags:

Soup<tag> is used for returning content between opening and closing tag including tag.

    In[30]:soup.title

 Out[30]:<title>List of Presidents in India till 2010 – Wikipedia, the free encyclopedia</title>

    soup.<tag>.string: Return string within given tag
    In [38]:soup.title.string
    Out[38]:u ‘List of Presidents in India and Brazil till 2010 in India – Wikipedia, the free encyclopedia’
    Find all the links within page’s <a> tags: Tag a link using tag “<a>”. So, 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>

    Find the right table:

As a table to pull up information about Presidents in India and Brazil till 2010 is being searched for, identifying the right table first is important. Here’s a command to scrape information enclosed in all table tags.

all_tables= soup.find_all(‘table’)

Identify the right table by using attribute “class” of table needs to filter the right table. Thereafter, inspect the class name by right clicking on the required table of web page as follows:

    Inspect element
    Copy the class name or find the class name of right table from the last command’s output.

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

right_table

That’s how we can identify the right table.

    Extract the information to DataFrame: There is a need to iterate through each row (tr) and then assign each element of tr (td) to a variable and add it to a list. Let’s analyse the Table’s HTML structure of the table. (extract information for table heading <th>)

To access value of each element, there is a need to use “find(text=True)” option with each element.  Finally, there is data in dataframe.

There are various other ways to scrape data using “BeautifulSoup” that reduce manual efforts to collect data from web pages. Code written in BeautifulSoup is considered to be more robust than the regular expressions. The web scraping method we discussed use “BeautifulSoup” and “urllib2” libraries in Python. That was a brief beginner’s guide to start using Python for web scraping.

Source: https://www.promptcloud.com/blog/web-scraping-python-guide

Web Scraping with Python: A Beginner’s Guide

Web Scraping with Python: A Beginner’s Guide

In the Big Data world, Web Scraping or Data extraction services are the primary requisites for Big Data Analytics. Pulling up data from the web has become almost inevitable for companies to stay in business. Next question that comes up is how to go about web scraping as a beginner.

Data can be extracted or scraped from a web source using a number of methods. Popular websites like Google, Facebook, or Twitter offer APIs to view and extract the available data in a structured manner.  This prevents the use of other methods that may not be preferred by the API provider. However, the demand to scrape a website arises when the information is not readily offered by the website. Python, an open source programming language is often used for Web Scraping due to its simple and rich ecosystem. It contains a library called “BeautifulSoup” which carries on this task. Let’s take a deeper look into web scraping using python.

Setting up a Python Environment:

To carry out web scraping using Python, you will first have to install the Python Environment, which enables to run code written in the python language. The libraries perform data scraping;

Beautiful Soup is a convenient-to-use python library. It is one of the finest tools for extracting information from a webpage. Professionals can scrape information from web pages in the form of tables, lists, or paragraphs. Urllib2 is another library that can be used in combination with the BeautifulSoup library for fetching the web pages. Filters can be added to extract specific information from web pages. Urllib2 is a Python module that can fetch URLs.

For MAC OSX :

To install Python libraries on MAC OSX, users need to open a terminal win and type in the following commands, single command at a time:

sudoeasy_install pip

pip install BeautifulSoup4

pip install lxml

For Windows 7 & 8 users:

Windows 7 & 8 users need to ensure that the python environment gets installed first. Once, the environment is installed, open the command prompt and find the way to root C:/ directory and type in the following commands:

easy_install BeautifulSoup4

easy_installlxml

Once the libraries are installed, it is time to write data scraping code.

Running Python:

Data scraping must be done for a distinct objective such as to scrape current stock of a retail store. First, a web browser is required to navigate the website that contains this data. After identifying the table, right click anywhere on it and then select inspect element from the dropdown menu list. This will cause a window to pop-up on the bottom or side of your screen displaying the website’s html code. The rankings appear in a table. You might need to scan through the HTML data until you find the line of code that highlights the table on the webpage.

Python offers some other alternatives for HTML scraping apart from BeautifulSoup. They include:

    Scrapy
    Scrapemark
    Mechanize

 Web scraping converts unstructured data from HTML code into structured form such as tabular data in an Excel worksheet. Web scraping can be done in many ways ranging from the use of Google Docs to programming languages. For people who do not have any programming knowledge or technical competencies, it is possible to acquire web data by using web scraping services that provide ready to use data from websites of your preference.

HTML Tags:

To perform web scraping, users must have a sound knowledge of HTML tags. It might help a lot to know that HTML links are defined using anchor tag i.e. <a> tag, “<a href=“http://…”>The link needs to be here </a>”. An HTML list comprises <ul> (unordered) and <ol> (ordered) list. The item of list starts with <li>.

HTML tables are defined with<Table>, row as <tr> and columns are divided into data as <td>;

    <!DOCTYPE html> : A HTML document starts with a document type declaration
    The main part of the HTML document in unformatted, plain text is defined by <body> and </body> tags
    The headings in HTML are defined using the heading tags from <h1> to <h5>
    Paragraphs are defined with the <p> tag in HTML
    An entire HTML document is contained between <html> and </html>

Using BeautifulSoup in Scraping:

While scraping a webpage using BeautifulSoup, the main concern is to identify the final objective. For instance, if you would like to extract a list from webpage, a step wise approach is required:

    First and foremost step is to import the required libraries:

 #import the library used to query a website

import urllib2

#specify the url wiki = “https://”

#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 BeautifulSoup

#Parse the html in the ‘page’ variable, and store it in Beautiful Soup format

soup = BeautifulSoup(page)

    Use function “prettify” to visualize nested structure of HTML page
    Working with Soup tags:

Soup<tag> is used for returning content between opening and closing tag including tag.

    In[30]:soup.title

 Out[30]:<title>List of Presidents in India till 2010 – Wikipedia, the free encyclopedia</title>

    soup.<tag>.string: Return string within given tag
    In [38]:soup.title.string
    Out[38]:u ‘List of Presidents in India and Brazil till 2010 in India – Wikipedia, the free encyclopedia’
    Find all the links within page’s <a> tags: Tag a link using tag “<a>”. So, 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>

    Find the right table:

As a table to pull up information about Presidents in India and Brazil till 2010 is being searched for, identifying the right table first is important. Here’s a command to scrape information enclosed in all table tags.

all_tables= soup.find_all(‘table’)

Identify the right table by using attribute “class” of table needs to filter the right table. Thereafter, inspect the class name by right clicking on the required table of web page as follows:

    Inspect element
    Copy the class name or find the class name of right table from the last command’s output.

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

right_table

That’s how we can identify the right table.

    Extract the information to DataFrame: There is a need to iterate through each row (tr) and then assign each element of tr (td) to a variable and add it to a list. Let’s analyse the Table’s HTML structure of the table. (extract information for table heading <th>)

To access value of each element, there is a need to use “find(text=True)” option with each element.  Finally, there is data in dataframe.

There are various other ways to scrape data using “BeautifulSoup” that reduce manual efforts to collect data from web pages. Code written in BeautifulSoup is considered to be more robust than the regular expressions. The web scraping method we discussed use “BeautifulSoup” and “urllib2” libraries in Python. That was a brief beginner’s guide to start using Python for web scraping.

Source: https://www.promptcloud.com/blog/web-scraping-python-guide

Easy Web Scraping using PHP Simple HTML DOM Parser Library

Easy Web Scraping using PHP Simple HTML DOM Parser Library

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

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

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

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

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

Scrape All Links

<?php
include "simple_html_dom.php";

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

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

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

?>

Scrape images

<?php
include "simple_html_dom.php";

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

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

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

?>

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

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

Powerful Web Scraping Software – Content Grabber Review

Powerful Web Scraping Software – Content Grabber Review

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

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

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

Applications of Content Grabber:

The following are the few applications of Content Grabber:

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

Content Grabber Features:

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

1. Point and Click Interface

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

2. Easy to Use

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

3. Reliable and Scalable

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

4. High Performance

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

5. Debugging, Logging and Error Handling

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

6. Scripting

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

7. Unlimited Web Scraping Agents

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

8. Automation

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

And much more

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

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

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

1. Visual Studio 2013 integration

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

2. Remove Content Grabber branding

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

3. Custom Design Templates

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

4. Royalty free distribution

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

5. Programming Interface

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

6. Custom Web Scraping Application Development:

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

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

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

How Web Scraping for Brand Monitoring is used in Retail Sector

How Web Scraping for Brand Monitoring is used in Retail Sector

Structured or unstructured, business data always plays an instrumental part in driving growth, development, and innovation for your dream venture. Irrespective of industrial sectors or verticals, big data, seems to be of paramount significance for every business or enterprise.

The unsurpassed popularity and increasing importance of big data gave birth to the concept of web scraping, thus enhancing growth opportunities for startups. Large or small, every business establishment will now achieve successful website monitoring and tracking.
How web scraping serves your branding need?

Web scraping helps in extracting unorganized data and ordering it into organized and manageable formats. So if your brand is being talked about in multiple ways (on social media, on expert forums, in comments etc.), you can set the scraping tool algorithm to fetch only data that contains reference about the brand. As an outcome, marketers and business owners around the brand can gauge brand sentiment and tweak their launch marketing campaign to enhance visibility.

Look around and you will discover numerous web scraping solutions ranging from manual to fully automated systems. From Reputation Tracking to Website monitoring, your web scraper can help create amazing insights from seemingly random bits of data (both in structured as well as unstructured format).
Using web scraping

The concept of web scraping revolutionizes the use of big data for business. With its availability across sectors, retailers are on cloud nine. Here’s how the retail market is utilizing the power of Web Scraping for brand monitoring.

Determining pricing strategy

The retail market is filled with competition. Whether it is products or pricing strategies, every retailer competes hard to stay ahead of the growth curve. Web scraping techniques will help you crawl price comparison sites’ pricing data, product descriptions, as well as images to receive data for comparison, affiliation, or analytics.

As a result, retailers will have the opportunity to trade their products at competitive prices, thus increasing profit margins by a whopping 10%.

Tracking online presence

Current trends in ecommerce herald the need for a strong online presence. Web scraping takes cue from this particular aspect, thus scraping reviews and profiles on websites. By providing you a crystal clear picture of product performance, customer behavior, and interactions, web scraping will help you achieve Online Brand Intelligence and monitoring.
Detection of fraudulent reviews

Present-day purchasers have this unique habit of referring to reviews, before finalizing their purchase decisions. Web scraping helps in the identification of opinion-spamming, thus figuring out fake reviews. It will further extend support in detecting, reviewing, streamlining, or blocking reviews, according to your business needs.
Online reputation management

Web data scraping helps in figuring out avenues to take your ORM objectives forward. With the help of the scraped data, you learn about both the impactful as well as vulnerable areas for online reputation management. You will have the web crawler identifying demographic opinions such as age group, gender, sentiments, and GEO location.

Social media analytics

Since social media happens to be one of the most crucial factors for retailers, it will be imperative to Scrape Social Media websites and extract data from Twitter. The web scraping technology will help you watch your brand in Social Media along with fetching Data for social media analytics. With social media channels such as Twitter monitoring services, you will strengthen your firm’s’ branding even more than before.
Advantages of BM

As a business, you might want to monitor your brand in social media to gain deep insights about your brand’s popularity and the current consumer behavior. Brand monitoring companies will watch your brand in social media and come up with crucial data for social media analytics. This process has immense benefits for your business, these are summarized over here –

Locate Infringers

Leading brands often face the challenge thrown by infringers. When brand monitoring companies keep a close look at products available in the market, there is less probability of a copyright infringement. The biggest infringement happens in the packaging, naming and presentation of products. With constant monitoring and legal support provided by the Trademark Law, businesses could remain protected from unethical competitors and illicit business practices.

Manage Consumer Reaction and Competitor’s Challenges

A good business keeps a check on the current consumer sentiment in the targeted demographic and positively manages the same in the interest of their brand. The feedback from your consumers could be affirmative or negative but if you have a hold on the social media channels, web platforms and forums, you, as a brand will be able to propagate trust at all times.

When competitor brands indulge in backbiting or false publicity about your brand, you can easily tame their negative comments by throwing in a positive image in front of your target audience. So, brand monitoring and its active implementation do help in positive image building and management for businesses.
Why Web scraping for BM?

Web scraping for brand monitoring gives you a second pair of eyes to look at your brand as a general consumer. Considering the flowing consumer sentiment in the market during a specific business season, you could correct or simply innovate better ways to mold the target audience in your brand’s favor. Through a systematic approach towards online brand intelligence and monitoring, future business strategies and possible brand responses could be designed, keeping your business actively prepared for both types of scenarios.

For effective web scraping, businesses extract data from Twitter that helps them understand ‘what’s trending’ in their business domain. They also come closer to reality in terms of brand perception, user interaction and brand visibility in the notions of their clientele. Web scraping professionals or companies scrape social media websites to gather relevant data related to your brand or your competitor’s that has the potential to affect your growth as a business. Management and organization of this data is done to extract out significant and reference building facts. Future strategy for your brand is designed by brand monitoring professionals keeping in mind the facts accumulated through web scraping. The data obtained through web scraping helps in –

Knowing the actual brand potential,
Expanding brand coverage,
Devising brand penetration,
Analyzing scope and possibilities for a brand and
Design thoughtful and insightful brand strategies.

In simple words, web scraping provides a business enough base of information that could be used to devise future plans and to make suggestive changes in the current business strategy.

Advantages of Web scraping for BM

Web scraping has made things seamless for businesses involved in managing their brands and active brand monitoring. There is no doubt, that web scraping for brand monitoring comes with immense benefits, some of these are –

Improved customer insight

When you have in hand and factual knowledge about your consumer base through social media channels, you are in a strong position to portray your positive image as a brand. With more realistic data on your hands, you could develop strategies more effectively and make realistic goals for your brand’s improvement. Social media insights also allows marketers to create highly targeted and custom marketing messages – thus leading to better likelihood of sales conversion.

Monitoring your Competition

Web scraping helps you realize where your brand stands in the market among the competition. The actual penetration of your brand in the targeted segment helps in getting a clear picture of your present business scenario. Through careful removal of competition in your concerned business category, you could strengthen your brand image.

Staying Informed

When your brand monitoring team is keeping track of all social media channels, it becomes easier for you to stay informed about latest comments about your business on sites like Facebook, Twitter and social forums etc. You could have deep knowledge about the consumer behavior related to your brand and your competitors on these web destinations.

Improved Consumer Satisfaction and Sales

Reputation tracking done through web scraping helps in generating planned response at times of crisis. It also mends the communication gap between consumer and the brand, hence improving the consumer satisfaction. This automatically translates into trust building and brand loyalty improving your brand’s sales.

To sign off

By granting opportunities to monitor your social media data, web scraping is undoubtedly helping retail businesses take a significant step towards perfect branding. If you are one of the key players in this sector, there’s reason for celebration ahead!

Source: https://www.promptcloud.com/blog/How-Web-Scraping-for-Brand-Monitoring-is-used-in-Retail-Sector

How to use Social Media Scraping to be your Competitors’ Nightmare

How to use Social Media Scraping to be your Competitors’ Nightmare

Big data and competitive intelligence have been in the limelight for quite some time now. The almost magical power of big data to help a company make just the right decisions have been talked about a lot. When it comes to big data, the kind of benefits that a business can get totally depends upon the sources they acquire it from. Social media is one of the best sources from where you can get data that helps your business in a multitude of ways. Now that every business is deep rooted on the internet, social media data becomes all the more relevant and crucial. Here is how you can use data scraped from social media sites to get an edge in the competition.

Keeping watch on your competitors

Social media is the best place to watch your competitors’ activity and take counter initiatives to keep up or take over them. If you want to know what your competitors are up to, a social media scraping setup for scraping the posts that mention your competitors’ brand/product names can do the trick. This can also be used to learn a thing or two from their activities on social media so that you can take respective measures to stay ahead of them. For example, you could know if your competitor is running a special promotional offer at the moment and come up with something better than theirs to keep up. This can do wonders if you are in a highly competitive industry like Ecommerce where the competition is intense. If you are not using some help from web scraping technology to keep a close watch on your competitors, you could easily get left over in this fast-paced business scene.

Solving customer issues at the earliest

Customers are vocal about their experience with different products and services on social media sites these days. If you have a customer whose issue was left unsolved, there is a good chance that he/she will take it to the social media to vent the frustration. Watching out for such instances and giving them prompt support should be something you should do if you want to retain these customers and stop them from ruining your brand’s image. By scraping social media sites for posts that mention your product/service, you can easily find out if there are such grievances from customers. This can make sure to an extent that you don’t let unhappy customers stay that way, which eventually hurts your business in the long run. Customers can make or break your company, so using social media scraping to serve the customers better can help you succeed eventually.

Sentiment analysis

Social media data can play a good job at helping you understand user sentiments. With the help of social media scraping, a business can get the big picture about general perception of their brand by their users. This can go a long way since this level of feedback can help you fix unnoticed issues with your company and service quickly. By rectifying them, you can make your brand more appealing to the customers. Sentiment analysis will provide you with the opportunity to transform your business into how customers want it to be. Social media scraping is the one and only way to have access to this user sentiment data which can help you optimize your business for the customers.

Web crawling for social media data

When social media data possess so much value to businesses, it makes sense to look for efficient ways to gather and use this data. Manually scrolling through millions of tweets doesn’t make sense, this is why you should use social media scraping to aggregate the relevant data for your business. Besides, web scraping technologies make it possible to handle huge amounts of data with ease. Since the size of data is huge when it comes to business related requirements, web scraping is the only scalable solution worth considering. To make things even simpler, there are reliable web scraping solutions that offer social media scraping services for brand monitoring.

Bottom line

Since social media has become an integral part of online businesses, the data available on these sites possess immense value to companies in every industry. Social media scraping can be used for brand monitoring and gaining competitive intelligence that can be used to optimize your business model for maximum effectiveness. This will in turn make your company stand out from the competition and the added advantage of insights gained from social media data will help you to take over your competitors.

Source: https://www.promptcloud.com/blog/social-media-scraping-for-competitive-intelligence

Business Intelligence & Data Warehousing in a Business Perspective

Business Intelligence & Data Warehousing in a Business Perspective

Business Intelligence

Business Intelligence has become a very important activity in the business arena irrespective of the domain due to the fact that managers need to analyze comprehensively in order to face the challenges.

Data sourcing, data analysing, extracting the correct information for a given criteria, assessing the risks and finally supporting the decision making process are the main components of BI.

In a business perspective, core stakeholders need to be well aware of all the above stages and be crystal clear on expectations. The person, who is being assigned with the role of Business Analyst (BA) for the BI initiative either from the BI solution providers' side or the company itself, needs to take the full responsibility on assuring that all the above steps are correctly being carried out, in a way that it would ultimately give the business the expected leverage. The management, who will be the users of the BI solution, and the business stakeholders, need to communicate with the BA correctly and elaborately on their expectations and help him throughout the process.

Data sourcing is an initial yet crucial step that would have a direct impact on the system where extracting information from multiple sources of data has to be carried out. The data may be on text documents such as memos, reports, email messages, and it may be on the formats such as photographs, images, sounds, and they can be on more computer oriented sources like databases, formatted tables, web pages and URL lists. The key to data sourcing is to obtain the information in electronic form. Therefore, typically scanners, digital cameras, database queries, web searches, computer file access etc, would play significant roles. In a business perspective, emphasis should be placed on the identification of the correct relevant data sources, the granularity of the data to be extracted, possibility of data being extracted from identified sources and the confirmation that only correct and accurate data is extracted and passed on to the data analysis stage of the BI process.

Business oriented stake holders guided by the BA need to put in lot of thought during the analyzing stage as well, which is the second phase. Synthesizing useful knowledge from collections of data should be done in an analytical way using the in-depth business knowledge whilst estimating current trends, integrating and summarizing disparate information, validating models of understanding, and predicting missing information or future trends. This process of data analysis is also called data mining or knowledge discovery. Probability theory, statistical analysis methods, operational research and artificial intelligence are the tools to be used within this stage. It is not expected that business oriented stake holders (including the BA) are experts of all the above theoretical concepts and application methodologies, but they need to be able to guide the relevant resources in order to achieve the ultimate expectations of BI, which they know best.

Identifying relevant criteria, conditions and parameters of report generation is solely based on business requirements, which need to be well communicated by the users and correctly captured by the BA. Ultimately, correct decision support will be facilitated through the BI initiative and it aims to provide warnings on important events, such as takeovers, market changes, and poor staff performance, so that preventative steps could be taken. It seeks to help analyze and make better business decisions, to improve sales or customer satisfaction or staff morale. It presents the information that manager's need, as and when they need it.

In a business sense, BI should go several steps forward bypassing the mere conventional reporting, which should explain "what has happened?" through baseline metrics. The value addition will be higher if it can produce descriptive metrics, which will explain "why has it happened?" and the value added to the business will be much higher if predictive metrics could be provided to explain "what will happen?" Therefore, when providing a BI solution, it is important to think in these additional value adding lines.

Data warehousing

In the context of BI, data warehousing (DW) is also a critical resource to be implemented to maximize the effectiveness of the BI process. BI and DW are two terminologies that go in line. It has come to a level where a true BI system is ineffective without a powerful DW, in order to understand the reality behind this statement, it's important to have an insight in to what DW really is.

A data warehouse is one large data store for the business in concern which has integrated, time variant, non volatile collection of data in support of management's decision making process. It will mainly have transactional data which would facilitate effective querying, analyzing and report generation, which in turn would give the management the required level of information for the decision making.

The reasons to have BI together with DW

At this point, it should be made clear why a BI tool is more effective with a powerful DW. To query, analyze and generate worthy reports, the systems should have information available. Importantly, transactional information such as sales data, human resources data etc. are available normally in different applications of the enterprise, which would obviously be physically held in different databases. Therefore, data is not at one particular place, hence making it very difficult to generate intelligent information.

The level of reports expected today, are not merely independent for each department, but managers today want to analyze data and relationships across the enterprise so that their BI process is effective. Therefore, having data coming from all the sources to one location in the form of a data warehouse is crucial for the success of the BI initiative. In a business viewpoint, this message should be passed and sold to the managements of enterprises so that they understand the value of the investment. Once invested, its gains could be achieved over several years, in turn marking a high ROI.

Investment costs for a DW in the short term may look quite high, but it's important to re-iterate that the gains are much higher and it will span over many years to come. It also reduces future development cost since with the DW any requested report or view could be easily facilitated. However, it is important to find the right business sponsor for the project. He or she needs to communicate regularly with executives to ensure that they understand the value of what's being built. Business sponsors need to be decisive, take an enterprise-wide perspective and have the authority to enforce their decisions.

Process

Implementation of a DW itself overlaps with some phases of the above explained BI process and it's important to note that in a process standpoint, DW falls in to the first few phases of the entire BI initiative. Gaining highly valuable information out of DW is the latter part of the BI process. This can be done in many ways. DW can be used as the data repository of application servers that run decision support systems, management Information Systems, Expert systems etc., through them, intelligent information could be achieved.

But one of the latest strategies is to build cubes out of the DW and allow users to analyze data in multiple dimensions, and also provide with powerful analytical supporting such as drill down information in to granular levels. Cube is a concept that is different to the traditional relational 2-dimensional tabular view, and it has multiple dimensions, allowing a manager to analyze data based on multiple factors, and not just two factors. On the other hand, it allows the user to select whatever the dimension he wish to choose for analyzing purposes and not be limited by one fixed view of data, which is called as slice & dice in DW terminology.

BI for a serious enterprise is not just a phase of a computerization process, but it is one of the major strategies behind the entire organizational drivers. Therefore management should sit down and build up a BI strategy for the company and identify the information they require in each business direction within the enterprise. Given this, BA needs to analyze the organizational data sources in order to build up the most effective DW which would help the strategized BI process.

High level Ideas on Implementation

At the heart of the data warehousing process is the extract, transform, and load (ETL) process. Implementation of this merely is a technical concern but it's a business concern to make sure it is designed in such a way that it ultimately helps to satisfy the business requirements. This process is responsible for connecting to and extracting data from one or more transactional systems (source systems), transforming it according to the business rules defined through the business objectives, and loading it into the all important data model. It is at this point where data quality should be gained. Of the many responsibilities of the data warehouse, the ETL process represents a significant portion of all the moving parts of the warehousing process.

Creation of a powerful DW depends on the correctness of data modeling, which is the responsibility of the database architect of the project, but BA needs to play a pivotal role providing him with correct data sources, data requirements and most importantly business dimensions. Business Dimensional modeling is a special method used for DW projects and this normally should be carried out by the BA and from there onwards technical experts should take up the work. Dimensions are perspectives specific to a business that could be used for analysis purposes. As an example, for a sales database, the dimensions could include Product, Time, Store, etc. Obviously these dimensions differ from one business to another and hence for each DW initiative those dimensions should be correctly identified and that could be very well done by a person who has experience in the DW domain and understands the business as well, making it apparent that DW BA is the person responsible.

Each of the identified dimensions would be turned in to a dimension table at the implementation phase, and the objective of the above explained ETL process is to fill up these dimension tables, which in turn will be taken to the level of the DW after performing some more database activities based on a strong underlying data model. Implementation details are not important for a business stakeholder but being aware of high level process to this level is important so that they are also on the same pitch as that of the developers and can confirm that developers are actually doing what they are supposed to do and would ultimately deliver what they are supposed to deliver.

Security is also vital in this regard, since this entire effort deals with highly sensitive information and identification of access right to specific people to specific information should be correctly identified and captured at the requirements analysis stage.

Advantages

There are so many advantages of BI system. More presentation of analytics directly to the customer or supply chain partner will be possible. Customer scores, customer campaigns and new product bundles can all be produced from analytic structures resulting in high customer retention and creation of unique products. More collaboration within information can be achieved from effective BI. Rather than middle managers getting great reports and making their own areas look good, information will be conveyed into other functions and rapidly shared to create collaborative decisions increasing the efficiency and accuracy. The return on human capital will be greatly increased.

Managers at all levels will save their time on data analysis, and hence saving money for the enterprise, as the time of managers is equal to money in a financial perspective. Since powerful BI would enable monitoring internal processes of the enterprises more closely and allow making them more efficient, the overall success of the organization would automatically grow. All these would help to derive a high ROI on BI together with a strong DW. It is a common experience to notice very high ROI figures on such implementations, and it is also important to note that there are many non-measurable gains whilst we consider most of the measurable gains for the ROI calculation. However, at a stage where it is intended to take the management buy-in for the BI initiative, it's important to convert all the non measurable gains in to monitory values as much as possible, for example, saving of managers time can be converted in to a monitory value using his compensation.

The author has knowledge in both Business and IT. Started career as a Software Engineer and moved to work in the business analysis area of a premier US based software company.

Source: http://ezinearticles.com/?Business-Intelligence-and-Data-Warehousing-in-a-Business-Perspective&id=35640

Difference between Data Mining and KDD

Difference between Data Mining and KDD

Data, in its raw form, is just a collection of things, where little information might be derived. Together with the development of information discovery methods(Data Mining and KDD), the value of the info is significantly improved.

Data mining is one among the steps of Knowledge Discovery in Databases(KDD) as can be shown by the image below.KDD is a multi-step process that encourages the conversion of data to useful information. Data mining is the pattern extraction phase of KDD. Data mining can take on several types, the option influenced by the desired outcomes.

Knowledge Discovery in Databases Steps
Data Selection

KDD isn’t prepared without human interaction. The choice of subset and the data set requires knowledge of the domain from which the data is to be taken. Removing non-related information elements from the dataset reduces the search space during the data mining phase of KDD. The sample size and structure are established during this point, if the dataset can be assessed employing a testing of the info.
Pre-processing

Databases do contain incorrect or missing data. During the pre-processing phase, the information is cleaned. This warrants the removal of “outliers”, if appropriate; choosing approaches for handling missing data fields; accounting for time sequence information, and applicable normalization of data.
Transformation

Within the transformation phase attempts to reduce the variety of data elements can be assessed while preserving the quality of the info. During this stage, information is organized, changed in one type to some other (i.e. changing nominal to numeric) and new or “derived” attributes are defined.
Data mining

Now the info is subjected to one or several data-mining methods such as regression, group, or clustering. The information mining part of KDD usually requires repeated iterative application of particular data mining methods. Different data-mining techniques or models can be used depending on the expected outcome.
Evaluation

The final step is documentation and interpretation of the outcomes from the previous steps. Steps during this period might consist of returning to a previous step up the KDD approach to help refine the acquired knowledge, or converting the knowledge in to a form clear for the user.In this stage the extracted data patterns are visualized for further reviews.
Conclusion

Data mining is a very crucial step of the KDD process.

For further reading aboud KDD and data mining ,please check this link.

Source: http://nocodewebscraping.com/difference-data-mining-kdd/

Data Mining vs Screen-Scraping

Data Mining vs Screen-Scraping

Data mining isn't screen-scraping. I know that some people in the room may disagree with that statement, but they're actually two almost completely different concepts.

In a nutshell, you might state it this way: screen-scraping allows you to get information, where data mining allows you to analyze information. That's a pretty big simplification, so I'll elaborate a bit.

The term "screen-scraping" comes from the old mainframe terminal days where people worked on computers with green and black screens containing only text. Screen-scraping was used to extract characters from the screens so that they could be analyzed. Fast-forwarding to the web world of today, screen-scraping now most commonly refers to extracting information from web sites. That is, computer programs can "crawl" or "spider" through web sites, pulling out data. People often do this to build things like comparison shopping engines, archive web pages, or simply download text to a spreadsheet so that it can be filtered and analyzed.

Data mining, on the other hand, is defined by Wikipedia as the "practice of automatically searching large stores of data for patterns." In other words, you already have the data, and you're now analyzing it to learn useful things about it. Data mining often involves lots of complex algorithms based on statistical methods. It has nothing to do with how you got the data in the first place. In data mining you only care about analyzing what's already there.

The difficulty is that people who don't know the term "screen-scraping" will try Googling for anything that resembles it. We include a number of these terms on our web site to help such folks; for example, we created pages entitled Text Data Mining, Automated Data Collection, Web Site Data Extraction, and even Web Site Ripper (I suppose "scraping" is sort of like "ripping"). So it presents a bit of a problem-we don't necessarily want to perpetuate a misconception (i.e., screen-scraping = data mining), but we also have to use terminology that people will actually use.

Source: http://ezinearticles.com/?Data-Mining-vs-Screen-Scraping&id=146813

Tips for scraping business directories

Tips for scraping business directories

Are you looking to scrape business directories to generate leads?

Here are a few tips for scraping business directories.

Web scraping is not rocket science. But there are good and bad and worst ways of doing it.

Generating sales qualified leads is always a headache. The old school ways are to buy a list from sites like Data.com. But they are quite expensive.

Scraping business directories can help generate sales qualified leads. The following tips can help you scrape data from business directories efficiently.

1) Choose a good framework to write the web scrapers. This can help save a lot of time and trouble. Python Scrapy is our favourite, but there are other non-pythonic frameworks too.

2) The business directories might be having anti-scraping mechanisms. You have to use IP rotating services to do the scrape. Using IP rotating services, crawl with multiple changing IP addresses which can cover your tracks.

3) Some sites really don’t want you to scrape and they will block the bot. In these cases, you may need to disguise your web scraper as a human being. Browser automation tools like selenium can help you do this.

4) Web sites will update their data quite often. The scraper bot should be able to update the data according to the changes. This is a hard task and you need professional services to do that.

One of the easiest ways to generate leads is to scrape from business directories and use enrich them. We made Leadintel for lead research and enrichment.

Source: http://blog.datahut.co/tips-for-scraping-business-directories/

How to Avoid the Most Common Traps in Web Scraping?

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

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

How Can You Avoid These Traps?

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

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

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

ECJ clarifies Database Directive scope in screen scraping case

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

Flight data extracted

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

Ryanair claimed that PR Aviation’s activity:

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

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

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

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

Ryanair appealed

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

The ECJ’s ruling

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

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

Antitrust implications

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

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


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

An Easy Way For Data Extraction

There are so many data scraping tools are available in internet. With these tools you can you download large amount of data without any stress. From the past decade, the internet revolution has made the entire world as an information center. You can obtain any type of information from the internet. However, if you want any particular information on one task, you need search more websites. If you are interested in download all the information from the websites, you need to copy the information and pate in your documents. It seems a little bit hectic work for everyone. With these scraping tools, you can save your time, money and it reduces manual work.

The Web data extraction tool will extract the data from the HTML pages of the different websites and compares the data. Every day, there are so many websites are hosting in internet. It is not possible to see all the websites in a single day. With these data mining tool, you are able to view all the web pages in internet. If you are using a wide range of applications, these scraping tools are very much useful to you.

The data extraction software tool is used to compare the structured data in internet. There are so many search engines in internet will help you to find a website on a particular issue. The data in different sites is appears in different styles. This scraping expert will help you to compare the date in different site and structures the data for records.

And the web crawler software tool is used to index the web pages in the internet; it will move the data from internet to your hard disk. With this work, you can browse the internet much faster when connected. And the important use of this tool is if you are trying to download the data from internet in off peak hours. It will take a lot of time to download. However, with this tool you can download any data from internet at fast rate.There is another tool for business person is called email extractor. With this toll, you can easily target the customers email addresses. You can send advertisement for your product to the targeted customers at any time. This the best tool to find the database of the customers.

 Source  URL : http://ezinearticles.com/?An-Easy-Way-For-Data-Extraction&id=3517104