Data Mining - Retrieving Information From Data

Data Mining - Retrieving Information From Data

Data mining definition is the process of retrieving information from data. It has become very important now days because data that is processed is usually kept for future reference and mainly for security purposes in a company. Data transforms is processed into information and it is mostly used in different ways depending on what information one is extracting and from where the person is extracting the information.

It is commonly used in marketing, scientific information and research work, fraud detection and surveillance and many more and most of this work is done using a computer. This definition can come in different terms data snooping, data fishing and data dredging all this refer to data mining but it depends in which department one is. One must know data mining definition so that he can be in a position to make data.

The method of data mining has been there for so many centuries and it is used up to date. There were early methods which were used to identify data mining there are mainly two: regression analysis and bayes theorem. These methods are never used now days because a lot of people have advanced and technology has really changed the entire system.

With the coming up or with the introduction of computers and technology, it becomes very fast and easy to save information. Computers have made work easier and one can be able to expand more knowledge about data crawling and learn on how data is stored and processed through computer science.

Computer science is a course that sharpens one skill and expands more about data crawling and the definition of what data mining means. By studying computer science one can be in a position to know: clustering, support vector machines and decision trees there are some of the units that are found on computer science.

It's all about all this and this knowledge must be applied here. Government institutions, small scale business and supermarkets use data.

The main reason most companies use data mining is because data assist in the collection of information and observations that a company goes through in their daily activity. Such information is very vital in any companies profile and needs to be checked and updated for future reference just in case something happens.

Businesses which use data crawling focus mainly on return of investments, and they are able to know whether they are making a profit or a loss within a very short period. If the company or the business is making a profit they can be in a position to give customers an offer on the product in which they are selling so that the business can be a position to make more profit in an organization, this is very vital in human resource departments it helps in identifying the character traits of a person in terms of job performance.

Most people who use this method believe that is ethically neutral. The way it is being used nowadays raises a lot of questions about security and privacy of its members. Data mining needs good data preparation which can be in a position to uncover different types of information especially those that require privacy.

A very common way in this occurs is through data aggregation.

Data aggregation is when information is retrieved from different sources and is usually put together so that one can be in a position to be analyze one by one and this helps information to be very secure. So if one is collecting data it is vital for one to know the following:

    How will one use the data that he is collecting?
    Who will mine the data and use the data.
    Is the data very secure when am out can someone come and access it.
    How can one update the data when information is needed
    If the computer crashes do I have any backup somewhere.

It is important for one to be very careful with documents which deal with company's personal information so that information cannot easily be manipulated.

source : http://ezinearticles.com/?Data-Mining---Retrieving-Information-From-Data&id=5054887

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

Web Data Extraction Services

Web Data Extraction Services

Web Data Extraction from Dynamic Pages includes some of the services that may be acquired through outsourcing. It is possible to siphon information from proven websites through the use of Data Scrapping software. The information is applicable in many areas in business. It is possible to get such solutions as data collection, screen scrapping, email extractor and Web Data Mining services among others from companies providing websites such as Scrappingexpert.com.

Data mining is common as far as outsourcing business is concerned. Many companies are outsource data mining services and companies dealing with these services can earn a lot of money, especially in the growing business regarding outsourcing and general internet business. With web data extraction, you will pull data in a structured organized format. The source of the information will even be from an unstructured or semi-structured source.

In addition, it is possible to pull data which has originally been presented in a variety of formats including PDF, HTML, and test among others. The web data extraction service therefore, provides a diversity regarding the source of information. Large scale organizations have used data extraction services where they get large amounts of data on a daily basis. It is possible for you to get high accuracy of information in an efficient manner and it is also affordable.

Web data extraction services are important when it comes to collection of data and web-based information on the internet. Data collection services are very important as far as consumer research is concerned. Research is turning out to be a very vital thing among companies today. There is need for companies to adopt various strategies that will lead to fast means of data extraction, efficient extraction of data, as well as use of organized formats and flexibility.

In addition, people will prefer software that provides flexibility as far as application is concerned. In addition, there is software that can be customized according to the needs of customers, and these will play an important role in fulfilling diverse customer needs. Companies selling the particular software therefore, need to provide such features that provide excellent customer experience.

It is possible for companies to extract emails and other communications from certain sources as far as they are valid email messages. This will be done without incurring any duplicates. You will extract emails and messages from a variety of formats for the web pages, including HTML files, text files and other formats. It is possible to carry these services in a fast reliable and in an optimal output and hence, the software providing such capability is in high demand. It can help businesses and companies quickly search contacts for the people to be sent email messages.

It is also possible to use software to sort large amount of data and extract information, in an activity termed as data mining. This way, the company will realize reduced costs and saving of time and increasing return on investment. In this practice, the company will carry out Meta data extraction, scanning data, and others as well.

Source: http://ezinearticles.com/?Web-Data-Extraction-Services&id=4733722

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

An Easy Way For Data Extraction

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.

However, there are some more scraping tolls are available in internet. And also some of esteemed websites are providing the information about these tools. You download these tools by paying a nominal amount.

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

Data Mining Process - Why Outsource Data Mining Service?

Data Mining Process - Why Outsource Data Mining Service?

Overview of Data Mining and Process:

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

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

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

* Retrieving Data

* Analyzing Data

* Extracting Data

* Transforming Data

* Loading Data

* Managing Databases

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

Why Outsource Data Online Mining Service?

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

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

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

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

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

Scraping Yelp Data and How to use?

Scraping Yelp Data and How to use?

We get a lot of requests to scrape data from Yelp. These requests come in on a daily basis, sometimes several times a day. At the same time we have not seen a good business case for a commercial project with scraping Yelp.

We have decided to release a simple example Yelp robot which anyone can run on Chrome inside your computer, tune to your own requirements and collect some data. With this robot you can save business contact information like address, postal code, telephone numbers, website addresses etc.  Robot is placed in our Demo space on Web Robots portal for anyone to use, just sign up, find the robot and use it.

How to use it:

    Sign in to our portal here.
    Download our scraping extension from here.
    Find robot named Yelp_us_demo in the dropdown.
    Modify start URL to the first page of your search results. For example: http://www.yelp.com/search?find_desc=Restaurants&find_loc=Arlington,+VA,+USA
    Click Run.
    Let robot finish it’s job and download data from portal.

Some things to consider:

This robot is placed in our Demo space – therefore it is accessible to anyone. Anyone will be able to modify and run it, anyone will be able to download collected data. Robot’s code may be edited by someone else, but you can always restore it from sample code below. Yelp limits number of search results, so do not expect to scrape more results than you would normally see by search.

In case you want to create your own version of such robot, here it’s full code:

// starting URL above must be the first page of search results.
// Example: http://www.yelp.com/search?find_desc=Restaurants&find_loc=Arlington,+VA,+USA

steps.start = function () {

   var rows = [];

   $(".biz-listing-large").each (function (i,v) {
     if ($("h3 a", v).length > 0)
       {
        var row = {};
        row.company = $(".biz-name", v).text().trim();
        row.reviews =$(".review-count", v).text().trim();
        row.companyLink = $(".biz-name", v)[0].href;
        row.location = $(".secondary-attributes address", v).text().trim();
        row.phone = $(".biz-phone", v).text().trim();
        rows.push (row);
      }
   });

   emit ("yelp", rows);
   if ($(".next").length === 1) {
     next ($(".next")[0].href, "start");
   }
 done();
};

Source: https://webrobots.io/scraping-yelp-data/

Assuring Scraping Success with Proxy Data Scraping

Have you ever heard of "Data Scraping?" Data Scraping is the process of collecting useful data that has been placed in the public domain of the internet (private areas too if conditions are met) and storing it in databases or spreadsheets for later use in various applications. Data Scraping technology is not new and many a successful businessman has made his fortune by taking advantage of data scraping technology.

Sometimes website owners may not derive much pleasure from automated harvesting of their data. Webmasters have learned to disallow web scrapers access to their websites by using tools or methods that block certain ip addresses from retrieving website content. Data scrapers are left with the choice to either target a different website, or to move the harvesting script from computer to computer using a different IP address each time and extract as much data as possible until all of the scraper's computers are eventually blocked.

Thankfully there is a modern solution to this problem. Proxy Data Scraping technology solves the problem by using proxy IP addresses. Every time your data scraping program executes an extraction from a website, the website thinks it is coming from a different IP address. To the website owner, proxy data scraping simply looks like a short period of increased traffic from all around the world. They have very limited and tedious ways of blocking such a script but more importantly -- most of the time, they simply won't know they are being scraped.

You may now be asking yourself, "Where can I get Proxy Data Scraping Technology for my project?" The "do-it-yourself" solution is, rather unfortunately, not simple at all. Setting up a proxy data scraping network takes a lot of time and requires that you either own a bunch of IP addresses and suitable servers to be used as proxies, not to mention the IT guru you need to get everything configured properly. You could consider renting proxy servers from select hosting providers, but that option tends to be quite pricey but arguably better than the alternative: dangerous and unreliable (but free) public proxy servers.

There are literally thousands of free proxy servers located around the globe that are simple enough to use. The trick however is finding them. Many sites list hundreds of servers, but locating one that is working, open, and supports the type of protocols you need can be a lesson in persistence, trial, and error. However if you do succeed in discovering a pool of working public proxies, there are still inherent dangers of using them. First off, you don't know who the server belongs to or what activities are going on elsewhere on the server. Sending sensitive requests or data through a public proxy is a bad idea. It is fairly easy for a proxy server to capture any information you send through it or that it sends back to you. If you choose the public proxy method, make sure you never send any transaction through that might compromise you or anyone else in case disreputable people are made aware of the data.

A less risky scenario for proxy data scraping is to rent a rotating proxy connection that cycles through a large number of private IP addresses. There are several of these companies available that claim to delete all web traffic logs which allows you to anonymously harvest the web with minimal threat of reprisal. Companies such as offer large scale anonymous proxy solutions, but often carry a fairly hefty setup fee to get you going.

Source:http://ezinearticles.com/?Assuring-Scraping-Success-with-Proxy-Data-Scraping&id=248993

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/

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/

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 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

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/