Outsource Data Mining Services to Offshore Data Entry Company

Companies in India offer complete solution services for all type of data mining services.

Data Mining Services and Web research services offered, help businesses get critical information for their analysis and marketing campaigns. As this process requires professionals with good knowledge in internet research or online research, customers can take advantage of outsourcing their Data Mining, Data extraction and Data Collection services to utilize resources at a very competitive price.

In the time of recession every company is very careful about cost. So companies are now trying to find ways to cut down cost and outsourcing is good option for reducing cost. It is essential for each size of business from small size to large size organization. Data entry is most famous work among all outsourcing work. To meet high quality and precise data entry demands most corporate firms prefer to outsource data entry services to offshore countries like India.

In India there are number of companies which offer high quality data entry work at cheapest rate. Outsourcing data mining work is the crucial requirement of all rapidly growing Companies who want to focus on their core areas and want to control their cost.

Why outsource your data entry requirements?

Easy and fast communication: Flexibility in communication method is provided where they will be ready to talk with you at your convenient time, as per demand of work dedicated resource or whole team will be assigned to drive the project.

Quality with high level of Accuracy: Experienced companies handling a variety of data-entry projects develop whole new type of quality process for maintaining best quality at work.

Turn Around Time: Capability to deliver fast turnaround time as per project requirements to meet up your project deadline, dedicated staff(s) can work 24/7 with high level of accuracy.

Affordable Rate: Services provided at affordable rates in the industry. For minimizing cost, customization of each and every aspect of the system is undertaken for efficiently handling work.

Outsourcing Service Providers are outsourcing companies providing business process outsourcing services specializing in data mining services and data entry services. Team of highly skilled and efficient people, with a singular focus on data processing, data mining and data entry outsourcing services catering to data entry projects of a varied nature and type.

Why outsource data mining services?

360 degree Data Processing Operations
Free Pilots Before You Hire
Years of Data Entry and Processing Experience
Domain Expertise in Multiple Industries
Best Outsourcing Prices in Industry
Highly Scalable Business Infrastructure
24X7 Round The Clock Services

The expertise management and teams have delivered millions of processed data and records to customers from USA, Canada, UK and other European Countries and Australia.

Outsourcing companies specialize in data entry operations and guarantee highest quality & on time delivery at the least expensive prices.


Source: http://ezinearticles.com/?Outsource-Data-Mining-Services-to-Offshore-Data-Entry-Company&id=4027029

Data Mining and Financial Data Analysis

Introduction:

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

Objective:

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

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

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

Data mining:

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

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

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

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

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

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

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

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

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

Data Warehouse:

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

Text:

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

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

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

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

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

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

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

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

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

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

The Products:

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

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

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

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

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

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

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


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

A Cheaper and Effective Solution For Spanish Data Entry Projects

With Spanish being spoken by more than 400 million people in 22 countries around the world, the need for Spanish Data Entry Services is growing constantly. While most businesses have in-house service providers for their Spanish Data entry projects, this proves to be both expensive and time consuming. A cheaper and better alternative would be to Outsource Spanish Data Entry projects to India.
Indian Outsourcing companies offering Spanish data entry services employ experienced and certified Spanish language experts who are well versed and fluent in Spanish. In order to ensure that the highest quality of service is provided, outsourcing companies follow a specified four step process that is listed below

o All data to be entered is captured using OCR (optical character recognition), ICR (intelligent character recognition), MICR (magnetic ink character recognition) and barcode recognition systems in order to minimize mistakes and maximize speed.

o Any additional data that could not be captured in the previous stage is typed out and verified. The captured data is then evaluated by validation and verification experts who check each and every word and mark out any inconsistencies that may appear in the language.

o A certified Spanish language expert proofreads the entire document and cross-checks it with the original. This is done to make sure that there are no errors.

o The processed data is then formatted, arranged and indexed and sent to the client as per their specific requirements.

Being in the foreign language data entry and transcription industry for more than a decade, Indian companies have the required expertise and skill that is needed to see a project through completion. Apart from Spanish data entry services, Spanish transcription support is also offered if needed. A few of the services that are offered by the outsourcing companies are

o Spanish data entry from hard copies to digital web-based systems

o Spanish data entry from hard/soft copy to any format

o Spanish business document and web-based indexing

o Spanish survey forms entry

o Spanish publications data entry

o Custom data export/import and interfaces with audits

o Data Cleansing of databases in Spanish

o Web Extraction and Data Mining in Spanish

o Creation and Maintenance of Directory Services in Spanish

o Spanish Data Capture and Document Imaging

o Spanish data entry through OCR from images

o Spanish Website Language Translation

With the huge saving that businesses make (sometimes up to 50%), they are able to shift their valuable time, energy and resources towards other core competencies. Indian outsourcing companies are also backed by hi-tech and reliable infrastructure and secure networks in order to ensure data safety. Outsourcing Spanish Data Entry Services to India will give businesses the added benefits of

o Cost-effective pricing

o Certified Spanish language experts

o Stringent quality checks

o Round the clock customer support

o Computer-assisted data capture

o State-of-the -art technology

o Quick turn around time

o Secure and safe networks

The reasonable prices, fast turn around time and high level of data accuracy have made India the choice destination for oversees clients in Spain, Latin America, Mexico, Europe and the United States. By outsourcing to India, they stand to gain a much cheaper and more effective solution for their Spanish data entry projects.



Source: http://ezinearticles.com/?A-Cheaper-and-Effective-Solution-For-Spanish-Data-Entry-Projects&id=1558394

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

Top Data Mining Tools

Data mining is important because it means pulling out critical information from vast amounts of data. The key is to find the right tools used for the expressed purposes of examining data from any number of viewpoints and effectively summarize it into a useful data set.

Many of the tools used to organize this data have become computer based and are typically referred to as knowledge discovery tools.

Listed below are the top data mining tools in the industry:

    Insightful Miner - This tool has the best selection of ETL functions of any data mining tool on the market. This allows the merging, appending, sorting and filtering of data.
    SQL Server 2005 Data Mining Add-ins for Office 2007 - These are great add-ins for taking advantage of SQL Server 2005 predictive analytics in Office Excel 2007 and Office Visio 2007. The add-ins Allow you to go through the entire development lifecycle within Excel 2007 by using either a spreadsheet or external data accessible through your SQL Server 2005 Analysis Services instance.
    Rapidminder - Also known as YALE is a pretty comprehensive and arguably world-leading when it comes to an open-source data mining solution. it is widely used from a large number of companies an organizations. Even though it is open-source, this tool, out of the box provides a secure environment and provides enterprise capable support and services so you will not be left out in the cold.

The list is short but ever changing in order to meet the increasing demands of companies to provide useful information from years of data.

TonyRocks.com in Pittsburgh Pennsylvania is one of only a few companies in the region that offer data tools an strategies.

They also keep a nice and updated list of the the latest on new tools in integration strategies for your organization.



Source: http://ezinearticles.com/?Top-Data-Mining-Tools&id=1380551

Is Web Scraping Relevant in Today's Business World?

Different techniques and processes have been created and developed over time to collect and analyze data. Web scraping is one of the processes that have hit the business market recently. It is a great process that offers businesses with vast amounts of data from different sources such as websites and databases.

It is good to clear the air and let people know that data scraping is legal process. The main reason is in this case is because the information or data is already available in the internet. It is important to know that it is not a process of stealing information but rather a process of collecting reliable information. Most people have regarded the technique as unsavory behavior. Their main basis of argument is that with time the process will be over flooded and therefore lead to parity in plagiarism.

We can therefore simply define web scraping as a process of collecting data from a wide variety of different websites and databases. The process can be achieved either manually or by the use of software. The rise of data mining companies has led to more use of the web extraction and web crawling process. Other main functions such companies are to process and analyze the data harvested. One of the important aspects about these companies is that they employ experts. The experts are aware of the viable keywords and also the kind of information which can create usable statistic and also the pages that are worth the effort. Therefore the role of data mining companies is not limited to mining of data but also help their clients be able to identify the various relationships and also build the models.

Some of the common methods of web scraping used include web crawling, text gripping, DOM parsing, and expression matching. The latter process can only be achieved through parsers, HTML pages or even semantic annotation. Therefore there are many different ways of scraping the data but most importantly they work towards the same goal. The main objective of using web scraping service is to retrieve and also compile data contained in databases and websites. This is a must process for a business to remain relevant in the business world.

The main questions asked about web scraping touch on relevance. Is the process relevant in the business world? The answer to this question is yes. The fact that it is employed by large companies in the world and has derived many rewards says it all. It is important to note that many people regarded this technology as a plagiarism tool and others consider it as a useful tool that harvests the data required for the business success.

Using of web scraping process to extract data from the internet for competition analysis is highly recommended. If this is the case, then you must be sure to spot any pattern or trend that can work in a given market.



Source: http://ezinearticles.com/?Is-Web-Scraping-Relevant-in-Todays-Business-World?&id=7091414

Internet Data Mining - How Does it Help Businesses?

Internet has become an indispensable medium for people to conduct different types of businesses and transactions too. This has given rise to the employment of different internet data mining tools and strategies so that they could better their main purpose of existence on the internet platform and also increase their customer base manifold.

Internet data-mining encompasses various processes of collecting and summarizing different data from various websites or webpage contents or make use of different login procedures so that they could identify various patterns. With the help of internet data-mining it becomes extremely easy to spot a potential competitor, pep up the customer support service on the website and make it more customers oriented.

There are different types of internet data_mining techniques which include content, usage and structure mining. Content mining focuses more on the subject matter that is present on a website which includes the video, audio, images and text. Usage mining focuses on a process where the servers report the aspects accessed by users through the server access logs. This data helps in creating an effective and an efficient website structure. Structure mining focuses on the nature of connection of the websites. This is effective in finding out the similarities between various websites.

Also known as web data_mining, with the aid of the tools and the techniques, one can predict the potential growth in a selective market regarding a specific product. Data gathering has never been so easy and one could make use of a variety of tools to gather data and that too in simpler methods. With the help of the data mining tools, screen scraping, web harvesting and web crawling have become very easy and requisite data can be put readily into a usable style and format. Gathering data from anywhere in the web has become as simple as saying 1-2-3. Internet data-mining tools therefore are effective predictors of the future trends that the business might take.



Source: http://ezinearticles.com/?Internet-Data-Mining---How-Does-it-Help-Businesses?&id=3860679

Data Mining Software - Discover Software Modernization

Data mining software is usually an application that one uses and covers mostly with one's knowledge in the discovery of software modernization. Mining data software involves the understanding of the software artifacts that exist and the mining data tools. This process has very close relations with reverse engineering. The knowledge that one gains from studying data software that exists is usually presented in forms of models and by doing these queries one can be in a position to make his personal data mining software. With the knowledge that someone gains it must be applicable and one must also know the mining data tools that are suppose to be used apart from the soft wares. One can be able to know very widely about the mining data tools that are there in mining data software by doing computer science as a course. Computer science covers widely on what are the procedures, steps of mining data software and how can use the mining data tools.

This software is mostly used in making of databases schemes. Making of databases is not as easy as many would think it requires one to have some knowledge about computer engineering and the basic concepts of computers.;This software is mostly used in data crawling because it can be in a position to store data and one can be able to retrieve the data when needed.

The softwares are not that cheap they come in different varieties and it will depend on which information or the database on which one is coming up with.

Data mining software are usually in different levels there is the data level, design level, application level, architectural level, call graph level and program level it will depend on which level one is covering and this come together with mining data tools.

Data software's have increased rapidly through the introduction of computers and ERP definition. Computers hackers have been able to get the softwares at a very low price and this has made data mining to become very easy and quick to use in the shops and supermarkets and also government institutions. One cannot do data crawling without having the basic knowledge about data mining soft wares because soft wares are the programmes that are usually installed into the computer and without the programmes then no data can be processed.

There are a lot of challenges that come with the use of the mining soft ware. One can easily crush the software he is using or the softwares can easily break they are normally sold on CDS one can easily break it or loose it.

High chances of losing the data that someone is coming up with is very high because computers easily crash due to some difficulties that they experience or a virus can easily crush the computer.

Mining software take a very large space and in most of the computers. The reason behind this is because, data crawling use graphics. Graphics usually occupy a lot of space in terms of the size of the local disk. One is suppose to look for a computer that has very good memory. Data crawling is something that needs to be updated each and every time something appears along the way.



Source: http://ezinearticles.com/?Data-Mining-Software---Discover-Software-Modernization&id=5054991

All You Need to Know Regarding Web Scraping Services

What is web scraping anyway? Surely, that’s the first thing in many businesses’ minds when they first hear of the term. With that said, you most certainly should avail of web scraping services because it’s basically your means to improving your decision-making process. At any rate, web scraping is essentially a method of getting data from a multitude of websites in one go (as opposed to copy-pasting data manually from one website, forum, open database, and so forth ad nauseaum). Instead of depending on market research and surveys, you can get your raw data right there on the Internet, but you require a web scraping service and software to streamline data extraction all the same.

The Evolution of Web Scraping in the Modern Era

Finding relevant data in a sea of information that covers all bases can be quite tough for a company to handle. Back in the Nineties, it was a personal in-joke how search engines have a hard time searching for the websites you’re looking for without further disambiguation on your part (plus back then, search engine algorithms were so primitive that even after you attempted disambiguation, you’re still offered the most popular websites in your search string instead of the most relevant ones). At any rate, as search engines have become advanced, so too has data extraction. A multitude of software applications have been developed to help you acquire the most relevant of info.

No longer will you have to sift through “gravel” and “sand” in order to acquire the precious stones underneath; irrelevant entries when it comes to outsourcing Internet marketing (like, for example, outsourcing for call center solutions keeps popping up in your extracted data) will no longer be a problem with the latest web scraping programs currently available. Your marketing data will be filled with raw yet relevant, on-topic information every time that’s quite easy to disseminate, categorize, analyze, and percolate. You’ll spend more time organizing everything into something coherent and useful in your decision-making process than effort in deleting irrelevant, non-sequitur data that shouldn’t have been gathered in the first place.

The Importance of Web Scraping and Data Extraction Techniques

Quite a lot of companies utilize freelancers to manually copy-paste data and scour the web for article sources and whatnot. This is quite inefficient considering the fact that ad campaigns require accurate, on-the-dot information about the target audience and current trends at all times, such that hiring people to do this website-per-website, article-per-article would be detrimental to your business needs. What’s more, there exist web scraping services that are quite dependable in getting the exact information you require in shaping up your advertising and promotional plan without having to pay freelancers on a commission basis to do it for you.

Web scraping services are superior to the labor-intensive method of manually gathering information in light of the latter’s tendency to waste time and effort. The data colleting from such means is also significantly less compared to the resources required to put together such a team of data extractors. Besides which, web scrappers nowadays make use of advanced search algorithms comparable to that to Google Panda and Penguin, such that you’re likelier to get information that you’re looking for via these means.



Source: http://wscraper.com/blog/2013/all-you-need-to-know-regarding-web-scraping-services/

Web Scraping Evolved: APIs for Turning Webpage Content into Valuable Data

While the rates in adoption of semantic standards are increasing, the majority of the web is still home to mostly unstructured data.  Search engines, like Google, remain focused on looking at the HTML markup for clues to create richer results for their users.  The creation of schema.org and similar movements has aided in the progression of the ability draw valuable content from webpages.

But even with semantic standards in place, structured data requires a parser to extract information and convert it into a data-interchange format, namely JSON or XML.  Many libraries exist for this, and in several popular coding languages.  But be warned, most of these parser libraries are far from polished.  Most are community-produced, and thus may not be complete or up to date, as standards are ever changing.  On the flip side, website owners whom don’t fully follow semantic rules, can break the parser.  And of course there are sites which contain no structured data formatting at all.   This inconsistency causes problems for intelligent data harvesting, and can be a roadblock for new business ideas and startups.

Several companies are offering an API service to make sense of this unstructured data, helping to remove these roadblocks.  For example, AlchemyAPI offers a suite of data extraction APIs including a Structured Content Scraping API, which enables structured data to be extracted based on both visual and structural traits.  Another company, DiffBot, is also taking care of the “dirty work” in the cloud, allowing entrepreneurs and developers to focus on their business instead of the semantics involved in parsing.  DiffBot stands out because of their unique approach.  Instead of looking at the data as a computer, they are looking visually, like a human would.  They first classify what type of webpage (eg. article, blog post, product, etc.) and then proceed to extract what visually appears to be relevant data for that page type (article title, most relevant image, etc).

Currently their website lists APIs for Page Classification (check out their infographic), as well as parsing Article type webpages.  Much of the web, including discussion boards, events, e-commerce data, etc. remains as potential future API offerings and it will be interesting to see which they go after next.

You can test drive the Artcle API on their website and see the extraction results instantly, as shown below of this article:This API can be a handy tool for young startup companies looking to avoid the parsing game.
For Delve, a New York City startup based out of WeWork Labs, they can’t wait around for the true Semantic Web to get here, so they’ve been using DiffBot’s Article API as a main component of their product.  Delve provides an enterprise news reader adding in a social element.  Teams as small as five, up to one-thousand, can converse and collaborate on relevant news articles.


Source: http://blog.programmableweb.com/2012/09/13/web-scraping-evolved-apis-for-turning-webpage-content-into-valuable-data/

Know What the Truth Behind Data Mining Outsourcing Service

We came to that, what we call the information age where industries are like useful data needed for decision-making, the creation of products - among other essential uses for business. Information mining and converting them to useful information is a part of this trend that allows companies to reach their optimum potential. However, many companies that do not meet even one deal with data mining question because they are simply overwhelmed with other important tasks. This is where data mining outsourcing comes in.

There have been many definitions to introduced, but it can be simply explained as a process that involves sorting through large amounts of raw data to extract valuable information needed by industries and enterprises in various fields. In most cases this is done by professionals, professional organizations and financial analysts. He has seen considerable growth in the number of sectors or groups that enter my self.
There are a number of reasons why there is a rapid growth in data mining outsourcing service subscriptions. Some of them are presented below:

A wide range of services

Many companies are turning to information mining outsourcing, because they cover a wide range of services. These services include, but are not limited to data from web applications congregation database, collect contact information from different sites, extract data from websites using the software, the sort of stories from sources news, information and accumulate commercial competitors.

Many companies fall

Many industries benefit because it is fast and realistic. The information extracted by data mining service providers of outsourcing used in crucial decisions in the field of direct marketing, e-commerce, customer relationship management, health, scientific tests and other experimental work, telecommunications, financial services, and a whole lot more.

A lot of advantages

Subscribe data mining outsourcing services it's offers many benefits, as providers assures customers to render services to world standards. They strive to work with improved technologies, scalability, sophisticated infrastructure, resources, timeliness, cost, the system safer for the security of information and increased market coverage.

Outsourcing allows companies to focus their core business and can improve overall productivity. Not surprisingly, information mining outsourcing has been a first choice of many companies - to propel the business to higher profits.


Source: http://ezinearticles.com/?Know-What-the-Truth-Behind-Data-Mining-Outsourcing-Service&id=5303589

Scraping Data off a Web Site

I’m taking the Data Analysis class through Coursera and one of the topics we’ve covered so far is how to “scape” data off a web site. The idea is to programmatically got through the source code of a web page, pull out some data, and then clean it up so you can analyze it. This may seem like overkill at first glance. After all, why not just select the data with your mouse and copy-and-paste into a spreadsheet? Well, for one, there may be dozens (or hundreds) of pages to visit and copying-and-pasting from each one would be time-consuming and impractical. Second, rarely does a copy-and-paste off a web site produce data ready for analysis. You have to tidy it up, sometimes quite a bit. Clearly these are both tasks we would like to automate.

To put this idea to use, I decided to scrape some data from the box scores of Virginia Tech football games. I attended Tech and love watching their football team, so this seemed like a fun exercise. Here’s an example of one of their box scores. You’ll see it is has everything but what songs the band played during halftime. I decided to start simple and just scrape the Virginia Tech Drive Summaries. This summarizes each drive, including things like number of plays, number of yards gained, and time of possession. Here’s the function I wrote in R, called vtFballData:

vtFballData <- function(start,stop,season){
    dsf <- c()
    # read the source code
    for (i in start:stop){
    url <- paste("http://www.hokiesports.com/football/stats/showstats.html?",i,sep="")
    web_page <- readLines(url)

    # find where VT drive summary begins
    dsum <- web_page[(grep("Virginia Tech Drive Summary", web_page) - 2):
                         (grep("Virginia Tech Drive Summary", web_page) + 18)]
    dsum2 <- readHTMLTable(dsum)
    rn <- dim(dsum2[[1]])[1]
    cn <- dim(dsum2[[1]])[2]
    ds <- dsum2[[1]][4:rn,c(1,(cn-2):cn)]
    ds[,3] <- as.character(ds[,3]) # convert from factor to character
    py <- do.call(rbind,strsplit(sub("-"," ",ds[,3])," "))
    ds2 <- cbind(ds,py)
    ds2[,5] <- as.character(ds2[,5]) # convert from factor to character
    ds2[,6] <- as.character(ds2[,6]) # convert from factor to character
    ds2[,5] <- as.numeric(ds2[,5]) # convert from character to numeric
    ds2[,6] <- as.numeric(ds2[,6]) # convert from character to numeric
    ds2[,3] <- NULL # drop original pl-yds column

    names(ds2) <-c("quarter","result","top","plays","yards")
    # drop unused factor levels carried over from readlines
    ds2$quarter <- ds2$quarter[, drop=TRUE]
    ds2$result <- ds2$result[, drop=TRUE]

    # convert TOP from factor to character
    ds2[,3] <- as.character(ds2[,3])
    # convert TOP from M:S to just seconds
    ds2$top <- sapply(strsplit(ds2$top,":"),
          function(x) {
            x <- as.numeric(x)
            x[1]*60 + x[2]})

    # need to add opponent
    opp <- web_page[grep("Drive Summary", web_page)]
    opp <- opp[grep("Virginia Tech", opp, invert=TRUE)] # not VT
    opp <- strsplit(opp,">")[[1]][2]
    opp <- sub(" Drive Summary</td","",opp)
    ds2 <- cbind(season,opp,ds2)
    dsf <- rbind(dsf,ds2)
    }
return(dsf)
}

I’m sure this is three times longer than it needs to be and could be written much more efficiently, but it works and I understand it. Let’s break it down.

My function takes three values: start, stop, and season. Start and stop are both numerical values needed to specify a range of URLs on hokiesports.com. Season is simply the year of the season. I could have scraped that as well but decided to enter it by hand since this function is intended to retrieve all drive summaries for a given season.

The first thing I do in the function is define an empty variable called “dsf” (“drive summaries final”) that will ultimately be what my function returns. Next I start a for loop that will start and end at numbers I feed the function via the “start” and “stop” parameters. For example, the box score of the 1st game of the 2012 season has a URL ending in 14871. The box score of the last regular season game ends in 14882. To hit every box score of the 2012 season, I need to cycle through this range of numbers. Each time through the loop I “paste” the number to the end of “http://www.hokiesports.com/football/stats/showstats.html?” and create my URL. I then feed this URL to the readLines() function which retrieves the code of the web page and I save it as “web_page”.

Let’s say we’re in the first iteration of our loop and we’re doing the 2012 season. We just retrieved the code of the box score web page for the Georgia Tech game. If you go to that page, right click on it and view source, you’ll see exactly what we have stored in our “web_page” object. You’ll notice it has a lot of stuff we don’t need. So the next part of my function zeros in on the Virginia Tech drive summary:

# find where VT drive summary begins
dsum <- web_page[(grep("Virginia Tech Drive Summary", web_page) - 2):
                 (grep("Virginia Tech Drive Summary", web_page) + 18)]

This took some trial and error to assemble. The grep() function tells me which line contains the phrase “Virginia Tech Drive Summary”. I subtract 2 from that line to get the line number where the HTML table for the VT drive summary begins (i.e., where the opening <table> tag appears). I need this for the upcoming function. I also add 18 to that line number to get the final line of the table code. I then use this range of line numbers to extract the drive summary table and store it as “dsum”. Now I feed “dsum” to the readHTMLTable() function, which converts an HTML table to a dataframe (in a list object) and save it as “dsum2″. The readHTMLTable() function is part of the XML package, so you have download and install that package first and call library(XML) before running this function.

At this point we have a pretty good looking table. But it has 4 extra rows at the top we need to get rid of. Plus I don’t want every column. I only want the first column (quarter) and last three columns (How lost, Pl-Yds, and TOP). This is a personal choice. I suppose I could have snagged every column, but decided to just get a few. To get what I want, I define two new variables, “rn” and “cn”. They stand for row number and column number, respectively. “dsum2″ is a list object with the table in the first element, [[1]]. I reference that in the call to the dim () function. The first element returned is the number of rows, the second the number of columns. Using “rn” and “cn” I then index dsum2 to pull out a new table called “ds”. This is pretty much what I wanted. The rest of the function is mainly just formatting the data and giving names to the columns.

The next three lines of code serve to break up the “Pl-Yds” column into two separate columns: plays and yards. The following five lines change variable classes and remove the old “Pl-Yds” column. After that I assign names to the columns and drop unused factor levels. Next up I convert TOP into seconds. This allows me to do mathematical operations, such as summing and averaging.

The final chunk of code adds the opponent. This was harder than I thought it would be. I’m sure it can be done faster and easier than I did it, but what I does works. First I use the grep() function to identify the two lines that contain the phrase “Drive Summary”. One will always have Virginia Tech and the other their opponent. The next line uses the invert parameter of grep to pick the line that does not contain Virginia Tech. The selected line looks like this for the first box score of 2012: “<td colspan=\”9\”>Georgia Tech Drive Summary</td>”. Now I need to extract “Georgia Tech”. To do this I split the string by “>” and save the second element:

opp <- strsplit(opp,">")[[1]][2]

It looks like this after I do the split:

[[1]]
[1] "<td colspan=\"9\""              "Georgia Tech Drive Summary</td"

Hence the need to add the “[[1]][2]” reference. Finally I substitute ” Drive Summary</td” with nothing and that leaves me with “Georgia Tech”. Finally I add the season and opponent to the table and update the “dsf” object. The last line is necessary to allow me to add each game summary to the bottom of the previous table of game summaries.

Here’s how I used the function to scrape all VT drive summaries from the 2012 regular season:

dsData2012 <- vtFballData(14871,14882,2012)

To identify start and stop numbers I had to go to the VT 2012 stats page and hover over all the box score links to figure out the number sequence. Fortunately they go in order. (Thank you VT athletic dept!) The bowl game is out of sequence; its number is 15513. But I could get it by calling vtFballData(15513,15513,2012). After I call the function, which takes about 5 seconds to run, I get a data frame that looks like this:

 season          opp quarter result top plays yards
   2012 Georgia Tech       1   PUNT 161     6    24
   2012 Georgia Tech       1     TD 287    12    56
   2012 Georgia Tech       1  DOWNS 104     5    -6
   2012 Georgia Tech       2   PUNT 298     7    34
   2012 Georgia Tech       2   PUNT  68     4    10
   2012 Georgia Tech       2   PUNT  42     3     2

Now I’m ready to do some analysis! There are plenty of other variables I could have added, such as whether VT won the game, whether it was a home or away game, whether it was a noon, afternoon or night game, etc. But this was good enough as an exercise. Maybe in the future I’ll revisit this little function and beef it up.


Source: http://www.clayford.net/statistics/scraping-data-off-a-web-site/

Is Data Scraping Unethical?

Perhaps the biggest challenges that website owners face, in addition to attracting visitors, is coming up with original content to publish on their websites.

Search engines are ravenously hungry creatures. They are constantly scraping the web, seeking content that they can add to the index, and if your site publishes good quality original content, the chances are very likely that you will receive higher ranking on the SERPs.

The process may not be as simple as it sounds, as there are perhaps millions of competitors in the same area, who may be competing for ranking on the same keyword(s).

Because of the challenges that are faced, with the very time-consuming and labor intensive tasks of continually creating and publishing original content, website owners may often seek shortcuts or use methods and applications that are frowned on by the search engines.

In order to remain competitive, another of the tasks, with which website owners are faced, is with keeping an eye on the competition. You need to know what the competition is up to, and you need to be able to react to, or else you can easily get left behind. One of the ways that you can do this is by developing applications that focus on data scraping. Obtaining the data may be harmless, but how it is used is where the questions often arise.

While the practice may appear to fairly innocuous and can be useful, there are several instances where it may be questioned.

There is now rapidly expanding industry for data mining. Reports are that we now create more data every day than we did in the over the last two decades, and the market for data mining continues to expand exponentially. Marketers are constantly scraping the web to build profiles of consumers, and we may be making easier for them, by leaving trails that they can easily follow.

It may be it bit disconcerting to know that every website that you ever visit is actually logged, and the data can be used to build a profile of your habits. Many uses may find it intrusive, to find that information that should be considered as private, is now available for public consumption.

Scraping can involve not only personal data, but also your buying behaviour as well as customary habits or hobbies. All of your online activities can be tracked, and although it may be stated as otherwise, there are ways that your data can be shared by other third parties, with contravening any laws

It is easy to collect detailed information, such as cell-phone numbers, email addresses and even your posts on the social networks, can easily be tracked and analysed.

There is considerable debate as to the ownership of data that is posted on the social networks. To whom does it really belong, and who should be allowed access to it?

It is also not surprising that media outlets are using data scraping methods by employing what are called listening devices to monitor what is being said on the social networks in real time. It is one of the ways that they can observe what is being said about specific organisations, products or people.

The debate is sure to continue, but there is no doubt that it can be useful.

Source: http://www.twm.co.nz/is-data-scraping-unethical/