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

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 http://www.Anonymizer.com offer large scale anonymous proxy solutions, but often carry a fairly hefty setup fee to get you going.

The other advantage is that companies who own such networks can often help you design and implementation of a custom proxy data scraping program instead of trying to work with a generic scraping bot. After performing a simple Google search, I quickly found one company (www.ScrapeGoat.com) that provides anonymous proxy server access for data scraping purposes. Or, according to their website, if you want to make your life even easier, ScrapeGoat can extract the data for you and deliver it in a variety of different formats often before you could even finish configuring your off the shelf data scraping program.

Whichever path you choose for your proxy data scraping needs, don't let a few simple tricks thwart you from accessing all the wonderful information stored on the world wide web!


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

Usefulness of Web Scraping Services

For any business or organization, surveys and market research play important roles in the strategic decision-making process. Data extraction and web scraping techniques are important tools that find relevant data and information for your personal or business use. Many companies employ people to copy-paste data manually from the web pages. This process is very reliable but very costly as it results to time wastage and effort. This is so because the data collected is less compared to the resources spent and time taken to gather such data.

Nowadays, various data mining companies have developed effective web scraping techniques that can crawl over thousands of websites and their pages to harvest particular information. The information extracted is then stored into a CSV file, database, XML file, or any other source with the required format. After the data has been collected and stored, data mining process can be used to extract the hidden patterns and trends contained in the data. By understanding the correlations and patterns in the data; policies can be formulated and thereby aiding the decision-making process. The information can also be stored for future reference.

The following are some of the common examples of data extraction process:

• Scrap through a government portal in order to extract the names of the citizens who are reliable for a given survey.
• Scraping competitor websites for feature data and product pricing
• Using web scraping to download videos and images for stock photography site or for website design

Automated Data Collection
It is important to note that web scraping process allows a company to monitor the website data changes over a given time frame. It also collects the data on a routine basis regularly. Automated data collection techniques are quite important as they help companies to discover customer trends and market trends. By determining market trends, it is possible to understand the customer behavior and predict the likelihood of how the data will change.

The following are some of the examples of the automated data collection:

• Monitoring price information for the particular stocks on hourly basis
• Collecting mortgage rates from the various financial institutions on the daily basis
• Checking on weather reports on regular basis as required

By using web scraping services it is possible to extract any data that is related to your business. The data can then be downloaded into a spreadsheet or a database for it to be analyzed and compared. Storing the data in a database or in a required format makes it easier for interpretation and understanding of the correlations and for identification of the hidden patterns.

Through web scraping it is possible to get quicker and accurate results and thus saving many resources in terms of money and time. With data extraction services, it is possible to fetch information about pricing, mailing, database, profile data, and competitors data on a consistent basis. With the emergence of professional data mining companies outsourcing your services will greatly reduce your costs and at the same time you are assured of high quality services.


Source: http://ezinearticles.com/?Usefulness-of-Web-Scraping-Services&id=7181014

Web Data Extraction Services and Data Collection Form Website Pages

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

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

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

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

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

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

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

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


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

Data Entry Outsourcing - A Necessity For a More Professional and Efficient Work!

Many companies have to handle huge volume of internal and external information related to company operation, from making day to day decisions, to do a research on whether to enter a new market. In such cases data entry is a regular and continuous requirement. Yet there are also some companies whose data entry works maybe just a temporary requirement. But neither of the two cases mentioned above can deny the fact that accumulated data is a powerful management resource.

Although important, does not mean that this work is a core office work. The basics of completing a traditional data entry job usually involves copying and pasting text or numbers over and over, continuously filling out forms with information, or something similar. While it is fairly simple work and does not require much skill or thinking. It is not necessity to hire regular employee with competitive salary and benefits to ensure smooth and efficient handing of this information, especially when your data entry work is just a temporary requirement.

Most of the time-consuming data entry jobs are being done by outsourcing. For example, catalog management, which involves handling and maintaining paper catalogs, is not just time consuming, but also expensive. By converting your product catalogs to online and digital catalogs, making changes, and updating your product catalog becomes as easy as the click of a button once data entry has been completed.

With two requirements fulfilled-the development of the internet and flexible work hours, many people who don't want a regular work or just lose their jobs choose to providing data entry freelance. It is cheaper than hire a full-time employee, while the other side of the coin is, you cannot assured that they received proper training and there will be a third party who will act as intermediary should problems arise.

Hence, it is therefore much better to search for reliable outsourcing companies who can not only provide competitive price but also quality work with a guarantee. Such companies are emerging mainly in China, India and other countries where the cost of human labor are extremely low while the service is highly qualified.


Source: http://ezinearticles.com/?Data-Entry-Outsourcing---A-Necessity-For-a-More-Professional-and-Efficient-Work!&id=3396508

Understanding Data Mining

Well begun is half done. We can say that the invention of Internet is the greatest invention of the century which allows for quick information retrieval. It also has negative aspects, as it is an open forum therefore differentiating facts from fiction seems tough. It is the objective of every researcher to know how to perform mining of data on the Internet for accuracy of data. There are a number of search engines that provide powerful search results.

Knowing File Extensions in Data Mining

For mining data the first thing is important to know file extensions. Sites ending with dot-com are either commercial or sales sites. Since sales is involved there is a possibility that the collected information is inaccurate. Sites ending with dot-gov are of government departments, and these sites are reviewed by professionals. Sites ending with dot-org are generally for non-profit organizations. There is a possibility that the information is not accurate. Sites ending with dot-edu are of educational institutions, where the information is sourced by professionals. If you do not have an understanding you may take help of professional data mining services.

Knowing Search Engine Limitations for Data Mining

Second step is to understand when performing data mining is that majority search engines have filtering, file extension, or parameter. These are restrictions to be typed after your search term, for example: if you key in "marketing" and click "search," every site will be listed from dot-com sites having the term "marketing" on its website. If you key in "marketing site.gov," (without the quotation marks) only government department sites will be listed. If you key in "marketing site:.org" only non-profit organizations in marketing will be listed. However, if you key in "marketing site:.edu" only educational sites in marketing will be displayed. Depending on the kind of data that you want to mine after your search term you will have to enter "site.xxx", where xxx will being replaced by.com,.gov,.org or.edu.

Advanced Parameters in Data Mining

When performing data mining it is crucial to understand far beyond file extension that it is even possible to search particular terms, for example: if you are data mining for structural engineer's association of California and you key in "association of California" without quotation marks the search engine will display hundreds of sites having "association" and "California" in their search keywords. If you key in "association of California" with quotation marks, the search engine will display only sites having exactly the phrase "association of California" within the text. If you type in "association of California" site:.com, the search engine will display only sites having "association of California" in the text, from only business organizations.

If you find it difficult it is better to outsource data mining to companies like Online Web Research Services


Source: http://ezinearticles.com/?Understanding-Data-Mining&id=5608012

Digging Up Dollars With Data Mining - An Executive's Guide

Introduction

Traditionally, organizations use data tactically - to manage operations. For a competitive edge, strong organizations use data strategically - to expand the business, to improve profitability, to reduce costs, and to market more effectively. Data mining (DM) creates information assets that an organization can leverage to achieve these strategic objectives.

In this article, we address some of the key questions executives have about data mining. These include:

    What is data mining?
    What can it do for my organization?
    How can my organization get started?

Business Definition of Data Mining

Data mining is a new component in an enterprise's decision support system (DSS) architecture. It complements and interlocks with other DSS capabilities such as query and reporting, on-line analytical processing (OLAP), data visualization, and traditional statistical analysis. These other DSS technologies are generally retrospective. They provide reports, tables, and graphs of what happened in the past. A user who knows what she's looking for can answer specific questions like: "How many new accounts were opened in the Midwest region last quarter," "Which stores had the largest change in revenues compared to the same month last year," or "Did we meet our goal of a ten-percent increase in holiday sales?"

We define data mining as "the data-driven discovery and modeling of hidden patterns in large volumes of data." Data mining differs from the retrospective technologies above because it produces models - models that capture and represent the hidden patterns in the data. With it, a user can discover patterns and build models automatically, without knowing exactly what she's looking for. The models are both descriptive and prospective. They address why things happened and what is likely to happen next. A user can pose "what-if" questions to a data-mining model that can not be queried directly from the database or warehouse. Examples include: "What is the expected lifetime value of every customer account," "Which customers are likely to open a money market account," or "Will this customer cancel our service if we introduce fees?"

The information technologies associated with DM are neural networks, genetic algorithms, fuzzy logic, and rule induction. It is outside the scope of this article to elaborate on all of these technologies. Instead, we will focus on business needs and how data mining solutions for these needs can translate into dollars.

Mapping Business Needs to Solutions and Profits

What can data mining do for your organization? In the introduction, we described several strategic opportunities for an organization to use data for advantage: business expansion, profitability, cost reduction, and sales and marketing. Let's consider these opportunities very concretely through several examples where companies successfully applied DM.

Expanding your business: Keystone Financial of Williamsport, PA, wanted to expand their customer base and attract new accounts through a LoanCheck offer. To initiate a loan, a recipient just had to go to a Keystone branch and cash the LoanCheck. Keystone introduced the $5000 LoanCheck by mailing a promotion to existing customers.

The Keystone database tracks about 300 characteristics for each customer. These characteristics include whether the person had already opened loans in the past two years, the number of active credit cards, the balance levels on those cards, and finally whether or not they responded to the $5000 LoanCheck offer. Keystone used data mining to sift through the 300 customer characteristics, find the most significant ones, and build a model of response to the LoanCheck offer. Then, they applied the model to a list of 400,000 prospects obtained from a credit bureau.

By selectively mailing to the best-rated prospects determined by the DM model, Keystone generated $1.6M in additional net income from 12,000 new customers.

Reducing costs: Empire Blue Cross/Blue Shield is New York State's largest health insurer. To compete with other healthcare companies, Empire must provide quality service and minimize costs. Attacking costs in the form of fraud and abuse is a cornerstone of Empire's strategy, and it requires considerable investigative skill as well as sophisticated information technology.

The latter includes a data mining application that profiles each physician in the Empire network based on patient claim records in their database. From the profile, the application detects subtle deviations in physician behavior relative to her/his peer group. These deviations are reported to fraud investigators as a "suspicion index." A physician who performs a high number of procedures per visit, charges 40% more per patient, or sees many patients on the weekend would be flagged immediately from the suspicion index score.

What has this DM effort returned to Empire? In the first three years, they realized fraud-and-abuse savings of $29M, $36M, and $39M respectively.

Improving sales effectiveness and profitability: Pharmaceutical sales representatives have a broad assortment of tools for promoting products to physicians. These tools include clinical literature, product samples, dinner meetings, teleconferences, golf outings, and more. Knowing which promotions will be most effective with which doctors is extremely valuable since wrong decisions can cost the company hundreds of dollars for the sales call and even more in lost revenue.

The reps for a large pharmaceutical company collectively make tens of thousands of sales calls. One drug maker linked six months of promotional activity with corresponding sales figures in a database, which they then used to build a predictive model for each doctor. The data-mining models revealed, for instance, that among six different promotional alternatives, only two had a significant impact on the prescribing behavior of physicians. Using all the knowledge embedded in the data-mining models, the promotional mix for each doctor was customized to maximize ROI.

Although this new program was rolled out just recently, early responses indicate that the drug maker will exceed the $1.4M sales increase originally projected. Given that this increase is generated with no new promotional spending, profits are expected to increase by a similar amount.

Looking back at this set of examples, we must ask, "Why was data mining necessary?" For Keystone, response to the loan offer did not exist in the new credit bureau database of 400,000 potential customers. The model predicted the response given the other available customer characteristics. For Empire, the suspicion index quantified the differences between physician practices and peer (model) behavior. Appropriate physician behavior was a multi-variable aggregate produced by data mining - once again, not available in the database. For the drug maker, the promotion and sales databases contained the historical record of activity. An automated data mining method was necessary to model each doctor and determine the best combination of promotions to increase future sales.

Getting Started

In each case presented above, data mining yielded significant benefits to the business. Some were top-line results that increased revenues or expanded the customer base. Others were bottom-line improvements resulting from cost-savings and enhanced productivity. The natural next question is, "How can my organization get started and begin to realize the competitive advantages of DM?"

In our experience, pilot projects are the most successful vehicles for introducing data mining. A pilot project is a short, well-planned effort to bring DM into an organization. Good pilot projects focus on one very specific business need, and they involve business users up front and throughout the project. The duration of a typical pilot project is one to three months, and it generally requires 4 to 10 people part-time.

The role of the executive in such pilot projects is two-pronged. At the outset, the executive participates in setting the strategic goals and objectives for the project. During the project and prior to roll out, the executive takes part by supervising the measurement and evaluation of results. Lack of executive sponsorship and failure to involve business users are two primary reasons DM initiatives stall or fall short.

In reading this article, perhaps you've developed a vision and want to proceed - to address a pressing business problem by sponsoring a data mining pilot project. Twisting the old adage, we say "just because you should doesn't mean you can." Be aware that a capability assessment needs to be an integral component of a DM pilot project. The assessment takes a critical look at data and data access, personnel and their skills, equipment, and software. Organizations typically underestimate the impact of data mining (and information technology in general) on their people, their processes, and their corporate culture. The pilot project provides a relatively high-reward, low-cost, and low-risk opportunity to quantify the potential impact of DM.

Another stumbling block for an organization is deciding to defer any data mining activity until a data warehouse is built. Our experience indicates that, oftentimes, DM could and should come first. The purpose of the data warehouse is to provide users the opportunity to study customer and market behavior both retrospectively and prospectively. A data mining pilot project can provide important insight into the fields and aggregates that need to be designed into the warehouse to make it really valuable. Further, the cost savings or revenue generation provided by DM can provide bootstrap funding for a data warehouse or related initiatives.

Recapping, in this article we addressed the key questions executives have about data mining - what it is, what the benefits are, and how to get started. Armed with this knowledge, begin with a pilot project. From there, you can continue building the data mining capability in your organization; to expand your business, improve profitability, reduce costs, and market your products more effectively.


Source: http://ezinearticles.com/?Digging-Up-Dollars-With-Data-Mining---An-Executives-Guide&id=6052872

ASP - Screen scraping an authenticated site

My company is using a web hosting company to host the company website. I as the webmaster have access to a staging server and a production server. I use a web page to replicate the site from staging to production.

The replication page requires me to Authenticate the first time I access it and then knows me every subsequent time I load the page. When I close the browser I have to authenticate again.  When the page loads it gives me the current state of replication (complete, running ...) I am trying to screen scrape the status of the replication.

The reason I'm trying to scrape the status is that I have to run post replication actions on the production site to hide features I'm adding that have not yet been approved for production.

Let me explain.  If I get a request for a site change, I make the change and upload the change to the staging server. When the change is approved it moves into production. The problem occurs when requests need to remain active in staging for review and can't go live in production. Normally this holds up replication because replication simply duplicates what is in staging to production. To get around this problem, I added a features table to my DB so I can turn on and off new features. This is great except for the fact that upon completion of replication, I need to turn off the features in the DB.

At present, replication works like this:
1. Load replication page
2. Supply replication credentials
3. Press the button to begin replication
4. Refresh the page until the status reads: complete
5. Run the post replication code to turn off not yet approved features.

What I would like:
1. Load my own page
2. Call the replication page in an iframe which asks me to authenticate
3. Automatically refresh the page in the iframe at a set interval scraping the status
4. If the status is “Complete” do post replication actions else return to previous step and refresh page.

So far I’ve got everything set except for the scraping.  I’ve been using fidder and know the following:
1. The replication page uses NTLM authentication method
2. The replication page stores a cookie upon login containing siteserverid=

I’ve researched the issue and come up with a handy scraping script, (http://www.codeproject.com/KB/asp/gethtml.aspx) which does get html source code but not authenticated sites. I’ve yet to figure out how to add my authentication information to the request so I can scrape.  Does anyone understand my situation enough to assist me?


Source: http://www.nullskull.com/q/10211086/screen-scraping-an-authenticated-site.aspx

How You Can Benefit From Low Latency Market Data Solutions In Excel

Low latency market data solutions in Excel are attractive to anyone who wishes to increase their efficiency and accuracy in the trading market. Low latency together with Excel's benefits allow traders and investors to process market updates and complete orders in a small amount of time thus providing a competitive advantage.

Many establishments are seeking out the most efficient IT infrastructure and applications in order to function well in algorithmic trading. Further decreasing the latency of market data solutions can increase trading companies' efficiency in delivering the vast amount of data as well as coping with the current volatile market.

In order to perform financial transactions, financial institutions use low latency trading to connect to Electronic Communication Networks and stock exchanges. Trading venues define it as the measurement of the processing delay between entering the order and accepting the transmission.

The financial services that are well aware of the value of Excel in management of data, know how extremely beneficial it is to have low latency market data solutions in Excel. While there have been many attempts to create platforms and applications that can provide higher benefits than Excel, this application still has the most demand.

The Excel application is used to handle a number of tasks such as risk management, data management, data exchange as well as publishing and subscribing to real-time data. To make use of Excel's features or applications, financial services have gone through measures to incorporate Excel with real-time market data. Although previous methods have proven successful, they each include some downsides:

1. One common method in the past is the downloading of market data from online sources into Excel. This method can be time-consuming especially when it requires that you visit various websites and then manually copy their data into your spreadsheet.

2. Another method is by way of scraping data from various websites. This involves the use of automation to acquire data from different sources. Its downside is aside from legal complications, this method's success depends on the availability of the source.

3. The most preferred method is availing the service of vendors that provide data and at the same time incorporates this data into an Excel application. While they can provide convenience as well as quality, these services usually ask for a fee.

Currently there are online services that provide low latency market data solutions in Excel. To financial participants who want to create a low latency data distribution system, there are services that have already created an efficient data distribution protocol that includes a data management system as well as database silo storage facilities.



Source: http://ezinearticles.com/?How-You-Can-Benefit-From-Low-Latency-Market-Data-Solutions-In-Excel&id=7096300

Outsourcing Data Entry Services

Data or raw information is the backbone of any industry or business organization. However, raw data is seldom useful in its pure form. For it to be of any use, data has to be recorded properly and organized in a particular manner. Only then can data be processed. That is why it is important to ensure accurate data entry. But because of the unwieldy nature of data, feeding data is a repetitive and cumbersome job and it requires heavy investment, both in terms of time and energy from staff. At the same time, it does not require a high level of technical expertise. Due to these factors, data entry can safely be outsourced, enabling companies to devote their time and energy on tasks that enhance their core competence.

Many companies, big and small, are therefore enhancing their productivity by outsourcing the endless monotonous tasks that tend to cut down the organization's productivity. In times to come, outsourcing these services will become the norm and the volume of work that is outsourced will multiply. The main reason for these kinds of development is the Internet. Web based customer service and instant client support has made it possible for service providers to act as one stop business process outsourcing partners to parent companies that require support.

Data entry services are not all alike. Different clients have different demands. While some clients may require recording information coupled with document management and research, others may require additional services like form processing or litigation support. Data entry itself could be from various sources. For instances, sometimes information may need to be typed out from existing documents while at other times, data needs to be extracted from images or scanned documents. To rise up to these challenges, service providers who offer these services must have the expertise and the software to ensure rapid and accurate data entry. That is why it is important to choose your service provider with a lot of care.

Before hiring your outsourcing partner, you need to ask yourself the following questions.

* What kind of reputation does the company enjoy? Do they have sufficient years of experience? What kind of history and background does the company enjoy?

* Do they have a local management arm that you can liaise with on a regular basis?

* Do the service personnel understand your requirements and can they handle them effectively?

* What are the steps taken by the company to ensure that there is absolutely no compromise in confidentiality and security while dealing with vital confidential data?

* Is there a guarantee in place?

* What about client references?

The answers to these questions will help you identify the right partner for outsourcing your data entry service requirements.


Source: http://ezinearticles.com/?Outsourcing-Data-Entry-Services&id=3568373

Web data Scraping is the most effective offers

Every growing business needs a way to reduce, significantly, the time and financial resources that it dedicates to handling its growing informational need. Web Data Scraping offers the most effective yet very economical solution to the data loads that your company has to handle constantly. The variety of handling services from this company includes data scraping, web scraping and website scraping.

The company offers the most valuable and efficient website data scraping software that will enable you to scrape out all the relevant information that you need from the World Wide Web. The extracted information is valuable to a variety of production, consumption and service industries. For comparison of prices online, website change detection, research, weather data monitoring, web data integration and web mash up and many more uses, the web scraping software from Web Data Scraping is the best bet you can find from the web scraping market.

The software that this company offers will handle all the web harvesting and website scraping in a manner that more of simulates a human exploration of the websites you want to scrape from. A high level HTTP and fully embedding popular browsers like Mozilla and the exclusive ones work with web data extraction from Webdatascraping.us

The data scraping technology from Web Data Scraping has the capability to bypass all the technical measures that the institutional owners of the websites implement to stop bots. Imagine paying for web scraping software that cannot bypass blockade by these websites from which you need to use their information. This company guarantees that not any excess traffic monitoring, IP address blockade or additions of entries like robots.txt will be able to prevent its functioning. In addition, there are many website scraping crawlers that are easily detected and blocked by commercial anti-bot tools like distil, sentor and siteblackbox. Web Data Scraping is not preventable with any of these and most importantly with verification software’s like catches.

We have expertise in following listed services for which you can ask us.
- Contact Information Scraping from Website.
- Data Scraping from Business Directory – Yellow pages, Yell, Yelp, Manta, Super pages.
- Email Database Scraping from Website/Web Pages.
- Extract Data from EBay, Amazon, LinkedIn, and Government Websites.
- Website Content, Metadata scraping and Information scraping.
- Product Information Scraping – Product details, product price, product images.
- Web Research, Internet Searching, Google Searching and Contact Scraping.
- Form Information Filling, File Uploading & Downloading.
- Scraping Data from Health, Medical, Travel, Entertainment, Fashion, Clothing Websites.

Every company or organization, survey and market research for strategic decisions plays an important role in the process of data extraction and Web technology. Important instruments that relevant data and information for your personal or commercial use scraping. Many companies paste manually copying data from Web pages people, it is time to try and wastage as a result, the process is too expensive, that it's because the resources spent less and collect data from the time taken to collect data is very reliable.

Nowadays, a CSV file, a database, an XML file that thousands of websites and crop-specific crawl your pages can have different data mining companies effective web information technology, or other source data scraping is saved with the required format. Collect data and process data mining stored after the lies hidden patterns and trends can be used to understand patterns in data correlations and delete; Policy formulated and decisions. Data is stored for future use.


Source: http://www.selfgrowth.com/articles/web-data-scraping-is-the-most-effective-offers

Three Common Methods For Web Data Extraction

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

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

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

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

Raw regular expressions and code

Advantages:

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

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

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

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

Disadvantages:

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

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

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

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

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

Ontologies and artificial intelligence

Advantages:

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

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

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

Disadvantages:

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

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

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

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

Screen-scraping software

Advantages:

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

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

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

Disadvantages:

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

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

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

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

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


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