Rush Poker at Full Tilt and Data-Mining

Well there was a surprise update today at full tilt poker and okay that's nothing new, but this one included a new game called Rush Poker. One thing you can say what full tilt poker and that is they are always thinking about new ways to keep poker players interested and excited about playing.

Rush poker adds an element of speed to single table play of no limit Texas hold'em cash games whereby you are immediately transported to a new table every time you fold your hand. This is done instantly, allowing you to play hundreds of hands per hour. Now this is not turbo speed like you would have seen in tournaments, this is more physically changing the makeup of your table allowing you to engage in as many hands as you're comfortable with. The faster you fold your hand, the faster you get to see your next hand.

The way the software allows you to do this, is by placing you amongst the pool of players who were also waiting for the next time to start. This means you can be put in any position, and be facing any combination of any players that are active in the pool at that moment. Truly an interesting concept here, and full tilt should be commended for their forward thinking, which has really kept them as the leaders in the online poker market at least as far as features go.

The only time I found that I had to wait in this game was when I was in the big blind, and that basically meant that I had to wait for somebody to raise or just check fold to me. Pretty much any other position you can glance at your hand, click full or quick fold, and you're off to another table.

Alright so it's fun, action-packed, and demands your considerable concentration, but what about 3rd party tracking software? I was using Hold'em Indicator on first visit to these tables and used the odds display, but since your table and opponents change every hand, there was no profiling going on whatsoever. In addition, Full Tilt does not allow observers at these tables, so there won't be any easy way to data mine statistics for programs like Poker Tracker, Hold'em Manager or Poker Edge. You are just going to be playing these straight-up on card strength and the betting patterns of that very hand you are in.

So if you eliminate profiling from this game, there will be other rules of engagement in order to stay profitable. Of course Full Tilt Poker is just going to rake in much more well, rake. But if you can focus for a couple of hours you could be seeing as many as 1000 hands. I could see playing tight solid aggressive could be working under the circumstances, but I also noticed a lot of blind stealing going on here. Players already sort of trying to take advantage of the fact that players and the blinds might not even be there and they can tell because they're on auto fold and they're already sitting at another table looking at another hand.

It's going to be interesting to see what games and buy-in levels Full Tilt expands this to and how popular it's going to be with players. Nonetheless, another industry-leading innovation from full tilt poker.


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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.



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Data Entry Outsourcing

Data entry outsourcing provides a tremendous amount of flexibility in budget because offsite data entry outsourcing services enable organizations to pay for certain services on a per project and as needed basis. The option cuts down costs and need for hiring special personnel and training them, harnessing fresh engineering proficiency, and slashes the expenditure to be made on operations. In general such tasks tend to be repetitive and monotonous yet require a high degree of attention to detail. For these reasons and more, most businesses choose to outsource such tasks to specialists.

The effectiveness of employing freelance staff members at a preferred rate was gaining momentum even among businesses operating on a small scale. Data entry outsourcing of specific tasks to outsiders meant that organizations could make use of the know-how possessed by them, even though the organization did not have offices in those parts of the globe.

Data entry outsourcing projects are growing exponentially, in terms of the revenues involved, people employed, and the amount of data entailed. With the world going digital, the speed and accuracy is greatly enhanced when companies decide to outsource such tasks. The service providers and service buyers can now function almost on a real-time basis, although they may be geographically poles apart. This leads to the leveraging of scale of the economy of the outsourced work and the capacity to deliver astronomically valuable service at reduced, end-customer price.

The size of the database of all businesses is expanding. The strategic or top management is able to project into the future using the data of the past. Data is a managerial tool. As it is difficult to house every procedure under one roof, the importance of data entry outsourcing is in high demand. Much stress can be taken away, and the venture turned profitable, especially if the low-cost, third world countries are targeted.

Data entry outsourcing may typically call for online or offline treatment at the providers' end. It is a lucrative and astute choice to outsource the entering of data because the business going in for it can focus on core processes, leaving the work of entering data to reliable service providers - those who specialize in the field and have the necessary infrastructure. Businesses, which outsource data entry to self owned or third-party service providers, have been found to do financially better than others. This results in their being able to pay their workers higher wages and influence their morale positively.



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What is Data Entry - Which Data Entry Services Are Popular?

Today, data entry business is the fastest growing businesses in the world whether we think so or not but it is fact. This data online entry is dynamic and is in regular changes.

Data form entries can be described as numeric, alpha numeric, text and form entry. Data entries services are very useful in business firms and organizations as there is a huge demand of entry.

Therefore, the outsourcing form entry business is flexible and is require detailed information and accuracy of ease of access. If you will outsource data entry requirements it helps to improve information management system whether you are running a small business or large business company because information is vital asset in this industry.

You can release from the difficulty of all your information processing requirements because of high quality, cost effective services provided by data entry companies which also helps you to focus on other business development processes which are also important.

Specially trained and skilled excel, word entry professional from offshore countries provide you excellent services with significant suggestions. There are several advantages of information entry outsourcing some majors are:

o Lowest Possible Data-Entry Cost
o Accurate and Fast delivery
o Access of specialized service
o Increased client satisfaction
o Savings manpower and training costs
o Focusing energy and workforce on your core business

Some of the most important services provided by outsourcing companies are described below:

Catalogs Entries
Directories Entries
Numeric Information
Textual Information
Data Capture and Data Collection
Image Information
Online Form Information
OCR/ICR Processing

By outsourcing data-entry services you can enjoy the convenience and security of work done by data entries companies. DataEntryOutsourcing is provide complete data online entry services and you can get more information about our data online entry company so visit our website.


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Backtesting & Data Mining

In this article we'll take a look at two related practices that are widely used by traders called Backtesting and Data Mining. These are techniques that are powerful and valuable if we use them correctly, however traders often misuse them. Therefore, we'll also explore two common pitfalls of these techniques, known as the multiple hypothesis problem and overfitting and how to overcome these pitfalls.

Backtesting

Backtesting is just the process of using historical data to test the performance of some trading strategy. Backtesting generally starts with a strategy that we would like to test, for instance buying GBP/USD when it crosses above the 20-day moving average and selling when it crosses below that average. Now we could test that strategy by watching what the market does going forward, but that would take a long time. This is why we use historical data that is already available.

"But wait, wait!" I hear you say. "Couldn't you cheat or at least be biased because you already know what happened in the past?" That's definitely a concern, so a valid backtest will be one in which we aren't familiar with the historical data. We can accomplish this by choosing random time periods or by choosing many different time periods in which to conduct the test.

Now I can hear another group of you saying, "But all that historical data just sitting there waiting to be analyzed is tempting isn't it? Maybe there are profound secrets in that data just waiting for geeks like us to discover it. Would it be so wrong for us to examine that historical data first, to analyze it and see if we can find patterns hidden within it?" This argument is also valid, but it leads us into an area fraught with danger...the world of Data Mining

Data Mining

Data Mining involves searching through data in order to locate patterns and find possible correlations between variables. In the example above involving the 20-day moving average strategy, we just came up with that particular indicator out of the blue, but suppose we had no idea what type of strategy we wanted to test? That's when data mining comes in handy. We could search through our historical data on GBP/USD to see how the price behaved after it crossed many different moving averages. We could check price movements against many other types of indicators as well and see which ones correspond to large price movements.

The subject of data mining can be controversial because as I discussed above it seems a bit like cheating or "looking ahead" in the data. Is data mining a valid scientific technique? On the one hand the scientific method says that we're supposed to make a hypothesis first and then test it against our data, but on the other hand it seems appropriate to do some "exploration" of the data first in order to suggest a hypothesis. So which is right? We can look at the steps in the Scientific Method for a clue to the source of the confusion. The process in general looks like this:

Observation (data) >>> Hypothesis >>> Prediction >>> Experiment (data)

Notice that we can deal with data during both the Observation and Experiment stages. So both views are right. We must use data in order to create a sensible hypothesis, but we also test that hypothesis using data. The trick is simply to make sure that the two sets of data are not the same! We must never test our hypothesis using the same set of data that we used to suggest our hypothesis. In other words, if you use data mining in order to come up with strategy ideas, make sure you use a different set of data to backtest those ideas.

Now we'll turn our attention to the main pitfalls of using data mining and backtesting incorrectly. The general problem is known as "over-optimization" and I prefer to break that problem down into two distinct types. These are the multiple hypothesis problem and overfitting. In a sense they are opposite ways of making the same error. The multiple hypothesis problem involves choosing many simple hypotheses while overfitting involves the creation of one very complex hypothesis.

The Multiple Hypothesis Problem

To see how this problem arises, let's go back to our example where we backtested the 20-day moving average strategy. Let's suppose that we backtest the strategy against ten years of historical market data and lo and behold guess what? The results are not very encouraging. However, being rough and tumble traders as we are, we decide not to give up so easily. What about a ten day moving average? That might work out a little better, so let's backtest it! We run another backtest and we find that the results still aren't stellar, but they're a bit better than the 20-day results. We decide to explore a little and run similar tests with 5-day and 30-day moving averages. Finally it occurs to us that we could actually just test every single moving average up to some point and see how they all perform. So we test the 2-day, 3-day, 4-day, and so on, all the way up to the 50-day moving average.

Now certainly some of these averages will perform poorly and others will perform fairly well, but there will have to be one of them which is the absolute best. For instance we may find that the 32-day moving average turned out to be the best performer during this particular ten year period. Does this mean that there is something special about the 32-day average and that we should be confident that it will perform well in the future? Unfortunately many traders assume this to be the case, and they just stop their analysis at this point, thinking that they've discovered something profound. They have fallen into the "Multiple Hypothesis Problem" pitfall.

The problem is that there is nothing at all unusual or significant about the fact that some average turned out to be the best. After all, we tested almost fifty of them against the same data, so we'd expect to find a few good performers, just by chance. It doesn't mean there's anything special about the particular moving average that "won" in this case. The problem arises because we tested multiple hypotheses until we found one that worked, instead of choosing a single hypothesis and testing it.

Here's a good classic analogy. We could come up with a single hypothesis such as "Scott is great at flipping heads on a coin." From that, we could create a prediction that says, "If the hypothesis is true, Scott will be able to flip 10 heads in a row." Then we can perform a simple experiment to test that hypothesis. If I can flip 10 heads in a row it actually doesn't prove the hypothesis. However if I can't accomplish this feat it definitely disproves the hypothesis. As we do repeated experiments which fail to disprove the hypothesis, then our confidence in its truth grows.

That's the right way to do it. However, what if we had come up with 1,000 hypotheses instead of just the one about me being a good coin flipper? We could make the same hypothesis about 1,000 different people...me, Ed, Cindy, Bill, Sam, etc. Ok, now let's test our multiple hypotheses. We ask all 1000 people to flip a coin. There will probably be about 500 who flip heads. Everyone else can go home. Now we ask those 500 people to flip again, and this time about 250 will flip heads. On the third flip about 125 people flip heads, on the fourth about 63 people are left, and on the fifth flip there are about 32. These 32 people are all pretty amazing aren't they? They've all flipped five heads in a row! If we flip five more times and eliminate half the people each time on average, we will end up with 16, then 8, then 4, then 2 and finally one person left who has flipped ten heads in a row. It's Bill! Bill is a "fantabulous" flipper of coins! Or is he?

Well we really don't know, and that's the point. Bill may have won our contest out of pure chance, or he may very well be the best flipper of heads this side of the Andromeda galaxy. By the same token, we don't know if the 32-day moving average from our example above just performed well in our test by pure chance, or if there is really something special about it. But all we've done so far is to find a hypothesis, namely that the 32-day moving average strategy is profitable (or that Bill is a great coin flipper). We haven't actually tested that hypothesis yet.

So now that we understand that we haven't really discovered anything significant yet about the 32-day moving average or about Bill's ability to flip coins, the natural question to ask is what should we do next? As I mentioned above, many traders never realize that there is a next step required at all. Well, in the case of Bill you'd probably ask, "Aha, but can he flip ten heads in a row again?" In the case of the 32-day moving average, we'd want to test it again, but certainly not against the same data sample that we used to choose that hypothesis. We would choose another ten-year period and see if the strategy worked just as well. We could continue to do this experiment as many times as we wanted until our supply of new ten-year periods ran out. We refer to this as "out of sample testing", and it's the way to avoid this pitfall. There are various methods of such testing, one of which is "cross validation", but we won't get into that much detail here.


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Data Mining - Critical for Businesses to Tap the Unexplored Market

Knowledge discovery in databases (KDD) is an emerging field and is increasingly gaining importance in today's business. The knowledge discovery process, however, is vast, involving understanding of the business and its requirements, data selection, processing, mining and evaluation or interpretation; it does not have any pre-defined set of rules to go about solving a problem. Among the other stages, the data mining process holds high importance as the task involves identification of new patterns that have not been detected earlier from the dataset. This is relatively a broad concept involving web mining, text mining, online mining etc.

What Data Mining is and what it is not?

The data mining is the process of extracting information, which has been collected, analyzed and prepared, from the dataset and identifying new patterns from that information. At this juncture, it is also important to understand what it is not. The concept is often misunderstood for knowledge gathering, processing, analysis and interpretation/ inference derivation. While these processes are absolutely not data mining, they are very much necessary for its successful implementation.

The 'First-mover Advantage'

One of the major goals of the data mining process is to identify an unknown or rather unexplored segment that had always existed in the business or industry, but was overlooked. The process, when done meticulously using appropriate techniques, could even make way for niche segments providing companies the first-mover advantage. In any industry, the first-mover would bag the maximum benefits and exploit resources besides setting standards for other players to follow. The whole process is thus considered to be a worthy approach to identify unknown segments.

The online knowledge collection and research is the concept involving many complications and, therefore, outsourcing the data mining services often proves viable for large companies that cannot devote time for the task. Outsourcing the web mining services or text mining services would save an organization's productive time which would otherwise be spent in researching.

The data mining algorithms and challenges

Every data mining task follows certain algorithms using statistical methods, cluster analysis or decision tree techniques. However, there is no single universally accepted technique that can be adopted for all. Rather, the process completely depends on the nature of the business, industry and its requirements. Thus, appropriate methods have to be chosen depending upon the business operations.

The whole process is a subset of knowledge discovery process and as such involves different challenges. Analysis and preparation of dataset is very crucial as the well-researched material could assist in extracting only the relevant yet unidentified information useful for the business. Hence, the analysis of the gathered material and preparation of dataset, which also considers industrial standards during the process, would consume more time and labor. Investment is another major challenge in the process as it involves huge cost on deploying professionals with adequate domain knowledge plus knowledge on statistical and technological aspects.

The importance of maintaining a comprehensive database prompted the need for data mining which, in turn, paved way for niche concepts. Though the concept has been present for years now, companies faced with ever growing competition have realized its importance only in the recent years. Besides being relevant, the dataset from where the information is actually extracted also has to be sufficient enough so as to pull out and identify a new dimension. Yet, a standardized approach would result in better understanding and implementation of the newly identified patterns.


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Data Mining As a Process

The data mining process is also known as knowledge discovery. It can be defined as the process of analyzing data from different perspectives and then summarizing the data into useful information in order to improve the revenue and cut the costs. The process enables categorization of data and the summary of the relationships is identified. When viewed in technical terms, the process can be defined as finding correlations or patterns in large relational databases. In this article, we look at how data mining works its innovations, the needed technological infrastructures and the tools such as phone validation.

Data mining is a relatively new term used in the data collection field. The process is very old but has evolved over the time. Companies have been able to use computers to shift over the large amounts of data for many years. The process has been used widely by the marketing firms in conducting market research. Through analysis, it is possible to define the regularity of customers shopping. How the items are bought. It is also possible to collect information needed for the establishment of revenue increase platform. Nowadays, what aides the process is the affordable and easy disk storage, computer processing power and applications developed.

Data extraction is commonly used by the companies that are after maintaining a stronger customer focus no matter where they are engaged. Most companies are engaged in retail, marketing, finance or communication. Through this process, it is possible to determine the different relationships between the varying factors. The varying factors include staffing, product positioning, pricing, social demographics, and market competition.

A data-mining program can be used. It is important note that the data mining applications vary in types. Some of the types include machine learning, statistical, and neural networks. The program is interested in any of the following four types of relationships: clusters (in this case the data is grouped in relation to the consumer preferences or logical relationships), classes (in this the data is stored and finds its use in the location of data in the per-determined groups), sequential patterns (in this case the data is used to estimate the behavioral patterns and patterns), and associations (data is used to identify associations).

In knowledge discovery, there are different levels of data analysis and they include genetic algorithms, artificial neural networks, nearest neighbor method, data visualization, decision trees, and rule induction. The level of analysis used depends on the data that is visualized and the output needed.

Nowadays, data extraction programs are readily available in different sizes from PC platforms, mainframe, and client/server. In the enterprise-wide uses, size ranges from the 10 GB to more than 11 TB. It is important to note that two crucial technological drivers are needed and are query complexity and, database size. When more data is needed to be processed and maintained, then a more powerful system is needed that can handle complex and greater queries.

With the emergence of professional data mining companies, the costs associated with process such as web data extraction, web scraping, web crawling and web data mining have greatly being made affordable.


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Basics of Web Data Mining and Challenges in Web Data Mining Process

Today World Wide Web is flooded with billions of static and dynamic web pages created with programming languages such as HTML, PHP and ASP. Web is great source of information offering a lush playground for data mining. Since the data stored on web is in various formats and are dynamic in nature, it's a significant challenge to search, process and present the unstructured information available on the web.

Complexity of a Web page far exceeds the complexity of any conventional text document. Web pages on the internet lack uniformity and standardization while traditional books and text documents are much simpler in their consistency. Further, search engines with their limited capacity can not index all the web pages which makes data mining extremely inefficient.

Moreover, Internet is a highly dynamic knowledge resource and grows at a rapid pace. Sports, News, Finance and Corporate sites update their websites on hourly or daily basis. Today Web reaches to millions of users having different profiles, interests and usage purposes. Every one of these requires good information but don't know how to retrieve relevant data efficiently and with least efforts.

It is important to note that only a small section of the web possesses really useful information. There are three usual methods that a user adopts when accessing information stored on the internet:

• Random surfing i.e. following large numbers of hyperlinks available on the web page.
• Query based search on Search Engines - use Google or Yahoo to find relevant documents (entering specific keywords queries of interest in search box)
• Deep query searches i.e. fetching searchable database from eBay.com's product search engines or Business.com's service directory, etc.

To use the web as an effective resource and knowledge discovery researchers have developed efficient data mining techniques to extract relevant data easily, smoothly and cost-effectively.


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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

Can You Use Data Mining to Determine What Internet Marketing Tactic Works the Best?

Data mining in general is a sequence that analyzes and collects data from people about something that they are already doing. In essence it collects information from people about things that they normally do, for instance you can do a survey on how many people enjoy making money on the internet. In depth you can collect the demographics on how great a marketing method is preforming for other marketers that are using that same method fir there businesses and see if you should get involved with it as well.

You can also use data mining to collect intel on products that are out there in the market, and you can use the results to compare to your products, if you have any. Data mining can be used as a tool that can help you analyze information that you are already accessing, but with this tool you can funnel it in the right direction. This direction is to help depict information that you are going to need to strategically market your business on the internet or maybe target a specific group in which you can generate sales from.

How can a aspiring entrepreneur in the network marketing industry apply data mining in there small business?

First off, for any new marketer entering the industry there is a series of questions that the marketer have to ask in order for he or she can do a synopsis for his niche. Questions marketers can go out and ask in a survey is, What are your interests? Or what do you do for a living? Do you like what you do for a living? He or she can go as far as, Have you ever thought of owning your business? or How many people are interested being financially independent? These question are to basically give the marketer feel for what there niche is looking for, and how he can position the business, in a strategic way.

Many top marketers use data mining to compare tactics to see what would be a smarter way to promote there businesses. I'm sure you've those annoying pop up promotions that are all over the internet, that say " Get paid to do this survey " Well those are marketers out there that are using data mining to get information for there business. Now I'm not saying go out there and begin spamming everyone that comes on your website your products, because at the end of the day, spam is spam, I do not recommend spamming. But I do recommend using data mining to your advantage.


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Data Entry Services in India Are Getting Famous in the World!

Outsourcing has become the most profitable business in the world. This business is growing in India and other part of the world. These services are getting famous in the world and most of the business owners are saving their lots of money by doing outsourcing to different countries where India comes in top in the outsourcing. By outsourcing your offline and online information entry jobs, your company will maintain properly organized and up-to-date records of the employees and other important stuff. These jobs are usually done in the home environment.

India is very popular in providing the BPO services for their customers. There is large scale of BPO service providers running their business in India. The employees working in these offices are also very competent and trained. Data entry services in India is very popular all around the world because of having the access of BPO experts and the web data extraction experts.

What these BPO services provide you?

There are many business across the globe running on the outsource services, BPO services in India provides the ease of life to the business owner want quick and fast data entry work.

There are many well reputed firms working in India and doing their best to finish and deliver comes punctually. They're professional well equipped with the newest technology and software and more importantly with the professional labor work. They are fully trained and expert in their niche so if a business owner take the services then they get the in time work and quality. When you will select any BPO expert then you will find the following data entry expertise in these professional companies.

1. You will find the handwritten material with the help of experts.
2. Knowledge entry of e-books, directories, image files and etc.
3. You will also get the best services of data processing.
4. Business card knowledge entry
5. Bills and survey services which will help you to Maintain and correct records.
6. Alpha numeric data entry services
7. Data entry free trails.

Thousand of online BPO jobs are also available on the Indian big job portals and other data entry work. These services and work force reduce your workload and will enhance your productivity of your business. Outsourcing the right choice by any business owner because it reduces your total cost and you get the perfect and reliable work. When you approach to any professional service provider firm in India then it reduce the turnaround time and you get the professional data entry services.


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How to Decide If Off-Line Or Online Data Entry Is Right For You

Home-based, or outsourced, data entry generally refers to performing a set of specific tasks, usually involving typing of either textual or numerical data, provided by an employer to be done on a personal computer by a person working from or at home.

There are essentially two types of work a person may be supplied with, namely off-line or online data entry.

Defining Off-Line Work
In general, off-line work consists mainly of given information being entered into a specific designated database, following an employers detailed instructions, without making use of the Internet in the process. A pre-set time-frame for work to be completed in is usually given.

As a rule, payment is either on an hourly rate or fixed. Examples of work to be done could be completing off-line data sheets or forms and/ or re-formatting of data into MS Word, Access or Excel formats, as well as collection of specific data from one or more off-line databases.

Definition of Online Work
Online data entry, as the name suggests, is the use of the Internet in connection with specific tasks provided by the employer. Many businesse prefer this type, as it allows them to maintain a focus on the core activities of their business.

It is usually a more cost-effective option for the employers, as they are able to make use of reliable, efficient services without incurring the cost of continual overheads.

Online data entry can, among other possibilities, consist of entering or researching data on websites, completing and submitting Internet forms, data or image processing, proofreading and/ or correcting data, indexing, etc.

Services are generally paid for at either a fixed rate or per quantity of work submitted. While the fixed rate can be better where smaller assignments are concerned, the per quantity rate often works out more favourably for the worker.

Again, the majority of work is provided with a very specific deadline by which the work has to be completed. As these deadlines are often very tight, employees need to ensure that they really are able to complete the work within the given time frame before taking on the work.

The Comparison
It becomes clear that there are essentially not many major differences between off-line and online data entry. The main difference lies in the fact that, as the terms suggest, one type is performed using the Internet, while the other is performed off-line.

Online work does have tendency to be more varied, in particular if a project has to be researched. Obviously the scope is greater with online work, as many more sources of information or final destinations for certain projects can be accessed.

Protecting One's Interests
In either case, but in particular where online employers are concerned, it is essential that the potential employee ensures the employer is genuine. Unfortunately, the world of online data entry is being used by many con-artists using people's desre to find work as an easy way of making money for themselves.

This is particularly the case with the many so-called programs offered to data entry workers. Many, if not all of them, are scams intended to make a quick profit out of unsuspecting individuals. The basic rule of thumb when looking for online work is that no genuine employer will ask potential employees to make a payment.

Nowhere on the planet do people have to pay to be able to work, they are paid by the employer, never the other way around. There are also companies and individuals who will indeed provide work, but will then not provide the promised payment for it.

To protect oneself against such pitfalls, potential employers should be researched before an agreement to work for them has been made.



Source: http://ezinearticles.com/?How-to-Decide-If-Off-Line-Or-Online-Data-Entry-Is-Right-For-You&id=6563879

Data Mining Explained

Overview
Data mining is the crucial process of extracting implicit and possibly useful information from data. It uses analytical and visualization techniques to explore and present information in a format which is easily understandable by humans.

Data mining is widely used in a variety of profiling practices, such as fraud detection, marketing research, surveys and scientific discovery.

In this article I will briefly explain some of the fundamentals and its applications in the real world.

Herein I will not discuss related processes of any sorts, including Data Extraction and Data Structuring.

The Effort
Data Mining has found its application in various fields such as financial institutions, health-care & bio-informatics, business intelligence, social networks data research and many more.

Businesses use it to understand consumer behavior, analyze buying patterns of clients and expand its marketing efforts. Banks and financial institutions use it to detect credit card frauds by recognizing the patterns involved in fake transactions.

The Knack
There is definitely a knack to Data Mining, as there is with any other field of web research activities. That is why it is referred as a craft rather than a science. A craft is the skilled practicing of an occupation.

One point I would like to make here is that data mining solutions offers an analytical perspective into the performance of a company depending on the historical data but one need to consider unknown external events and deceitful activities. On the flip side it is more critical especially for Regulatory bodies to forecast such activities in advance and take necessary measures to prevent such events in future.

In Closing
There are many important niches of Web Data Research that this article has not covered. But I hope that this article will provide you a stage to drill down further into this subject, if you want to do so!

Should you have any queries, please feel free to mail me. I would be pleased to answer each of your queries in detail.


Source: http://ezinearticles.com/?Data-Mining-Explained&id=4341782

Online Data Entry and Data Mining Services

Data entry job involves transcribing a particular type of data into some other form. It can be either online or offline. The input data may include printed documents like Application forms, survey forms, registration forms, handwritten documents etc.

Data entry process is an inevitable part of the job to any organization. One way or other each organization demands data entry. Data entry skills vary depends upon the nature of the job requirement, in some cases data to be entered from a hard copy formats and in some other cases data to be entered directly into a web portal. Online data entry job generally requires the data to be entered in to any online data base.

For a super market, data associate might be required to enter the goods which have sold in a particular day and the new goods received in a particular day to maintain the stock well in order. Also, by doing this the concerned authorities will get an idea about the sale particulars of each commodity as they requires. In another example, an office the account executive might be required to input the day to day expenses in to the online accounting database in order to keep the account well in order.

The aim of the data mining process is to collect the information from reliable online sources as per the requirement of the customer and convert it to a structured format for the further use. The major source of data mining is any of the internet search engine like Google, Yahoo, Bing, AOL, MSN etc. Many search engines such as Google and Bing provide customized results based on the user's activity history. Based on our keyword search, the search engine lists the details of the websites from where we can gather the details as per our requirement.

Collect the data from the online sources such as Company Name, Contact Person, Profile of the Company, Contact Phone Number of Email ID Etc. are doing for the marketing activities. Once the data is gathered from the online sources into a structured format, the marketing authorities will start their marketing promotions by calling or emailing the concerned persons, which may result to create a new customer. So basically data mining is playing a vital role in today's business expansions. By outsourcing the data entry and its related works, you can save the cost that would be incurred in setting up the necessary infrastructure and employee cost.


Source: http://ezinearticles.com/?Online-Data-Entry-and-Data-Mining-Services&id=7713395

How Your Online Information is Stolen - The Art of Web Scraping and Data Harvesting

Web scraping, also known as web/internet harvesting involves the use of a computer program which is able to extract data from another program's display output. The main difference between standard parsing and web scraping is that in it, the output being scraped is meant for display to its human viewers instead of simply input to another program.

Therefore, it isn't generally document or structured for practical parsing. Generally web scraping will require that binary data be ignored - this usually means multimedia data or images - and then formatting the pieces that will confuse the desired goal - the text data. This means that in actually, optical character recognition software is a form of visual web scraper.

Usually a transfer of data occurring between two programs would utilize data structures designed to be processed automatically by computers, saving people from having to do this tedious job themselves. This usually involves formats and protocols with rigid structures that are therefore easy to parse, well documented, compact, and function to minimize duplication and ambiguity. In fact, they are so "computer-based" that they are generally not even readable by humans.

If human readability is desired, then the only automated way to accomplish this kind of a data transfer is by way of web scraping. At first, this was practiced in order to read the text data from the display screen of a computer. It was usually accomplished by reading the memory of the terminal via its auxiliary port, or through a connection between one computer's output port and another computer's input port.

It has therefore become a kind of way to parse the HTML text of web pages. The web scraping program is designed to process the text data that is of interest to the human reader, while identifying and removing any unwanted data, images, and formatting for the web design.

Though web scraping is often done for ethical reasons, it is frequently performed in order to swipe the data of "value" from another person or organization's website in order to apply it to someone else's - or to sabotage the original text altogether. Many efforts are now being put into place by webmasters in order to prevent this form of theft and vandalism.


Source: http://ezinearticles.com/?How-Your-Online-Information-is-Stolen---The-Art-of-Web-Scraping-and-Data-Harvesting&id=923976

Preference to Offshore Document Data Entry Services

A number or business organizations if different industries are seeking competent and precise document data entry services to maintain their business records safe for future references. Document data entry has advanced as a quickly developing and active industry structure almost accept in all major companies of the world. The companies doing businesses these days are undergoing rapid changes and therefore the need for services is becoming all the more crucial.

To get success you need to accomplish more understanding about the market, your business, clients as well as the prevailing factors that influence your business. A considerable amount of document is in one or the other way included in this entire process. These services is helpful in taking crucial decisions for the organization. It also provides you a standard in understanding the current and future business status of your company.

In this information age data-entry from documents and data conversion have become important elements for most business houses. The requirement for document services has reached zenith since companies work on processes like business merger and acquisitions, as well as new technology developments. In such scenarios having access to the right kind of data at the right time is very crucial and that is why companies opt for reliable services.

These services covers a range of professional business oriented activities such as document plus image processing to image editing as well as catalog processing. A few noteworthy examples of from documents include: PDF document indexing, insurance claim entry, online data capture as well as creating new databases. These services are important in industries like insurance companies, banks, government departments and airlines.

Companies such as Offshore and outsource and others offer an entire gamut of first rate data services. Actually, getting services from documents offshore to developing yet competent countries like India has made the process highly economical plus quality driven too.

Business giants around the world have realized multiple advantages associated in Offshore-Data-Entry. Companies not only prosper because of quality services but are also benefited because of better turn around time, maintaining confidentiality of data as well as economic rates.

Though the company works in all form of documents, there are few below mentioned areas where it specializes:

• Document data entry
• Document data entry conversion
• Document data processing
• Document data capture services
• Web data extraction
• Document scanning indexing

Since reputable companies like Offshore Data-Entry hire only well qualified and trained candidates work satisfaction is guaranteed. There are several steps involved in the quality check (QC) process and therefore accuracy level is maintained to 99.995% ensuring that the end result is delivered to the client far beyond his expectation.




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