Basics of Online Web Research, Web Mining & Data Extraction Services

The evolution of the World Wide Web and Search engines has brought the abundant and ever growing pile of data and information on our finger tips. It has now become a popular and important resource for doing information research and analysis.

Today, Web research services are becoming more and more complicated. It involves various factors such as business intelligence and web interaction to deliver desired results.

Web Researchers can retrieve web data using search engines (keyword queries) or browsing specific web resources. However, these methods are not effective. Keyword search gives a large chunk of irrelevant data. Since each webpage contains several outbound links it is difficult to extract data by browsing too.

Web mining is classified into web content mining, web usage mining and web structure mining. Content mining focuses on the search and retrieval of information from web. Usage mining extract and analyzes user behavior. Structure mining deals with the structure of hyperlinks.

Web mining services can be divided into three subtasks:

Information Retrieval (IR): The purpose of this subtask is to automatically find all relevant information and filter out irrelevant ones. It uses various Search engines such as Google, Yahoo, MSN, etc and other resources to find the required information.

Generalization: The goal of this subtask is to explore users' interest using data extraction methods such as clustering and association rules. Since web data are dynamic and inaccurate, it is difficult to apply traditional data mining techniques directly on the raw data.

Data Validation (DV): It tries to uncover knowledge from the data provided by former tasks. Researcher can test various models, simulate them and finally validate given web information for consistency.

Should you have any queries regarding Web research or Data mining applications, please feel free to contact us. We would be pleased to answer each of your queries in detail. Find more information at http://www.outsourcingwebresearch.com




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The Need for Specialised Data Mining Techniques for Web 2.0

Web 2.0 is not exactly a new version of the Web, but rather a way to describe a new generation of interactive websites centred on the user. These are websites that offer

interactive information sharing, as well as collaboration - a case in point being wikis and blogs - and is now expanding to other areas as well. These new sites are the result of new technologies and new ideas and are on the cutting edge of Web development. Due to their novelty, they create a rather interesting challenge for data mining.

Data mining is simply a process of finding patterns in masses of data. There is such a vast plethora of information out there on the Web that it is necessary to use data mining tools to make sense of it. Traditional data mining techniques are not very effective when used on these new Web 2.0 sites because the user interface is so varied. Since Web 2.0 sites are created largely by user-supplied content, there is even more data to mine for valuable information. Having said that, the additional freedom in the format ensures that it is much more difficult to sift through the content to find what is usable.The data available is very valuable, so where there is a new platform, there must be new techniques developed for mining the data. The trick is that the data mining methods must themselves be flexible as the sites they are targeting are flexible. In the initial days of the World Wide Web, which was referred to as Web 1.0, data mining programs knew where to look for the desired information. Web 2.0 sites lack structure, meaning there is no single spot for the mining program to target. It must be able to scan and sift through all of the user-generated content to find what is needed. The upside is that there is a lot more data out there, which means more and more accurate results if the data can be properly utilized. The downside is that with all that data, if the selection criteria are not specific enough, the results will be meaningless. Too much of a good thing is definitely a bad thing. Wikis and blogs have been around long enough now that enough research has been carried out to understand them better. This research can now be used, in turn, to devise the best possible data mining methods. New algorithms are being developed that will allow data mining applications to analyse this data and return useful. Another problem is that there are many cul-de-sacs on the internet now, where groups of people share information freely, but only behind walls/barriers that keep it away from the genera results.

The main challenge in developing these algorithms does not lie with finding the data, because there is too much of it. The challenge is filtering out irrelevant data to get to the meaningful one. At this point none of the techniques are perfected. This makes Web 2.0 data mining an exciting and frustrating field, and yet another challenge in the never ending series of technological hurdles that have stemmed from the internet. There are numerous problems to overcome. One is the inability to rely on keywords, which used to be the best method to search. This does not allow for an understanding of context or sentiment associated with the keywords which can drastically vary the meaning of the keyword population. Social networking sites are a good example of this, where you can share information with everyone you know, but it is more difficult for that information to proliferate outside of those circles. This is good in terms of protecting privacy, but it does not add to the collective knowledge base and it can lead to a skewed understanding of public sentiment based on what social structures you have entry into. Attempts to use artificial intelligence have been less than successful because it is not adequately focused in its methodology. Data mining depends on the collection of data and sorting the results to create reports on the individual metrics that are the focus of interest. The size of the data sets are simply too large for traditional computational techniques to be able to tackle them. That is why a new answer needs to be found. Data mining is an important necessity for managing the backhaul of the internet. As Web 2.0 grows exponentially, it is increasingly hard to keep track of everything that is out there and summarize and synthesize it in a useful way. Data mining is necessary for companies to be able to really understand what customers like and want so that they can create products to meet these needs. In the increasingly aggressive global market, companies also need the reports resulting from data mining to remain competitive. If they are unable to keep track of the market and stay abreast of popular trends, they will not survive. The solution has to come from open source with options to scale databases depending on needs. There are companies that are now working on these ideas and are sharing the results with others to further improve them. So, just as open source and collective information sharing of Web 2.0 created these new data mining challenges, it will be the collective effort that solves the problems as well.

It is important to view this as a process of constant improvement, not one where an answer will be absolute for all time. Since its advent, the internet has changed quite significantly as well as the way users interact with it. Data mining will always be a critical part of corporate internet usage and its methods will continue to evolve just as the Web and its content does.

There is a huge incentive for creating better data mining solutions to tackle the complexities of Web 2.0. For this reason, several companies exist just for the purpose of analysing and creating solutions to the data mining problem. They find eager buyers for their applications in companies which are desperate for information on markets and potential customers. The companies in question do not simply want more data, they want better data. This requires a system that can classify and group data, and then make sense of the results.While the data mining process is expensive to start with, it is well worth for a retail company because it provides insight into the market and thus enables quick decisions.The speed at which a company which has insightful information on the marketplace can react to changes, gives it a huge advantage over the competition. Not only can the company react quickly, it is likely to steer itself in the right direction if its information is based on updated data.Advanced data mining will allow companies not only to make snap decisions, but also to plan long range strategies, based on the direction the marketplace is heading. Data mining brings the company closer to its customers. The real winners here, are the companies that have now discovered that they can make a living by improving the existing data mining techniques. They have filled a niche that was only created recently, which no one could have foreseen and have done quite a, good job at it.




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Healthcare Marketing Series - Data Mining - The 21st Century Marketing Gold Rush

There is gold in them there hills! Well there is gold right within a few blocks of your office. Mining for patients, not unlike mining for gold or drilling for oil requires either great luck or great research.

It's all about the odds.

It's true that like old Jed from the Beverly Hillbillies, you might just take a shot and strike oil. But more likely you might drill a dry hole or dig a mine and find dirt not diamonds. Without research you might be a mere 2 feet from pay dirt, but drilling or mining in just the wrong spot.

Now oil companies and gold mining companies spend millions, if not, billions of dollars studying where and how to effectively find the "mother load". If market research is good enough for the big boys, it should be good enough for the healthcare provider. Remember as a health care professional you probably don't have the extras millions laying around to squander on trial and error marketing.

If you did there would be little need for you to market to find new patients to help.

In previous articles in the Health Care Marketing Series we talked about developing a marketing strategy, using metrics to measure the performance of your marketing execution, developing effective marketing warheads based on your marketing strategy, evaluating the most efficient ways to deliver those warheads, your marketing missile systems, and tying several marketing methods together into a marketing MIRV.

If you have been following along with our articles and starting to integrate the concepts detailed in them, by now you should have an excellent marketing infrastructure. Ready to launch laser guided marketing missiles tipped with nuclear marketing MIRVs. The better you have done your research, the more detailed your marketing strategy, the more effective and efficient your delivery systems, the bigger bang you'll receive from your marketing campaign. And ultimately the more lives you will help to change of patients that truly can benefit from your skills and talents as a doctor.

Sounds like you're ready for healthcare marketing shock and awe.

Everything is ready to launch, this is great, press the button and fire away!

Ah, but wait just a minute, General. What is the target? Where are they? What are the aiming coordinates?

The target? Why of course all those sick people out there.

Where are they? Well of course, out there!

The coordinates? Man just press the button, carpet bomb man. Carpet bomb!

This scenario is designed to show you how quickly the wheels can come off even the best intended marketing war machine. It brings us back full circle. We are right back to our original article on marketing strategy.

But this time we are going to introduce the concept of data mining. If you remember, our article on marketing strategy talked about doing research. We talked about research as the true cornerstone of all marketing efforts.

What is the target, General?

Answering this question is a little difficult and the truth is each healthcare provider needs to determine his or her high value target. And more importantly needs to know how to determine his or her high value targets.

Let's go back to our launch scenario to illustrate this point. Let's continue with our military analogy. Let's say we have several aircraft carriers, a few destroyers and a fleet of rowboats, making up our marketing battlefield.

As we have discussed previously, waging a marketing war, like any war, consumes resources. So do we want to launch our nuclear marketing MIRVs, the most valuable resources in our arsenal, and target the fleet of rowboats?

Or would it be wiser to target those aircraft carriers?

Well the obvious answer is "get those carriers".

But here is where things get a little tricky. One man's aircraft carrier is another man's rowboat.

You have to data mine your practice to determine which targets are high value targets.

What goes into that data mining process? Well first and foremost, what conditions do you 1.like to treat, 2. have a proven track record of treating and 3. obtain a reasonable reimbursement for treating.

In my own practice, I typically do not like or enjoy treating shoulder problems. I don't know if I don't like treating shoulders because I haven't had great results with them or if I haven't had great results, because I don't like treating them. Needless to say my reimbursement for treating shoulder cases is relatively low.

So do I really want to carpet bomb my marketing terrain and come up with 10 new cases of rotator cuff tears? These cases, for more than one reason, are my rowboats.

On the contrary, I like to treat neurological conditions like chronic pain; Neuropathy patients, Spinal Stenosis patients, Tinnitus patients, patients with Parkinson's Disease and Multiple Sclerosis patients. I've had results with these types of cases that have been good enough to publish. Because they are complex and difficult cases, I obtain a better than average reimbursement for my efforts. These cases are my aircraft carriers. If my marketing campaign brings me ten cases with these types of problems, chances are that the patient will obtain some great relief, I will find working with them an intellectual and stimulating challenge and my marketing efforts will bring me a handsome return on investment.

So the first lesson of data mining is to identify your aircraft carriers. They must be "your" aircraft carriers. You must have a good personal track record of helping these types of patients. You should enjoy treating these types of cases. And you should be rewarded for your time and expertise.

That's the first step in the process. Identifying your high value targets. The next step is THE most important aspect of healthcare marketing. As I discussed above, I enjoy working with complex neurological cases. But how many of these types of patients exist in my marketing terrain and are they looking for the type of help I can offer?

Being able to accurately answer these important questions is the single most valuable information I can extract using data mining.

It doesn't matter if I like treating these cases. It doesn't matter if I make a good living treating these cases. It doesn't matter if my success in treating these cases has made the local news. What matters is 1. do these types of cases exist in my neighborhood and 2. are they looking for the help I can provide to them?

You absolutely positively need to know who is looking for what in your marketing terrain and if what people are clamoring for is what you have to offer.

This knowledge is the most powerful tool in your marketing arsenal. It's your secret weapon. It is the foundation of your marketing strategy. It is so important that you should consider moving your office if the results of your data mining don't reveal an ocean full of aircraft carriers in your marketing terrain for you to target.

If your market research does not reveal an abundance of aircraft carriers on your horizon, you need to either 1. move to a new battlefield, 2. re-target your efforts towards the destroyers in your market or 3. try to create a market.

Let's look at your last choice. Trying to create a market. Unless you are Coke or Pepsi, your ability to create a market as a health care provider is extremely limited. To continue on with our analogy, to create a market requires converting rowboats into, at least, destroyers, but better yet aircraft carriers.

What would it cost if you took a rowboat to a ship yard and told them to rebuild it as an aircraft carrier?

This is what you face if you try to create a market where none exists. Unless you have a personality flaw and thrive on selling ice to Eskimos, creating a market is not a rewarding proposition.

So scratch this option off the table right now.

What about re-targeting your campaign towards destroyers? That's a viable option. It's a good option. It's probably your best option. It's an option that will likely give you your best return on investment. It is recommended that you focus your arsenal on the destroyers while at the same time never passing on an opportunity to sink an aircraft carrier.

So what is the secret? How do you data mine for aircraft carriers?

Well its quite simple in the internet age. Just use the services of a market research firm. I like http://www.marketresearch.com They will do the data mining for you.

They can provide market intelligence that will tell you not only what the health care aircraft carriers are, but also where they are.

With this information, you will have a competitive advantage in your marketing battlefield. You can segment, and target high value targets in your area while your competitors squander their marketing resources on rowboats. Or even worse carpet bomb and hit ocean water, not valuable targets.

Your marketing strategy should be highly targeted. Your marketing resources should be well spent. As we discussed in our very first article on true "Marketing Strategy" you should enter the battle against your competition already knowing your have won.

What gives you this dominant position in the market, is knowing ahead-of-time, who is looking for what in your marketing terrain. In other words, not trying to create a market, but rather identifying existing market niches, specifically targeting them with laser guided precision and having headlines and ad copy based on your strength versus the weakness of your competition within that niche.

This research-based marketing strategy is sure to cause a big bang with potential patients.

And leave your competition trying to sell ice to Eskimos.

I hope you see how important market research is and why it is a good thing to spend some of your marketing budget on research before you waste your marketing resources on poorly targeted low value or no-value targets. This article was intended to give you a glimpse at how to use data mining and consumer demographics information as a foundation for the development of a scientific research-based marketing strategy. This article shows you how to use existing resources to give your marketing efforts (and you) a competitive advantage.





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Data Mining - Techniques and Process of Data Mining

Data mining as the name suggest is extracting informative data from a huge source of information. It is like segregating a drop from the ocean. Here a drop is the most important information essential for your business, and the ocean is the huge database built up by you.

Recognized in Business

Businesses have become too creative, by coming up with new patterns and trends and of behavior through data mining techniques or automated statistical analysis. Once the desired information is found from the huge database it could be used for various applications. If you want to get involved into other functions of your business you should take help of professional data mining services available in the industry

Data Collection

Data collection is the first step required towards a constructive data-mining program. Almost all businesses require collecting data. It is the process of finding important data essential for your business, filtering and preparing it for a data mining outsourcing process. For those who are already have experience to track customer data in a database management system, have probably achieved their destination.

Algorithm selection

You may select one or more data mining algorithms to resolve your problem. You already have database. You may experiment using several techniques. Your selection of algorithm depends upon the problem that you are want to resolve, the data collected, as well as the tools you possess.

Regression Technique

The most well-know and the oldest statistical technique utilized for data mining is regression. Using a numerical dataset, it then further develops a mathematical formula applicable to the data. Here taking your new data use it into existing mathematical formula developed by you and you will get a prediction of future behavior. Now knowing the use is not enough. You will have to learn about its limitations associated with it. This technique works best with continuous quantitative data as age, speed or weight. While working on categorical data as gender, name or color, where order is not significant it better to use another suitable technique.

Classification Technique

There is another technique, called classification analysis technique which is suitable for both, categorical data as well as a mix of categorical and numeric data. Compared to regression technique, classification technique can process a broader range of data, and therefore is popular. Here one can easily interpret output. Here you will get a decision tree requiring a series of binary decisions.




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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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Data Mining's Importance in Today's Corporate Industry

A large amount of information is collected normally in business, government departments and research & development organizations. They are typically stored in large information warehouses or bases. For data mining tasks suitable data has to be extracted, linked, cleaned and integrated with external sources. In other words, it is the retrieval of useful information from large masses of information, which is also presented in an analyzed form for specific decision-making.

Data mining is the automated analysis of large information sets to find patterns and trends that might otherwise go undiscovered. It is largely used in several applications such as understanding consumer research marketing, product analysis, demand and supply analysis, telecommunications and so on. Data Mining is based on mathematical algorithm and analytical skills to drive the desired results from the huge database collection.

It can be technically defined as the automated mining of hidden information from large databases for predictive analysis. Web mining requires the use of mathematical algorithms and statistical techniques integrated with software tools.

Data mining includes a number of different technical approaches, such as:

    Clustering
    Data Summarization
    Learning Classification Rules
    Finding Dependency Networks
    Analyzing Changes
    Detecting Anomalies

The software enables users to analyze large databases to provide solutions to business decision problems. Data mining is a technology and not a business solution like statistics. Thus the data mining software provides an idea about the customers that would be intrigued by the new product.

It is available in various forms like text, web, audio & video data mining, pictorial data mining, relational databases, and social networks. Data mining is thus also known as Knowledge Discovery in Databases since it involves searching for implicit information in large databases. The main kinds of data mining software are: clustering and segmentation software, statistical analysis software, text analysis, mining and information retrieval software and visualization software.

Data Mining therefore has arrived on the scene at the very appropriate time, helping these enterprises to achieve a number of complex tasks that would have taken up ages but for the advent of this marvelous new technology.



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Data Mining Process - Why Outsource Data Mining Service?

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

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

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

* Retrieving Data

* Analyzing Data

* Extracting Data

* Transforming Data

* Loading Data

* Managing Databases

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

Why Outsource Data Online Mining Service?

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

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

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

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



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