How Do We Store Data for Future Data Mining Without Knowing the Future Questions?

Let's talk a little bit about "transparency versus public access" and where it's appropriate, and where it obviously isn't. Not long ago, there was an interesting feature in the TV news, a big to do about nothing, where the First Lady Michelle had traveled to Spain, and as she was on her vacation, she was on vacation as a private citizen. Now whereas, people want transparency, one has to ask where privacy must take precedent, and where transparency should be afforded.

Now, you might not think this is a very good example, but when it comes to online social networks, paparazzi, and privacy all these things are really big issues. Recall when Sarah Palin's yahoo email account was hacked by a college student, Obama supporter in TN? Obviously, that crossed the line, but where do we draw the line online?

Okay so, let's get back to the main question here; How Do We Store Online Data without violating personal property, and how do we protect national security without breaches in data, or violations of personal privacy. And if we anonimize all the data for use at a future time, how should we store it for Future Data Mining Without Knowing the Future Questions?

The information and data could be stored by region, time, frequency, and relevance. It must be stored for a multitude of purposes, and we must determine who may obtain the data, who will use the data, and what will they use it for. You see, there are different ways to store the information categories to be displayed in, or various types of tags to assign it to.

Perhaps, all the information can be stored, every bit of it, and a trusted data inquirer who wants to ask the questions, will have to explain their inquiry to an artificially intelligent computer, and it can act like a Supreme Court review on privacy. In other words, if the reason for the information is not good enough, access to that particular information will be denied. And yes it could use constitutional extrapolations, which would be philosophically based on the same analogy as surgeon seizure rules, or Fifth Amendment rights of self-determination.

As if the data itself would be alive, and the artificial intelligent computer would be the judge deciding if the prosecution would be allowed to ask those questions of the computer data system. In this case you could just store all the information you could possibly take in, and not worry about it. Okay so, that is one option; just store all the data, regardless of what it is. Or another option is to store only some data, data you believe to be important for the future, but knowing the whole truth of the past, is not completely known.

This is problematic however due to "selective prosecution" challenges. You see, one of my biggest fears would be information taken at a context, and used to condemn people or character assassinate them, or incriminate them at a trial, or in the mass media in court of public opinion using stored data, using a computer forensic chain of data, selectively gathered.

We know that the media uses this trick early and often, and they do so in often ruining people's lives. We need to be careful with that. It's serious issue. The reality is you cannot trust humans, they have proven throughout history to be a trustworthy, and you don't have to go very far to find inherent corruptness and individuals of the human species. This being my primary reason for suggesting an AI computer system.

The other concept might be to not collect the data at all, because you don't really need the data, and if you have the data available, we all know that it will be abused. Of course, the proof of innocence could also very well be in that same data, you see that point? But, the chances for abuse is far too great when humans are involved. We've had previous Presidential Administrations use IRS data to attack their enemies, and use the FBI to track political opponents. State Governors have used state police to track persons whom they've had disputes with or political adversaries as well. The abuse of power is quite common.

So, under the opposite model, you could say; No Data from Anyone, Agency, Corporation, or Organization maybe collected period; you can't collect it, you can't have it, and you can't use it. That means you can't use it for good or for evil. Some might say that would be unfortunate because a lot of that data can help prevent crimes, it can help better solve the challenges and problems of our society, and it can help artificial intelligence make the best decisions based on the best information.

If we continually make decisions based on lack of information, is this really a smart way to do planning? If on the other hand we have irrelevant information, bad information, or information taken out of context, we will never be able to make any decisions without very unfortunate unintended consequences, which is what is happening now it seems.

At our think tank we talk a lot about this, but we don't do political correctness, and we aren't about to give the human species a free pass on integrity, they don't deserve it, they haven't earned it, and we all know they cannot be trusted.



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Data Mining Prevention by Poker Sites or What to do About WrecklessJoe55

As the ingenuity of third party program designers continues to challenge poker sites that need to ensure security for their users, along comes an upstart poker site that has changed one simple rule which could essentially solve a lot of problems for any player concerned about their long term statistics being examined by ruthless competitors.

Firstly though, let's define data mining for those who may not be sure what it is exactly. Data mining is the exchange of shared profiling information amongst a community of other players. As a player on most any online poker sites, it's quite likely you have been tracked through banned programs like Poker Sherlock or Poker Edge or had your information handed over via hand histories in another program called Poker Tracker. Although Poker Stars and Party Poker make this much more difficult (scanning your hard drive for such software) there are round-about tricks that enable them to work but you wouldn't want to describe them as smooth by any means.

Now the advantage of having access to a shared database of information about opponents is that if you happened to join an online table using this software, one or some of your opponents may be displayed via HUD some valuable statistics that may help your decision making during hand. Let's say for example that you are in a hand with a player named WrecklessJoe55. You are holding Th9h and the board shows Jc8cQc Ac and 2d. There is a big river bet put to you for the remainder of your stack to call. We will ignore the odds situation here for now, because either way, it's not the easiest call in the world.

Now let's say that through a purchased exchange of 100,000 hand histories via Poker Tracker you actually have some historical information on WrecklessJoe55 which clearly makes him a maniacal LAG player. Well that information would be leading towards a call. Just the opposite, if WrecklessJoe55 had a VPIP of 11% and PFR% of 7% along with a WSDW% of 72%, then these TAG statistics would be leading toward a fold - in fact I'd be almost sure of it.

The disdain poker sites have for these types of software is that you have never played with WrecklessJoe55 and you shouldn't know that information until YOU have ascertained it, not someone else. Yes, just like a regular live poker room. The Poker Stars security staff basically once told me that that is the guideline with which they want to emulate and all security policy emanates from that thinking.

Now we get to Cake Poker, an upstart network that is actually accepting USA player online! They came up with a policy that would essentially crush the inherent value in any data-mining program. It's rather simple too, as stated on the Cake Poker website:

"CakePoker players will be granted the option of changing their Poker Nickname every 7 Days. By allowing players to change their Poker Nickname often, CakePoker thus negates the effectiveness of shared or prolonged poker data tracking."

I wonder how much time and resources Poker Stars and Party Poker would save in their overall security budget if they adopted the same policy? Allow the players to change their name! It's simple! Big kudos to CakePoker for allowing this defence, in the name of protecting its players. Now although it no longer emulates a real live poker room, it definitely makes for a level playing field, and that's something to think about for the major players to be sure.

Marty Smith reviews and rates all the online poker calculators using video as well, so you can see them being used and know which one is right for you before you invest in one. He also has a free video series focusing on poker tournament strategies for beginners.




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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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What Is the Difference Between Data Capture and Data Entry?

In business, surveys and feedback forms are excellent ways to get to know more about your customers and even your own staff. But what happens when you gather all of this data together? Sometimes it can become too much information for one or even a group of people to wade through in order to extract the vital pieces of information needed to know how to be better for your clients and what they really want.

So you might have contemplated outsourcing the service instead. Outsourcing data capture and data entry saves a lot of time, hassle and usually gives you back truly excellent results that are presentable and easily understandable. But there are differences between data entry and data capture that you need to know, whether you already have your forms back with you or you are thinking about how to set out the form itself.

Thankfully you don't need to be too specific about how to set out the form, but from a monetary and time perspective you should probably consider the differences between capture and entry.

It might seem straight forward but often these things can overlap and then you're not truly sure what you're getting but here are the fundamental differences between data capture and data entry.

Data capture is a service in which data is captured via tick or check boxes and other items where areas are filled in with simple lines or shapes in order to get the right answer. These are usually multiple choice answers or the may be yes or no questions. Essentially that is what a data capture service would offer, the ability to extract data from particular text boxes in order to gather the most and least popular responses. Once this is done it can be extracted into documents such as Excel files and can be displayed as graphs or pie charts. Data capture is also known to be fairly cheap in comparison with data entry as most of the capturing process can be automated via intelligent software usually developed by the company themselves. In turn, it is also acknowledged to be quicker to get responses back because of the nature of how the data is extracted for you.

Data entry on the other hand is almost always manually entered text, copying exactly what the person who filled out the feedback form has written. Unfortunately at this point, it is not possible for computers to completely automate handwriting and this can be a somewhat more costly procedure. A lot of companies that do this kind of work tend to outsource the work to other countries such as India and China where it is cheaper to get the work done, thus passing on the savings, but as a manual job it is always going to cost more than an automated computer system. However data entry can give a much higher insight into what it is you're looking to find out. The written word is always considerably more useful than someone who checks a box as you get a vital look into what the returner is thinking and feeling about your product or service. And because there is manual work involved the time difference can often be quite a while.

These two services are worth considering when you look into setting out your form when it comes to how much it will cost you.

If you're interested in going ahead with of the two or are looking to find out more about data capture or data entry and would like to speak to a knowledgeable company who also offer excellent rates within the UK, please visit our website. There more information about how having your data converted can help you and your organisation.




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Spatial Data Mining Systems

Data mining systems are used for a variety of different purposes. Essentially, large amounts of data are stored in one particular spot, enabling organizations and companies to access information that will help them in their own marketing and surveillance strategies. By having access to all relevant data, a company can better employ their sales and production tactics. Companies and businesses can save large sums of money by researching past consumer behaviors and producing product in relation to how well it sold at certain times. This is just a small example of what data mining can do for a company.

Spatial data mining systems rely on the same principals. However, the data stored is related directly to special data. Spatial data mining systems are also used to detect patterns, but the patterns that are being looked for are geographical patterns. Up until this point geographical information systems and spatial data mining have existed as two separate technologies. Both systems have their own individual approaches to storing geographical data. Each system has derived from its own methods and traditions, making it difficult to cross the two. Geographical information systems tend to be much more basic and only provide the most simple form of functionality. Because there became a larger demand for geographically referenced data, the basic functions of GIS represented the massive need for more sophisticated methods of mining spatial data. There is a larger demand for geographical analysis and modeling as well as digital mapping and remote sensing.

Through spatial data mining, there have been numerous benefits experienced by those who make important decisions based on geographical information systems. Public and private sector organizations have recently become aware of the huge potential of the amount of information they possess in their thematic and geographical referenced databases. There are various types of companies who can benefit from geographical data. For example, those that are in the public health sector will use this data to determine the cause for epidemics such as disease clusters. In addition, some environmental agencies will use the information collected in these databases to understand the impact of land-use patterns that are in constant flux and how they relate to climate change. Geo-marketing companies will also find this information useful when they are conducting customer research regarding segmentation on spatial location.

However, spatial data mining systems force those who need them to face certain challenges. First of all, these databases tend to be extremely large and can be cumbersome to sort through when looking for specific information. Geographical information system datasets that already exist are usually split into featured and attributed components and this means that they are separated into hybrid data management systems. Both featured and attributed data systems require separate means of management. For example algorithmic requirements differ when it comes to relational data, which is in the attribute category and for topographical data, which falls under the feature category.

The two main systems for spatial data management are the raster and the vector. Depending on the needs of the data being used, it is important to analyze the benefits and downfalls of both systems.

Doing business in the 21st century doesn't have to be difficult - companies can enhance their marketing procedures through address validation software and various other list cleaning procedures so that they can target their market perfectly!




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Why Outsourcing Data Mining Services?

Are huge volumes of raw data waiting to be converted into information that you can use? Your organization's hunt for valuable information ends with valuable data mining, which can help to bring more accuracy and clarity in decision making process.

Nowadays world is information hungry and with Internet offering flexible communication, there is remarkable flow of data. It is significant to make the data available in a readily workable format where it can be of great help to your business. Then filtered data is of considerable use to the organization and efficient this services to increase profits, smooth work flow and ameliorating overall risks.

Data mining is a process that engages sorting through vast amounts of data and seeking out the pertinent information. Most of the instance data mining is conducted by professional, business organizations and financial analysts, although there are many growing fields that are finding the benefits of using in their business.

Data mining is helpful in every decision to make it quick and feasible. The information obtained by it is used for several applications for decision-making relating to direct marketing, e-commerce, customer relationship management, healthcare, scientific tests, telecommunications, financial services and utilities.

Data mining services include:

    Congregation data from websites into excel database
    Searching & collecting contact information from websites
    Using software to extract data from websites
    Extracting and summarizing stories from news sources
    Gathering information about competitors business

In this globalization era, handling your important data is becoming a headache for many business verticals. Then outsourcing is profitable option for your business. Since all projects are customized to suit the exact needs of the customer, huge savings in terms of time, money and infrastructure can be realized.

Advantages of Outsourcing Data Mining Services:

    Skilled and qualified technical staff who are proficient in English
    Improved technology scalability
    Advanced infrastructure resources
    Quick turnaround time
    Cost-effective prices
    Secure Network systems to ensure data safety
    Increased market coverage

Outsourcing will help you to focus on your core business operations and thus improve overall productivity. So data mining outsourcing is become wise choice for business. Outsourcing of this services helps businesses to manage their data effectively, which in turn enable them to achieve higher profits.



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The A B C D of Data Mining Services

If you are very new to the term 'data mining', let the meaning be explained to you. It is form of back office support services that are being offered by many call centers to analyze data from numerous resources and amalgamate them for some useful task. The business establishments in the present generation need to develop a strategy that helps them to cooperate with the market trends and allow them to perform well. The process of data mining is actually the retrieval process of essential and informative data that helps an organization to analyze the business perspectives and can further generate better interests in cutting cost, developing revenue and to acquire valuable data on business services/products.

It is a powerful analytical tool that permits the user to customize a wide range of data in different formats and categories as per their necessity. The data mining process is an integral part of a business plan for companies that need to undertake a diverse research on the customer building process. These analytical skills are generally performed by skilled industrial experts who assist the firms to accelerate their growth through the critical business activities. With a vast applicability in the present time, the back office support services with the data mining process is helping the businesses in understanding and predicting valuable information. Some of them include:

    Profiles of customers
    Customer buying behavior
    Customer buying trends
    Industry analysis

For a layman it is somewhat the process of processing some statistical data or methods. These processes are implemented with some specific tools that preform the following:

    Automated model scoring
    Business templates
    Computing target columns
    Database integration
    Exporting models to other applications
    Incorporating financial information

There are some benefits of Data Mining. Few of them are as follows:

    To understand the requirements of the customers which can help in efficient planning.
    Helps in minimizing risk and improve ROI.
    Generate more business and target the relevant market.
    Risk free outsourcing experience
    Provide data access to business analysts
    A better understanding of the demand supply graph
    Improve profitability by detect unusual pattern in sales, claims, transactions
    To cut down the expenses of Direct Marketing

Data mining is generally a part of the offshore back office services and outsourced to business establishments that require diverse data base on customers and their particular approach towards any service or product. For example banks, telecommunication companies, insurance companies, etc. require huge data base to promote their new policies. If you represent a similar company that needs appropriate data mining process then it is better that you outsource back office support services from a third party and fulfill your business goals with excellent results.

Katie Cardwell works as a senior sales and marketing analyst for a multinational call center company, based in United States of America. She takes care of all the business operations and analysis the back office support services that power an organization. Her extensive knowledge and expertise on Non -voice call center services such as Data Mining Services, Back office support services, etc, have helped many business players to stand with a straight spine and thus making a foothold in the data processing industry.




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