May I know what is data mining? What is the future development of data mining?
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Data mining (or knowledge discovery) is an area of AI that looks at large amounts of data and tries to find patterns in it.  These patterns are not always intuitive and can help you find new relationships that are easily missed.\n\nhope this helps :-)
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A data warehouse is a collection of data gathered and organized so that it can easily by analyzed, extracted, synthesized, and otherwise be used for the purposes of further understanding the data. It may be contrasted with data that is gathered to meet immediate business objectives such as order and payment transactions, although this data would also usually become part of a data warehouse.\n\nor\n\nA technique for properly assembling and managing data from various sources for the purpose of answering business questions and making decisions that were not previously possible.
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Try to find out something related with Mobile Technology OR GIS (Geographical Information System).  Both are upcoming Field and into major demand.
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This is a good website for mining information.\n\nhttp://www.infomine.com/commodities/
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Technically, all things are written in a long enough random sequence of numbers.  For example, the number Pi contains an infinite sequence of unpatterned numbers, so if you looked deeply enough, you could find any piece of information you wanted was already encoded in it.  But in practical terms, you can only find what you already know, so it has no predictive value.  So the answer is, no, a random event generator is not a real way to see into the future.
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Bioinformatics--\n\nBioinformatics and computational biology involve the use of techniques including applied mathematics, informatics, statistics, computer science, artificial intelligence, chemistry and biochemistry to solve biological problems usually on the molecular level. Research in computational biology often overlaps with systems biology. Major research efforts in the field include sequence alignment, gene finding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and protein-protein interactions, and the modeling of evolution.\n\nThe terms bioinformatics and computational biology are often used interchangeably. However bioinformatics more properly refers to the creation and advancement of algorithms, computational and statistical techniques, and theory to solve formal and practical problems posed by or inspired from the management and analysis of biological data. Computational biology, on the other hand, refers to hypothesis-driven investigation of a specific biological problem using computers, carried out with experimental and simulated data, with the primary goal of discovery and the advancement of biological knowledge. A similar distinction is made by National Institutes of Health in their working definitions of Bioinformatics and Computational Biology, where it is further emphasized that there is a tight coupling of developments and knowledge between the more hypothesis-driven research in computational biology and technique-driven research in bioinformatics. Computational biology also includes lesser known but equally important subdisciplines such as computational biochemistry and computational biophysics.\n\nA common thread in projects in bioinformatics and computational biology is the use of mathematical tools to extract useful information from data produced by high-throughput biological techniques such as genome sequencing. A representative problem in bioinformatics is the assembly of high-quality genome sequences from fragmentary "shotgun" DNA sequencing. Other common problems include the study of gene regulation using data from microarrays or mass spectrometry.
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This would be artificial intelligence. Yahoo Answers comes very close though...
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First of all you should refer to some text book on "Research Methodology". Here you will get a clear idea of what you are getting into.  \nSome areas of research can be Genetic Algorithms in Statistics, multivariable calculus, graph theoritic applications in statistics.
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Yes, but depending on the complexity of your problem. If your data is linearly separable, then a simple regression analysis will do. Use neural networks (such as backpropagation) only when the problem is hard.
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Modern computing technologies help to automate the accumulation of huge amounts of data. For eg. with ERP and CRM systems one can collect vast amounts of information about customers.\n\nHowever, due to the sheer amount of data, the humans cannot get any "useful" information out of it. For that purpose, we need data mining -- it supports decision making process of the business organization.\n\nConsider an example: A company wants has plans launch an expensive beauty product. It has past data about shopping patterns of its customers (let's say, five thousand customers). It wants to know how many customers buy expensive things regularly and out of these how many buy beauty products regularly. If humans are deployed to go through five thousand records, they will take too much of time and in all probability, produce inaccurate results due to human errors. A data mining software will automate the job and provide accurate results.
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bioinformatics \nBioinformatics or computational biology is the use of techniques from applied mathematics, informatics, statistics, and computer science to solve biological problems. Research in computational biology often overlaps with systems biology. Major research efforts in the field include sequence alignment, gene finding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and protein-protein interactions, and the modeling of evolution. The terms bioinformatics and computational biology are often used interchangeably, although the latter typically focuses on algorithm development and specific computational methods. (In the biology-mathematics-computer science triad, bioinformatics will intimately involve all three components while computational biology will focus on biology and mathematics.) A common thread in projects in bioinformatics and computational biology is the use of mathematical tools to extract useful information from noisy data produced by high-throughput biological techniques. (The field of data mining overlaps with computational biology in this regard.) Representative problems in computational biology include the assembly of high-quality DNA sequences from fragmentary "shotgun" DNA sequencing, and the prediction of gene regulation with data from mRNA microarrays or mass spectrometry.
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Nano technology is the latest buzz which have tremendous scope in coming future because we are going towards slimmer gadgets with greater features and nano technology is the only solution which can make this happen.\n\nGood luck
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business (profit trends, loss trends...)\n\nmathematics where you try to find instantaneous rates of change, position from a function (calculus)\n\nAnd....I dunno, making roads that bank enough so you don't fly off of them?
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data modeling is the process of taking real world ideas and concepts and translating them into data that can be stored in a computer system.  The data is usually stored in a relational databasae.\n\nEnterprise data modeling does this for an entire business enterprise.  Businesses have to store data on a wide variety of topics from financial data to Human Resources to client information.  The "art" of taking all of these real-world concpts and developing a data storage strategy such that all systems and applications can share data is what Enterprise Data Modeling is all about.
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(m)\n\nBioinformatics is the application of computer technology to the management of biological information. Computers are used to gather, store, analyze and integrate biological and genetic information which can then be applied to gene-based drug discovery and development. The need for Bioinformatics capabilities has been precipitated by the explosion of publicly available genomic information resulting from the Human Genome Project. The goal of this project - determination of the sequence of the entire human genome (approximately three billion base pairs) - will be reached by the year 2002. The science of Bioinformatics, which is the melding of molecular biology with computer science, is essential to the use of genomic information in understanding human diseases and in the identification of new molecular targets for drug discovery. In recognition of this, many universities, government institutions and pharmaceutical firms have formed bioinformatics groups, consisting of computational biologists and bioinformatics computer scientists. Such groups will be key to unraveling the mass of information generated by large scale sequencing efforts underway in laboratories around the world.
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I think you just need to go through this Wikipedia Page..\nIts really well maintained & with thorough explaination of various concepts..\nCheck: http://en.wikipedia.org/wiki/Portal:Artificial_intelligence
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Data mining software allows users to analyze large databases to solve business decision problems. Data mining is, in some ways, an extension of statistics, with a few artificial intelligence and machine learning twists thrown in. Like statistics, data mining is not a business solution, it is just a technology.  For example, consider a catalog retailer who needs to decide who should receive information about a new product. The information operated on by the data mining process is contained in a historical database of previous interactions with customers and the features associated with the customers, such as age, zip code, their responses. The data mining software would use this historical information to build a model of customer behavior that could be used to predict which customers would be likely to respond to the new product. By using this information a marketing manager can select only the customers who are most likely to respond.  The operational business software can then feed the results of the decision to the appropriate touch point systems (call centers, direct mail, web servers, email systems, etc.) so that the right customers receive the right offers.\n\nSo no matter what ever it is, all that matters is you use the way.