What Are The Principles Of Data


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Principles of Data Mining and Knowledge Discovery


Principles of Data Mining and Knowledge Discovery

Author:

language: en

Publisher:

Release Date: 1999


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Data as a Service


Data as a Service

Author: Pushpak Sarkar

language: en

Publisher: John Wiley & Sons

Release Date: 2015-07-31


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Data as a Service shows how organizations can leverage “data as a service” by providing real-life case studies on the various and innovative architectures and related patterns Comprehensive approach to introducing data as a service in any organization A reusable and flexible SOA based architecture framework Roadmap to introduce ‘big data as a service’ for potential clients Presents a thorough description of each component in the DaaS reference architecture so readers can implement solutions

Principles of Artificial Intelligence


Principles of Artificial Intelligence

Author: Harry Katzan Jr.

language: en

Publisher: iUniverse

Release Date: 2025-05-05


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This book covers the Principles of Artifi cial Intelligence. It is both a text book and a reference book. It is one of many books on the subject of artifi cial intelligernce. There are more than 400 of them. It is the only one that covers principles that is intended to refl ect on how to go about doing AI for productive purposes. It also covers about what AI is already, but it is more than that. It answers the question “Can a machine think?” and most people are quite tired of that question. In fact, people are now more interested in how to do what we want to do. In fact, AI is a inportant subject in our lives and here are two outstanding books that atune to that assertion: The Singularity is Nearer (2024) by Ray Kurzweil; Artifi cial Intelligence: A Modern Approach (1995) by Stuart Russell and Peter Norvig; The writers are exceedingly intelligent, and the books are useful but not that easy to read. University research is equally noteworthy. But what about the strategy of adopting AI for the modern operational environment? How do you know what to do and how to do it. Do you have to be a scientist or a mathematician to do the job? Absolutely not. Do you need to be a manager, a major CEO, or even the President of a coiuntry. Probably yes. But you need to have the information to do the job. This book gives you what you should do to implement AI in the organization and precisely what you need to know in order to do it. When doing the job of implementing, should you be knowledgeable about precisly what has to be done? Of course. Do you personally have to do it? Not at all. Do you need information on related subjects, of course again. Do you have to read this book serially? Of course not; it is too detailed. But when you fi nally get it done properly, you do deserve to be a DAI, that is a Doctor of Artifi cial Intelligence. That is proposed to be the case in the future. Will this be happy reading? On some topics, yes. Other sections, not so much. There are a lot of pages because the environment of AI is large and complicated. Many of the subjects covered in this book will be extremely useful in other areas of business and the organizaton. Artifi cial Intelligence is an extremely volatile subject. It is being adjusted daily, and it is almost impossible to fi gure out what is actually going on. The book will be revised and probably copied in content with an air of improvement. That is the way the world operates. Have a useful and interesting time reading the book. It will be worth the effort. One more thing. The book is for fi nding out about AI and associated subjects. Who knowswhat the professional and everyday people want to know. The book is for everyone. Equally important is the fact that the book is specifi cally designed for an online college course on AI and supports that assertion by including a substantional choice of subjects for the online professor. For example, the last section on managing uncertainly is very strongly AI based on the Theory of Evidence through the information on Dempster Shafer Theory. The author has been involved with AI since a university 3-week seminar in 1963 for a large corporation and taught one of the fi rst graduate-level university courses on AI in 1978. He has been the CEO of Artifi cial Intelligence Consulting (AICON), a university professor, and an international AI consultant, after working for Boeing, Oak Ridge National Lab, and IBM. He has written a few books and a few more peer reviewed papers.