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Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information (Englisch) Taschenbuch – 30. Mai 2013


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Produktinformation

Produktbeschreibungen

Pressestimmen

""The author has produced a sober, serious treatment of this emerging phenomenon, avoiding hype and gee-whiz cases in favor of concepts and mature advice. For example, the author offers ten distinctions between big data and small data, including such factors as goals, location, data structure, preparation, and longevity. This characterization provides much greater insight into the phenomenon than the standard 3V treatment (volume, velocity, and variety)."--""ComputingReviews.com, " October 3, 2013

""The book is written in a colloquial style and is full of anecdotes, quotations from famous people, and personal opinions."--""ComputingReviews.com, " February 3, 2014 ""The author has produced a sober, serious treatment of this emerging phenomenon, avoiding hype and gee-whiz cases in favor of concepts and mature advice. For example, the author offers ten distinctions between big data and small data, including such factors as goals, location, data structure, preparation, and longevity. This characterization provides much greater insight into the phenomenon than the standard 3V treatment (volume, velocity, and variety)."--""ComputingReviews.com, " October 3, 2013

""By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book."--""ODBMS.org, " March 21, 2014 ""The book is written in a colloquial style and is full of anecdotes, quotations from famous people, and personal opinions."--""ComputingReviews.com, " February 3, 2014 ""The author has produced a sober, serious treatment of this emerging phenomenon, avoiding hype and gee-whiz cases in favor of concepts and mature advice. For example, the author offers ten distinctions between big data and small data, including such factors as goals, location, data structure, preparation, and longevity. This characterization provides much greater insight into the phenomenon than the standard 3V treatment (volume, velocity, and variety)."--""ComputingReviews.com, " October 3, 2013

"By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book." --ODBMS.org, March 2014

"The book is written in a colloquial style and is full of anecdotes, quotations from famous people, and personal opinions." --ComputingReviews.com, February 2014

"The author has produced a sober, serious treatment of this emerging phenomenon, avoiding hype and gee-whiz cases in favor of concepts and mature advice. For example, the author offers ten distinctions between big data and small data, including such factors as goals, location, data structure, preparation, and longevity. This characterization provides much greater insight into the phenomenon than the standard 3V treatment (volume, velocity, and variety)." --ComputingReviews.com, October 2013

Über den Autor und weitere Mitwirkende

Jules Berman holds two bachelor of science degrees from MIT (Mathematics, and Earth and Planetary Sciences), a PhD from Temple University, and an MD, from the University of Miami. He was a graduate researcher in the Fels Cancer Research Institute, at Temple University, and at the American Health Foundation in Valhalla, New York. His post-doctoral studies were completed at the U.S. National Institutes of Health, and his residency was completed at the George Washington University Medical Center in Washington, D.C. Dr. Berman served as Chief of Anatomic Pathology, Surgical Pathology and Cytopathology at the Veterans Administration Medical Center in Baltimore, Maryland, where he held joint appointments at the University of Maryland Medical Center and at the Johns Hopkins Medical Institutions. In 1998, he became the Program Director for Pathology Informatics in the Cancer Diagnosis Program at the U.S. National Cancer Institute, where he worked and consulted on Big Data projects. In 2006, Dr. Berman was President of the Association for Pathology Informatics. In 2011 he received the Lifetime Achievement Award from the Association for Pathology Informatics. He is a co-author on hundreds of scientific publications. Today Dr. Berman is a free-lance author, writing extensively in his three areas of expertise: informatics, computer programming, and cancer biology. A complete list of his publications is available at http://www.julesberman.info/pubs.htm As a Program Director at the National Cancer Institute, Dr. Berman directed a multi-institutional Big Data project and actively organized and participated in high-level conferences and meetings where Big Data efforts were planned. He made a number of contributions to the field, particularly in the areas of identification, de-identification, data exchange protocols, standards development, regulatory/legal issues, and metadata annotation. Aside from his personal experiences

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2 von 2 Kunden fanden die folgende Rezension hilfreich
HASH(0x98114e58) von 5 Sternen Fundamental, full of wisdom and advice yet thorough 4. November 2013
Von A. Zubarev - Veröffentlicht auf Amazon.com
Format: Kindle Edition
A fantastic book! Must be part, if not yet, of the fundamentals of the Big Data as a field of science.

Highly recommend to those who are into the Big Data practice.

Yet, I confess this book is one of my best reads this year and for a number of reasons:

The book is full of wisdom, intimate insight, historical facts and real life examples to how Big Data projects get conceived, operate and sadly, yes, sometimes die.

But not only that, the book is most importantly is filled with valuable advice, accurate and even overwhelming amount of reference (from the positive side), and the author does not event stop there:

there are numerous technical excerpts, links and examples allowing to quickly accomplish many daunting tasks or make you aware of what one needs to perform as a data practitioner (excuse my use of the word practitioner, I just did not find a better substitute to it to trying to reference all who face Big Data).

Be aware that Jules Berman's background is in medicine, naturally, this book discusses this subject a lot as it is very dear to the author's heart I believe, this does not make this book any less significant however, quite the opposite, I trust if there is an area in science or practice where the biggest benefits can be ripped from Big Data projects it is indeed the medical science, let's make Cancer history!

On a personal note, for me as a database, BI professional it has helped to understand better the motives behind Big Data initiatives, their underwater rivers and high altitude winds that divert or propel them forward.

Additionally, I was impressed by the depth and number of mining algorithms covered in it. I must tell this made me very curious and tempting to find out more about these indispensable attributes of Big Data so sure I will be trying stretching my wallet to acquire several books that go more in depth on several most popular of them.

My favorite parts of the book, well, all of them actually, but especially chapter 9: Analysis, it is just very close to my heart. But the real reason is it let me see what I do with data from a different angle. And then the next - "Special Considerations", they are just two logical parts.

The writing language is of this book is very acceptable for all levels, I had no technical problem reading it in ebook format on my 8" tablet or a large screen monitor.

If I would be asked to say at least something negative I have to state I had a feeling initially that the book's first part reads like an academic material relaxing the reader as the book progresses forward.

I admit I am impressed with Jules' abilities to use several programming languages and OSS tools, bravo! And I agree, it is not too, too hard to grasp at least the principals of a modern programming language, which seems becomes a defacto knowledge standard item for any modern human being.

So grab a copy of this book, read it end to end and make yourself shielded from making mistakes at any stage of your Big Data initiative, by the way this book also helps build better future Big Data projects.
4 von 5 Kunden fanden die folgende Rezension hilfreich
HASH(0x9d3ab588) von 5 Sternen Berman on Big Data 29. September 2013
Von cheadleg - Veröffentlicht auf Amazon.com
Format: Taschenbuch
Principles of Big Data: Preparing, Sharing, and Analyzing Complex InformationReview
Jules J Berman, Ph.D., M.D.
Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information
Waltham, MA, Morgan Kaufmann, 2013

Some professional books are classics in their fields: Prosser and Keeton on Torts, Rubin's Pathology, and many others. Now there will be Berman on Big Data.
Dr. Jules Berman has conceived and crafted (the words "has written" are too tame) a comprehensive, definitive, explanatory treatment of the new analytical field which has come to be called "Big Data." The subject is germane to the modern quests for meaning involving many and different hosts of data--what do we have here? how do we put it together? does it have immutable characteristics? etc.
Like any professional classic, Big Data covers all the bases (it is longer than it looks and it is dense with information and ideas). The list of chapter titles shows that the book is all-encompassing. It is made clear which collection of data can be called "Big Data" and which cannot. (for example, "massive data" is not "big data"). Attribute after attribute is defined and examined. Nothing is left to chance. The book can be read straight through, but it can also be referred to by item or subject.
Two characteristics of Big Data need to be noted:
First, the Glossary is a classic of literary art in itself--145 items taking 17 pages of fine print to explain. The glossary descriptions take scientific and technical expressions such as "k-nearest neighbor algorithm" and "one-way hash" and bring them to light for the reader of any stripe, while giving useful specialized meanings to such otherwise commonplace expressions as "verification" and "validation."
Second, all through the book, Dr. Berman's many explanatory anecdotes have both the simplicity and the profundity of Aesop's Fables. They are worth savoring and enjoying just for themselves. And they do get the points over in classic fashion.
Dr, Berman's book is a reader's delight. It is remarkably free of acronyms, insider-jokes, and super-erudite constructions. It can be enjoyed profitably by readers along the whole scale of scientific expertise.
In science and technology, the concept of "big data" has a long way to go. To get there, one could hardly do better than to use Dr. Berman's Big Data as a companion on the way.

Geoffrey Cheadle
Brig Gen USAF (Ret.)
BS, MSEE, EE, CISSP
Chief Executive Manager
New Visions Data, LLC
2 von 2 Kunden fanden die folgende Rezension hilfreich
HASH(0x98097f48) von 5 Sternen Extremely thorough 12. Januar 2014
Von Andrew Rodwin - Veröffentlicht auf Amazon.com
Format: Kindle Edition Verifizierter Kauf
One of the best technical books I've read (and I read quite a few). Articulate, thorough, fascinating. Many technical authors struggle with clarity. This book is as clear as it gets.
HASH(0x9806c1c8) von 5 Sternen Berman's book on Big Data and thoroughly enjoyed it. The book provides a great framework to ... 16. August 2014
Von Hayes Williams - Veröffentlicht auf Amazon.com
Format: Taschenbuch
I just finished Dr. Berman's book on Big Data and thoroughly enjoyed it. The book provides a great framework to ask the right questions about your Big Data project. It takes you through a breadth of topics less about the technology and methods themselves and more about fundamental concepts across the entire space including possible pitfalls. The "gotchas" have the ring of experience behind them and the examples demonstrating each idea are detailed and extensive. If you want to find the book that will best start you thinking the right way about embarking on a project in this space, then this is it. Thanks very much, Dr. Berman.
1 von 8 Kunden fanden die folgende Rezension hilfreich
HASH(0x97e9c9b4) von 5 Sternen Not a good referance for Big Data! 19. Februar 2014
Von Anagha - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
I did not like this book by myself. Because I think it is not really a book to familiarized beginners with principles of Big Data.
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