The Fourth Paradigm: Data-Intensive Scientific Discovery Kindle Edition

4.1 out of 5 stars 13 customer reviews
:
:
Why is ISBN important?
ISBN
This bar-code number lets you verify that you're getting exactly the right version or edition of a book. The 13-digit and 10-digit formats both work.
Scan an ISBN with your phone
Use the Amazon App to scan ISBNs and compare prices.
Kindle App Ad
Buy
$0.99
eBook features:
  • Highlight, take notes, and search in the book
  • Length: 284 pages
Sold by: Amazon Digital Services LLC

Deliver to your Kindle or other device

Price
New from Used from
Kindle, October 16, 2009
"Please retry"
$0.99

The Amazon Book Review
The Amazon Book Review
Author interviews, book reviews, editors picks, and more. Read it now
click to open popover

Enter your mobile number below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
Getting the download link through email is temporarily not available. Please check back later.

  • Apple
  • Android
  • Windows Phone
  • Android

To get the free app, enter your mobile phone number.


Editorial Reviews

About the Author

Tony Hey is Vice President of Microsoft Research and has published over 150 research papers on particle physics, computer science and multi-disciplinary eScience. He is the co-author, with Ian Aitchison, of the graduate textbook Gauge Theories in Particle Physics (4th edition) and co-editor of Richard Feynman's Lectures on Computation. He is also co-author of popular science books on quantum mechanics and relativity, The New Quantum Universe and Einstein's Mirror. Most recently, he co-edited The Fourth Paradigm, a book about eScience and the new era of data-intensive science.

Product Details

  • File Size: 2590 KB
  • Print Length: 284 pages
  • Simultaneous Device Usage: Unlimited
  • Publisher: Microsoft Research; 1 edition (October 16, 2009)
  • Publication Date: October 16, 2009
  • Sold by: Amazon Digital Services LLC
  • Language: English
  • ASIN: B00318D9Y2
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Lending: Enabled
  • Enhanced Typesetting: Not Enabled
  • Amazon Best Sellers Rank: #141,501 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
  •  Would you like to give feedback on images or tell us about a lower price?

Customer Reviews

Top Customer Reviews

By J. Brew on January 29, 2010
Format: Kindle Edition Verified Purchase
"We have to do better producing tools to support the whole research cycle - from data capture and data curation to data analysis and data visualization." - Jim Gray

The Fourth Paradigm is a collection of papers talks on research areas that aim to improve the research cycle. The talks are a memorial to Microsoft Tech Fellow Jim Gray. Gray had the insight that science has gone through four paradigms so far. The first paradigm, which has lasted over the last few thousand years, was empirical science which describes natural phenomena. Over the last few hindered years, the second paradigm of theoretical science using models and generalizations has occurred. Within the last 50 to 70 years, the third paradigm of computational science has developed to simulate complex phenomena. Finally, the fourth paradigm (also known as eScience) has developed to unify theory, experiment, and simulation. Jim Gray says:"... it is worth distinguishing data-intensive science from computational science as a new, fourth paradigm for scientific exploration."

The book itself is divided into four major sections: Earth and Environment, Health and Wellbeing, Scientific Infrastructure, and Scholarly Communications with 6 to 8 papers per section. The emphasis here is on science; however, I'd assert that all these areas directly impact engineering as well. For example, the flight test of a new product involves an enormous amount of data, which produces much analysis, knowledge, and understanding. The principle idea of eScience (and eEngineering) is that the data and analysis interoperate with each other, such that information is at your fingertips for everyone, everywhere. The payoff is a large increase in information velocity and productivity.
Read more ›
Comment 30 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse
Format: Kindle Edition
Computer Science is still in early days, and often its tools are the object of study. This book focuses on how our ability to process data is helpful to Science. Tycho Brahe left us lots of measurements still readable today. Bits nowadays are more volatile than that.

It is a very interesting book, made of papers, and a starting point for a vision. Dedicated to a great person and researcher.

I hope that the "Microsoft" tag wouldn't lead someone to ignore it, not always there is a hidden plot, and in this case it's just a book that people working with someone felt to write to honor his work and his memory.
Comment 9 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse
Format: Kindle Edition Verified Purchase
I really enjoy the subject matter: science and computers.

However, all of the papers were top-down overviews. I wanted to dig into some case studies. For example, Microsoft has a working project: World Wide Telescope. How many data sources do they use? How do they blend data from conflicting sources? How do they curate the data? How much telescope gear (and how much computer hardware and software) would I need to contribute? None of the essays went into details on these projects.

Several papers did make some useful, interesting points.

Much of scientific research today is cottage industry: one group puts together some instruments, gathers data, analyzes it, publishes paper. A revolution akin to the industrial revolution will happen: specialized groups will operate instruments and publish data; other groups will analyze the data.

The data repositories of the future must accommodate large numbers of disparate groups gathering data -- and the scientific community must reward them. Data organization, provision of metadata, provenance are all big unsolved questions. (I'd have liked more detailed information here, too).

Some scientific instruments collect data so fast that the bottleneck is no longer data acquisition but data interpretation. Similarly, data repositories are so large that making copies of the dataset is expensive -- it will actually be cheaper for data repositories to offer services where researchers run custom programs against the data.

This high-level overview is grand, but it's hard to test. Surely these pronouncements are based on experience in actual scientific projects. I wanted to read more at this lower level.
Comment 10 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse
Format: Paperback
Great book, I think Bill Gate's quote from the back of the book sums it up nicely. "The impact of Jim Gray’s thinking is continuing to get people to think in a new way about how data and software are redefining what it means to do science." Also the book is available for free from Microsoft: [...]
Comment One person found this helpful. Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse
Format: Kindle Edition Verified Purchase
I am a fanatic of data, technology and enterprise. This cover the main tendencies and uses of scientific computing, big data, etc. and their challenges, touching interesting points to be considered and some drivers of the change. There are much more opportunities to come.
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse
Format: Paperback Verified Purchase
This was a much referenced book. It proved to be insightful in many areas but it also tended to be very relational database focused. It missed a lot of what is going on in object databases, semantics, very very large data structures. Scale is one of the issues and this broached the subject but only discussed it in terms of relational models. One might argue that relational models are good for reductionist efforts but synthesis efforts fail because they become compute bound.
Comment One person found this helpful. Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse

Set up an Amazon Giveaway

The Fourth Paradigm: Data-Intensive Scientific Discovery
Amazon Giveaway allows you to run promotional giveaways in order to create buzz, reward your audience, and attract new followers and customers. Learn more about Amazon Giveaway
This item: The Fourth Paradigm: Data-Intensive Scientific Discovery

What Other Items Do Customers Buy After Viewing This Item?