Showing posts with label data. Show all posts
Showing posts with label data. Show all posts

Beyond the Desktop Metaphor: Designing Integrated Digital Work Environments Review

Beyond the Desktop Metaphor: Designing Integrated Digital Work Environments
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After chancing to see the book at the FLOP (free library of Philadelphia) i checked it out to check it out. Basically, the authors are complaining that the metaphor of the desktop for our personal computing and organizing information is insufficient for the demands of life. Good point, but they don't argue persuasively that something is 'just around the corner' like many "tech-savvy" books always proclaim.
This work was meant to be a bit more academic and theoretical (did i spel that write?) in order to i guess present and stimulate deep thought about alternative metaphors for working with computers. I found the examples thoroughly explained and the book well written. But after about 3/4 of the book i got the point and didn't want to pore over the details of the rest of the book. i hope i didn't miss anything.


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The computer's metaphorical desktop, with its onscreen windows andhierarchy of folders, is the only digital work environment most users and designershave ever known. Yet empirical studies show that the traditional desktop design doesnot provide sufficient support for today's real-life tasks involving collaboration,multitasking, multiple roles, and diverse technologies. In Beyond the DesktopMetaphor, leading researchers and developers consider design approaches for apost-desktop future.The contributors analyze the limitations of the desktopenvironment--including the built-in conflict between access and display, thedifficulties in managing several tasks simultaneously, and the need to coordinatethe multiple technologies and information objects (laptops, PDAs, files, URLs,email) that most people use daily--and propose novel design solutions that worktoward a more integrated digital work environment. They describe systems thatfacilitate access to information, including Lifestreams, Haystack, Task Factory,GroupBar, and Scalable Fabric, and they argue that the organization of workenvironments should reflect the social context of work. They consider the notion ofactivity as a conceptual tool for designing integrated systems, and point to theKimura and Activity-Based Computing systems as examples.Beyond the Desktop Metaphoris the first systematic overview of state-of-the-art research on integrated digitalwork environments. It provides a glimpse of what the next generation of informationtechnologies for everyday use may look like--and it should inspire design solutionsfor users' real-world needs.

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Data Driven: Profiting from Your Most Important Business Asset Review

Data Driven: Profiting from Your Most Important Business Asset
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In contrast to several recent books on the importance of managing with data analytics, Data Driven starts with the IT infrastructure required to maintain consistent data, then focuses on data quality from the executive perspective of the hidden costs of poor data, and finally, explores how to make better decisions through proper data management.
A nice twist is a chapter on content providers who bring packaged data to the marketplace. This is a growing segment that is applicable to every business, since every business collects data that has value to companies in its ecosystem.
There is a chapter on Social Issues, which is great in intent but weak in content. Sad...
The book ends with "what to do over the next one hundred days" advice. If managers are serious about treating data as a business asset, then this chapter lays out the essentials of what to do.
I recommend this book for business executives to orient their thinking about data as a business asset and to realize what tangible actions must be done to make that a reality in their companies. I know that these are old old themes for the IT profession. However, these fundamental themes are oldies but goodies!

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Making Software: What Really Works, and Why We Believe It Review

Making Software: What Really Works, and Why We Believe It
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I'm going to go on record and say that this is one of the most important books about software development that has been published in the last few years. It's easy for many of us in the industry to complain that software engineering research is years behind practice and that it is hard to construct experiments or perform studies which produce information that is relevant for practitioners, but fact is, there are many things we can learn from published studies.
The editors of this book do a great job of explaining what we can and can not expect from research. They also adopt a very pragmatic mindset, taking the point of view that appropriate practice is highly contextual. Research can provide us with evidence, but not necessarily conclusions.
Beyond the philosophical underpinnings, 'Making Software' outlines research results in a variety of areas. It gives you plenty to think about when considering various approaches on your team. The chapter 'How Effective is Modularization?' is worth the price of the book alone.
I recommend this book for anyone who wants to learn how to think rigorously about practice.



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Many claims are made about how certain tools, technologies, and practices improve software development. But which claims are verifiable, and which are merely wishful thinking? In this book, leading thinkers such as Steve McConnell, Barry Boehm, and Barbara Kitchenham offer essays that uncover the truth and unmask myths commonly held among the software development community. Their insights may surprise you.

Are some programmers really ten times more productive than others?
Does writing tests first help you develop better code faster?
Can code metrics predict the number of bugs in a piece of software?
Do design patterns actually make better software?
What effect does personality have on pair programming?
What matters more: how far apart people are geographically, or how far apart they are in the org chart?

Contributors include:

Jorge Aranda Tom Ball Victor R. Basili Andrew Begel Christian Bird Barry Boehm Marcelo Cataldo Steven Clarke Jason Cohen Robert DeLine Madeline Diep Hakan Erdogmus Michael Godfrey Mark Guzdial Jo E. Hannay Ahmed E. Hassan Israel Herraiz Kim Sebastian Herzig Cory Kapser Barbara Kitchenham Andrew Ko Lucas Layman Steve McConnell Tim Menzies Gail Murphy Nachi Nagappan Thomas J. Ostrand Dewayne Perry Marian Petre Lutz Prechelt Rahul Premraj Forrest Shull Beth Simon Diomidis Spinellis Neil Thomas Walter Tichy Burak Turhan Elaine J. Weyuker Michele A. Whitecraft Laurie Williams Wendy M. Williams Andreas Zeller Thomas Zimmermann


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Beautiful Data: The Stories Behind Elegant Data Solutions Review

Beautiful Data: The Stories Behind Elegant Data Solutions
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"Beautiful Data" is a collection of essays on data; how people have transformed it, worked within its confines, and offers a glimpse of where we might go. Many of the essays are wonderful snippets into how some people perceive data while others fall flat. Overall its a mostly enjoyable read that helps open up your mind to new potentials.
First a disclaimer; I am not a data person. However I've been involved, fairly heavily, in the data field. In the parlance of the world, I'm a back end person. However I'm always trying to think about the front end; how will things be used and what information can we gleen from the system (or systems). With that in mind, this is a book that speaks to me - its all about the front end.
Some of the best essays in the book would be:
The first essay by Nathan Yau he talks very much about user created data and personal databases (knowledge bases). What's exciting here is how he takes data already out there, data you have provided, and creates something useful and yes, beautiful, out of it.
The Second essay by Follett and Holm really gets down to how if you want the data, you need to present it in a way that brings people into the process. As someone who has a slight crush on the statistics and practices in polling (and designing poll questions) this essay really was a fascinating read.
The third essay by Hughes detailed how he handled images on the Mars mission. There wasn't anything here that wasn't done in embedded systems 15 years ago; still it was a great walk down memory lane since I used to program embedded imaging systems.
Chapter 4 really hit home PNUTShell is cloud storage and data processing in real time. This really is the stuff of the future.
Chapter 5 by Jeff Hammerbacher really didn't offer too many insights but his writing style is fluid and fun plus he offered a glimpse into how Facebook grew.
We then have the slow section of the book - Chapter 8 on distributed social data had promise but it read more like a company white page than an interesting article. Same with Chapter 12 [...].
Thankfully chapter 10 on Radiohead's "House of Cards" video was there - and here we are presented with true beauty in data - beautiful enough to create a music video out of!
I'm still on the fence with Chapter 13 - What Data Doesn't Do. It was an interesting chapter but it felt both too long and too short at the same time. I almost felt that in the author, Coco Krumme, were to write a book on this topic, I'd want to read it. However her essay was not the right vehicle.
Finally, the last chapter - "Connecting Data" was a truly inspiring piece; one that offers up paths for the future. I am sure a few start ups will form over the questions posed in by Segaran (or maybe the questions to the questions).
Overall there were enough strengths to overcome the weak chapters. My main complaints are trivial; poor binding of the book, too many PhD candidate papers and not enough from out in the trenches. I'd love to see something from Stonebreaker here; its hard to talk about beautiful data and not have him in it. Or forget [...]and talk about many eyes. Or map reduce. Still, "Beautiful Data" succeeds. It opened up my mind to different possibilities for data representation and usage.


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In this insightful book, you'll learn from the best data practitioners in the field just how wide-ranging -- and beautiful -- working with data can be. Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video. With Beautiful Data, you will:

Explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web
Learn how to visualize trends in urban crime, using maps and data mashups
Discover the challenges of designing a data processing system that works within the constraints of space travel
Learn how crowdsourcing and transparency have combined to advance the state of drug research
Understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data
Learn about the massive infrastructure required to create, capture, and process DNA data

That's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include:
Nathan Yau Jonathan Follett and Matt Holm J.M. Hughes Raghu Ramakrishnan, Brian Cooper, and Utkarsh Srivastava Jeff Hammerbacher Jason Dykes and Jo Wood Jeff Jonas and Lisa Sokol Jud Valeski Alon Halevy and Jayant Madhavan Aaron Koblin with Valdean Klump Michal Migurski Jeff Heer Coco Krumme Peter Norvig Matt Wood and Ben Blackburne Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen Lukas Biewald and Brendan O'Connor Hadley Wickham, Deborah Swayne, and David Poole Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza Toby Segaran

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Super Crunchers: Why Thinking-By-Numbers is the New Way To Be Smart Review

Super Crunchers: Why Thinking-By-Numbers is the New Way To Be Smart
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Is it a new brand of cereal? Or maybe it's a granola bar, or a chunky peanut butter spread? Then again, could it be the latest infomercial exercise device designed to give you the six pack abs you've always dreamed of but know in your heart of hearts you'll never achieve? Actually, it's a book - the title a product of the very methods the book describes. Here's what SUPER CRUNCHERS says.
(1) Mathematical regression models generated from large datasets often generate better predictions than human experts, and they provide supporting information on the predictive weight and reliability of each explanatory variable.
(2) Well-crafted experiments using randomized trials and control groups provide good market research and behavioral analysis results.
(3) Technological advances - the Internet, massive data storage devices, rapid computation, broadband telecommunication - are making it possible to share more sources of information and create ever-larger databases for analysis.
(4) Today's companies engage in multiple forms of market research by creating and using large databases and large-scale randomized trials.
(5) Many phenomena conform to normal distributions in which 95% of the population will be found within two standard deviations of the mean, the5% balance generally divided evenly in the two tails.
That's it. I just saved you $25.00 U.S. and a half-dozen or more hours learning how a guy from Yale named Ian Ayres collected a bit of information about applied mathematical techniques that have been in practical use for decades, packaged them up, palmed them off as something new, and cooked up the ridiculous name Super Crunching to describe an ostensibly new technological development. Yet "Super Crunching" is nothing more than the author's marketing hype for a couple of standard mathematical methodologies, a creation of nothing from something. There's no new breakthrough here, no new paradigm.
Yes, the anecdotal information about the future prices of wine vintages, Capital One's teaser offerings, and evidence-based medical diagnosis are interesting (hence the two stars rating). The rest, however, is neither prescriptive nor sufficiently critically analytical. Should we go out shopping for a Super Cruncher tomorrow? Should we delight in the increased accuracy of data-driven modeling and prediction, or should we fear the implied manipulation of our desires and the incessant, single-minded drive toward maximum profit at the expense of creativity? Do we really want movies and books to be developed from mathematical models like Epagogix? Do we really want our every keystroke on the Internet to be fodder for market research that manipulates us in response? John Kenneth Galbraith, among others, warned of exogenous, manufactured demand decades ago.
SUPER CRUNCHERS is part business tome, part econometric paean, and part sociology book, but not fully any of the three. No matter how many time the author uses words like "cool" and "humongous" and "amazing," it's still regrettably a "No Sale" even for someone like me who enjoys reading about applied mathematics.


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Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites Review

Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites
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Mining the Social Web does a great job of introducing a wide variety of techniques and wealth of resources for exploring freely available social data and personal information. If you are willing to spend the time tinkering with the examples, the book is pure fun. It offers a nice compliment to Segaran's Programming Collective Intelligence: Building Smart Web 2.0 Applications. The two books overlap but where they do offer different perspectives and explanations of common techniques (e.g., TF-IDF, cosine similarity, Jaccard index). If you are well-versed in data mining the web you may find much of the discussion familiar. If you have only been casually engaged to date, your toolbox will fill quickly.
In order to work with the book's examples related to LinkedIn and Facebook you really need to have a robust collection of connections. In terms of the source code itself, most of it worked as is. I wasn't able to install the Buzz library which limited my interaction with material in chapter 7 and opted to not get involved with the LinkedIn or Facebook but found the discussions around them easy to follow. By far my favorite chapter in the book was chapter 8, "Blogs et al.: Natural Language Processing (and Beyond)..." It was quite fascinating and caused my reading list to grow considerably.

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Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who's making connections with social media, what they're talking about, or where they're located? This concise and practical book shows you how to answer these questions and more. You'll learn how to combine social web data, analysis techniques, and visualization to help you find what you've been looking for in the social haystack, as well as useful information you didn't know existed.

Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools.

Get a straightforward synopsis of the social web landscape
Use adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, and LinkedIn
Learn how to employ easy-to-use Python tools to slice and dice the data you collect
Explore social connections in microformats with the XHTML Friends Network
Apply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection
Build interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits

"Let Matthew Russell serve as your guide to working with social data sets old (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social Web is a natural successor to Programming Collective Intelligence: a practical, hands-on approach to hacking on data from the social Web with Python." --Jeff Hammerbacher, Chief Scientist, Cloudera

"A rich, compact, useful, practical introduction to a galaxy of tools, techniques, and theories for exploring structured and unstructured data." --Alex Martelli, Senior Staff Engineer, Google


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Programming Collective Intelligence: Building Smart Web 2.0 Applications Review

Programming Collective Intelligence: Building Smart Web 2.0 Applications
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This book is probably best for those of you who have read the theory, but are not quite sure how to turn that theory into something useful. Or for those who simply hunger for a survey of how machine learning can be applied to the web, and need a non-mathematical introduction.
My area of strength happens to be neural networks (my MS thesis topic was in the subject), so I will focus on that. In a few pages of the book, the author describes how the most popular of all neural networks, backpropagation, can be used to map a set of search terms to a URL. One might do this, for example, to try and find the page best matching the search terms. Instead of doing what nearly all other authors will do, prove the math behind the backprop training algorithm, he instead mentions what it does, and goes on to present python code that implements the stated goal.
The upside of the approach is clear -- if you know the theory of neural networks, and are not sure how to apply it (or want to see an example of how it can be applied), then this book is great for that. His example of adaptively training a backprop net using only a subset of the nodes in the network was interesting, and I learned from it. Given all the reading I have done over the years on the subject, that was a bit of a surprise for me.
However, don't take this book as being the "end all, be all" for understanding neural networks and their applications. If you need that, you will want to augment this book with writings that cover some of the other network architectures (SOM, hopfield, etc) that are out there. The same goes for the other topics that it covers.
In the end, this book is a great introduction to what is available for those new to machine learning, and shows better than any other book how it applies to Web 2.0. Major strengths of this book are its broad coverage, and the practicality of its contents. It is a great book for those who are struggling with the theory, and/or those who need to see an example of how the theory can be applied in a concise, practical way.
To the author: I expect this book will get a second edition, as the premise behind the book is such a good one. If that happens, perhaps beef up the equations a bit in the appendix, and cite some references or a bibliography for those readers interested in some more in depth reading about the theory behind all these wonderful techniques. (The lack of a bibliography is why I gave it 4 stars out of 5, I really think that those who are new to the subject would benefit greatly from knowing what sits on your bookshelf.)

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Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general--all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:
Collaborative filtering techniques that enable online retailers to recommend products or media
Methods of clustering to detect groups of similar items in a large dataset
Search engine features--crawlers, indexers, query engines, and the PageRank algorithm
Optimization algorithms that search millions of possible solutions to a problem and choose the best one
Bayesian filtering, used in spam filters for classifying documents based on word types and other features
Using decision trees not only to make predictions, but to model the way decisions are made
Predicting numerical values rather than classifications to build price models
Support vector machines to match people in online dating sites
Non-negative matrix factorization to find the independent features in adataset
Evolving intelligence for problem solving--how a computer develops its skill by improving its own code the more it plays a game
Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

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