Social Life of Information
Author: John Seely Brown
In this paperback edition of The Social Life of Information, the authors dispel many of the futurists' sweeping predictions that information technology will obliterate the need for everything from travel to supermarkets to business organizations to social life itself. But beaten down by info-glut, exasperated by computer crashes, and burned by dot-com stocks, individual users find it hard to get a fix on the true potential of the digital revolution.
A new preface updates and expands on the ideas of the original text, in which John Seely Brown and Paul Duguid argue that the gap between digerati hype and end-user gloom is largely due to the "tunnel vision" that information-driven technologies breed. We've become so focused on where we think we ought to be that we often fail to see where we're really going. We need to look beyond our obsession with information and individuals to include the critical social networks of which these are always a part.
The Social Life of Information shows how a better understanding of the contribution that communities, organizations, and institutions make to learning, working, and innovating can lead to the richest possible use of technology in our work and everyday lives.
Author Biography: John Seely Brown is the Chief Innovation Officer of 12 Entrepreneuring and the Chief Scientist of Xerox. He was the director of the Xerox Palo Alto Research Center (PARC) for ten years. Paul Duguid is affiliated with Xerox PARC and the University of California, Berkeley.
Publishers Weekly
From the chief scientist of Xerox Corporation and a research specialist in cultural studies at UC-Berkeley comes a treatise that casts a critical eye at all the hype surrounding the boom of the information age. The authors' central complaint is that narrowly focusing on new ways to provide information will not create the cyber-revolution so many technology designers have visualized. The problem (or joy) is that information acquires meaning only through social context. Brown and Duguid add a humanist spin to this idea by arguing, for example, that "trust" is a deep social relation among people and cannot be reduced to logic, and that a satisfying "conversation" cannot be held in an Internet chat room because too much social context is stripped away and cannot be replaced by just adding more information, such as pictures and biographies of the participants. From this standpoint, Brown and Duguid contemplate the future of digital agents, the home office, the paperless society, the virtual firm and the online university. Though they offer many insightful opinions, they have not produced an easy read. As they point out, theirs is "more a book of questions than answers" and they often reject "linear thinking." Like most futurists, they are fond of long neologisms, but they are given to particularly unpronounceable ones like "infoprefixification" (the tendency to put "info" in front of words). The result is an intellectual gem in which the authors have polished some facets and, annoyingly, left others uncut. (Mar.) Copyright 2000 Cahners Business Information.
What People Are Saying
W. Brian Arthur
Wonderful! A necessary read for everyone interested in the new economy. Brown and Duguid show us that human interactions, human conversations, and human meaning will still form the beating heart of business.
Paul Saffo
Punctures old information revoultion myths and breaks important new ground.
New interesting textbook: Modern Approaches to Manufacturing Improvement or Art of the Motor
Data Mining: Concepts and Techniques
Author: Jiawei Han
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.
Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data.
Whether you are a seasoned professional or a new student of data mining, this book has much to offer you:
* A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data.
* Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning.
* Dozens of algorithms andimplementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects.
* Complete classroom support for instructors at mkp.com/datamining2e companion site.
Table of Contents:
| Ch. 1 | Introduction | 1 |
| Ch. 2 | Data preprocessing | 47 |
| Ch. 3 | Data warehouse and OLAP technology : an overview | 105 |
| Ch. 4 | Data cube computation and data generalization | 157 |
| Ch. 5 | Mining frequent patterns, associations, and correlations | 227 |
| Ch. 6 | Classification and prediction | 285 |
| Ch. 7 | Cluster analysis | 383 |
| Ch. 8 | Mining stream, time-series, and sequence data | 467 |
| Ch. 9 | Graph mining, social network analysis, and multirelational data mining | 535 |
| Ch. 10 | Mining object, spatial, multimedia, text, and Web data | 591 |
| Ch. 11 | Applications and trends in data mining | 649 |
| App | An introduction to Microsoft's OLE DB for data mining | 691 |
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