Open AccessBook
Big data: The next frontier for innovation, competition, and productivity
TLDR
The amount of data in the authors' world has been exploding, and analyzing large data sets will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey.Abstract:
The amount of data in our world has been exploding, and analyzing large data sets—so-called big data— will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey's Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.read more
Citations
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Business intelligence and analytics: from big data to big impact
TL;DR: This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A, and introduces and characterized the six articles that comprise this special issue in terms of the proposed BI &A research framework.
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Beyond the hype
Amir H. Gandomi,Murtaza Haider +1 more
TL;DR: The need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats is highlighted and the need to devise new tools for predictive analytics for structured big data is reinforced.
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Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
TL;DR: This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies currently adopt to deal with the Big Data problems.
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A few useful things to know about machine learning
TL;DR: Tapping into the "folk knowledge" needed to advance machine learning applications is a natural next step in the development of artificial intelligence systems.
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Big Data: A Survey
Min Chen,Shiwen Mao,Yunhao Liu +2 more
TL;DR: The background and state-of-the-art of big data are reviewed, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid, as well as related technologies.
References
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Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
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MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
Journal Article
The magical number seven, plus or minus two: some limits on our capacity for processing information
TL;DR: The theory of information as discussed by the authors provides a yardstick for calibrating our stimulus materials and for measuring the performance of our subjects and provides a quantitative way of getting at some of these questions.
Book
Information Rules: A Strategic Guide to the Network Economy
Carl Shapiro,Hal R. Varian +1 more
TL;DR: Information Rules will help business leaders and policy makers - from executives in the entertainment, publishing, hardware, and software industries to lawyers, finance professionals, and writers -- make intelligent decisions about their information assets.
Can Electronic Medical Record Systems Transform Health Care
Richard Hillestad,James H. Bigelow,Anthony G. Bower,Federico Girosi,Robin Meili,Richard Scoville,Roger Taylor +6 more
TL;DR: It is concluded that effective EMR implementation and networking could eventually save more than $81 billion annually--by improving health care efficiency and safety--and that HIT-enabled prevention and management of chronic disease could eventually double those savings while increasing health and other social benefits.