The World’s Technological Capacity to Store, Communicate, and Compute Information

By Martin Hilbert, Priscila López. Science, 2011

1,672 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/64d2fe89ba7f105cb96796742f4dcbf4e56bd2ca

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In Computational Theory and Mathematics

In Computer Networks and Communications

Citations in the corpus

An introduction to quantum machine learning (2014, background): “Since the volume of globally stored data is growing by around 20% every year (currently ranging in the order of several hundred exabytes [1]), the pressure to ﬁnd innovative approaches to machine learning is rising.”
Big Data for Development: A Review of Promises and Challenges (2016, neutral): “Some 500 PlayStation 3 consoles amount to the average performance of a supercomputer in 2007, which makes this alternative quite price competitive (López and Hilbert, 2012).”
Responsible Data Science (2017, background): “This is not surprising since torrents of data are vigorously changing the way we do business, socialize, conduct research, and govern society (Hilbert and Lopez 2011; Manyika et al. 2011; White House 2016).”
Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review (2018, methodology): “Big data are characterized by the 8 Vs [1]: volume (exponentially increasing volumes) [2], variety (wide range of datasets), velocity (high processing speed) [3], veracity, value [4,5], variability, volatility, and validity [1].”
Best Care at Lower Cost: The Path to Continuously Learning Health Care in America (2013, background): “And computer processing speed has grown by an average rate of 60 percent per year over the past several decades (Hilbert and López, 2011).”
Data Mining for Internet of Things: A Survey (2014, background): “To clarify what the IoT refers to, several good surveys were presented recently each of which view the IoT from a different perspective: challenges [4], applications [7], standards [8], and Manuscript received October 1, 2012; revised June 2, 2013.”
Big Data Analytics in Intelligent Transportation Systems: A Survey (2019, background): “Traditional data storage infrastructure and database tools have been unable to cope with the increasingly large and complex mass data [158].”
Does ‘bigger’ mean ‘better’? Pitfalls and shortcuts associated with big data for social research (2015, background): “In 2007, it reached a global capacity of 6.4 trillion instructions per second for personal computers, which is equal to an increase of 58 % per year since the 1980s (Hilbert and López 2011).”
Opportunities at the Frontiers of Spintronics (2015, background): “As such, spintronics was instrumental in the digital revolution, wherein analog information was essentially replaced by digital data [5] to the point where the transmission, manipulation and storage of information have become a pervasive aspect of our lives.”
Big Data in product lifecycle management (2015, background): “The academic milestones [8, 27, 30–35] in the brief history of “Big Data” since 1997 are illustrated in Fig.”