Big data: How do your data grow?

By C. Lynch. Nature, 2008

756 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6e42ed47688746c1a81b6e79ca562d6c9d0be12e

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In Molecular Biology

Citations in the corpus

Deep Transfer Learning for Intelligent Cellular Traffic Prediction Based on Cross-Domain Big Data (2019, background): “AS TECHNOLOGICAL innovations gather pace, the smart phone evolution in the past decade has accelerated data generation and explosion, which has sped the era of big data [1]–[4].”
Distributed learning for Random Vector Functional-Link networks (2015, background): “The ICT world, however, is being rapidly reshaped by emerging trends such as big data [22], pervasive computing [33], commodity computing [11], Internet of things [2], and several others.”
A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line (2017, background): “The second challenge is how to incorporate the big data analytics [42] into digital twin model.”
A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality (2017, methodology): “This implies a continued development of computer hardware, statistical theories and numerical methods, as well as the creation and maintenance of open databases [65,66].”
An Integrated System for Regional Environmental Monitoring and Management Based on Internet of Things (2014, background): “However, challenges remain in the proper handling of “Big Data” [8] in both scientific and industrial areas, which need effective mechanisms for the acquisition, processing, transmission, storing, and management of massive data and information, and new paradigm of integrated EIS is expected to provi”
Big data, bigger dilemmas: A critical review (2015, background): “A similar uncertainty is prevalent in academic writing, where writers refer to the topic as “a moving target” (Anonymous, 2008, p.1) where data “can be ‘big’ in different ways” (Lynch, 2008, p. 28).”
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014, neutral): “With diversified data provisions, such as sensor networks, telescopes, scientific experiments, and high throughput instruments, the datasets increase at exponential rate [178,110] as demonstrated in Fig.”
Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors (2017, methodology): “As the data floods faced by our digital daily life, Big Data has been widely studied from both academia and practitioners (Lynch 2008; Frankel and Reid 2008; Russom 2011; LaValle et al. 2011; McAfee and Brynjolfsson 2012; Fan and Bifet 2013; Keim, Qu, and Ma 2013; Zhong et al. 2015).”
Big Data Analytics in Healthcare (2015, background): “Various attempts at defining big data essentially characterize it as a collection of data elements whose size, speed, type, and/or complexity require one to seek, adopt, and invent new hardware and software mechanisms in order to successfully store, analyze, and visualize the data [1–3].”
Big data for forecasting the impacts of global change on plant communities (2017, background): “Big data refers to data sets so large or complex that they challenge established methods for data capture, curation, storage, analysis, and transfer (Lynch, 2008).”