The Fourth Paradigm: Data-Intensive Scientific Discovery

By Tony (Anthony) John Grenville Hey. 2009

2,689 citations recorded by the source corpus.

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Citations in the corpus

BigScience: A Case Study in the Social Construction of a Multilingual Large Language Model (2022, neutral): “and the posthumous anthology (Hey et al., 2009) it inspired.”
Big Data: A Survey (2014, neutral): “In January 2007, Jim Gray, a pioneer of database software, called such transformation “The Fourth Paradigm” [23].”
Open challenges for data stream mining research (2014, background): “Aggregation with selective forgetting of past information is proposed in [25; 42] in the classification context: the former method [25] slides a window over the stream, while the latter [42] forgets entities that have not appeared for a while, and summarizes the information in frequent itemsets, whi”
Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide (2015, background): “The idea of the "fourth paradigm" of scientific research [8] posits that data-intensive, cross-domain research is the way of the future, and collaborative, distributed networks of researchers will work together to tackle scientific research problems.”
Exascale computing and big data (2015, background): “Similarly, “big data,” machine learning, and predictive data analytics have been hailed as the fourth paradigm of science, 12 allowing researchers to extract insights from both scientific instruments and computational simulations.”
A Survey of Large Scale Data Management Approaches in Cloud Environments (2011, background): “To tackle this problem, Jim Gray came up with the heuristic rule of ”20 queries” [1].”
Cybersecurity data science: an overview from machine learning perspective (2020, background): “Machine learning can significantly change the cybersecurity landscape and data science is leading a new scientific paradigm [13, 14].”
Defining architecture components of the Big Data Ecosystem (2014, positive): “This is also in accordance with the definition given by Jim Gray in his seminal book [10].”
A comparative assessment of decision-support tools for ecosystem services quantification and valuation (2013, background): “Although an ambitious goal, semantic meta modeling offers a path forward in improving ecosystem service quantification in an era of “big data” (Fox and Hendler, 2009; Villa, 2010).”
Data Sharing by Scientists: Practices and Perceptions (2011, methodology): “Following the previous research paradigms (experimental, theoretical, and computational), this new era has been called ‘‘the fourth paradigm: data-intensive scientific discovery’’ where ‘‘all of the science literature is online, all of the science data is online, and they interoperate with each othe”
Developing Open-Source Scientific Practice * (2018, background): “In addition to experiment and theory, the notions of simulation and data-intensive discovery are often referred to as “third and fourth pillars” of science [12].”
Data-driven geography (2014, positive): “It is also led to the suggestion that we are entering a new, fourth phase of science that will be driven not so much by careful observation by individuals, or theory development, or computational simulation, as by this new abundance of digital data (Hey et al. 2009).”