Mining big data: current status, and forecast to the future

By Wei Fan, Albert Bifet. 2013

836 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis (2018, background): “Gartner summarizes these ﬁve dimensions in its deﬁnition of Big Data in 2012 as “high volume, velocity and variety information assets cost-effective demand, innovative forms of information processing for enhanced insight and decision mak-ing” (Fan & Bifet, 2013 .”
The role of big data in smart city (2016, background): “Moreover, analytics of the weather data can help inform the people in advance about the possible hazardous conditions (e.g., flood information, extremely hot weather, drought, and so on) (Fan & Bifet, 2013).”
Measuring Engagement in eHealth and mHealth Behavior Change Interventions: Viewpoint of Methodologies (2018, neutral): “Visualization tools [82] and engagement indices such as those discussed by Baltierra et al [79] and Couper et al [88], or consideration of new data analyses techniques may be useful to get insights into data [95,96].”
Freshwater biomonitoring in the Information Age (2017, background): “It is often stated that characteristics of big data fulfill five “Vs”: vol ume, velocity, variety, variability, and value (Fan and Bifet 2013).”
Elastic stack in action for smart cities: Making sense of big data (2017, background): “• Velocity: obtaining useful information in real time from the raw data arriving continuously; In paper [8], Big Data is also characterized by two more V’s:”
Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection (2020, neutral): “The term big data was first proposed in 1998 in a lecture by John Mashey (Fan and Bifet 2013).”
A review of clustering techniques and developments (2017, background): “Big data [161-168] is also an emerging issue.”
Online Machine Learning in Big Data Streams (2018, methodology): “ng needs other solutions, such as communication between processing elements [174] via an interconnection topology [219]. For an outlook, mostly batch distributed data mining solutions are surveyed in [78]. Requirement 3: Online learning from big data has to be implemented in a distributed stream pro”
Machine Learning With Big Data: Challenges and Approaches (2017, background): “For example, value is often added as a 5th V [22], [26].”
Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors (2017, methodology): “As the data ﬂ oods 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).”
Assessing the impact of big data on firm innovation performance: Big data is not always better data (2020, background): “This could be due to the fact that bigger data is not always better data (Fan & Bifet, 2013).”