Big Data for Development: A Review of Promises and Challenges

By Martin Hilbert. 2016

569 citations recorded by the source corpus.

Subfields: Epidemiology, Management Information Systems, Transportation

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Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “In social sciences, Hilbert [14] compares the impact of big data analytics to that of the invention of the telescope and microscope for astronomy and biology, respectively.”
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, background): “Instead, the following additional 4 Vs combined with the volume of the data make up the big data model.(265,266)”
Mobile phone location data for disasters: A review from natural hazards and epidemics (2021, unknown): “(Hilbert, 2016) Several open data challenges have been initiated by collaborations between academia and industry data providers, such as the Data4Development Challenge held by Orange, which provided mobile phone data from Ivory Coast for analysis.”
Human resources for Big Data professions: A systematic classification of job roles and required skill sets (2017, background): “In particular, as noticed by Hilbert ( Hilbert, 2016 ), over the last two decades we have seen an exponential increase of information flow, stock and computational power.”
Aligning artificial intelligence with climate change mitigation (2022, unknown): “For instance, the use of ML in certain contexts may yield or exacerbate societal inequities, for example, by widening the digital divide 37,111 , through algorithmic bias 112 or by shifting power from public to large private entities by virtue of who controls relevant data or intellectual capital.”
Machine Learning for the Developing World (2018, methodology): “…ML4D, the limitations and difficulties of successfully deploying technologies in the developing world are often highlighted (Ali et al. 2016; Hilbert 2016), as is the immense potential of computer science and ML to positively impact these areas of the world (Dias and Brewer 2009; Quinn et…”
Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey (2018, background): “…(great volume and collection), “variety” (various types of data), “velocity” (rapid production, generation and analysis), “value” (low density and huge value), “variability” (inconsistency of data prevents easy handling and processing), and “veracity” (quality of data vary greatly) [37], [183].”
Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities (2020, background): “With regards to external process sensing, BDACs can contribute toward identifying bottlenecks or other potential hazards in supply chains [87], detect market disturbances and monitor the financial environment [88,89], and help predict prices and availability of key resources for production [90,91].”