Special Issue "Perspectives on Big Data and Data Sciences"
A special issue of Axioms (ISSN 2075-1680).
Deadline for manuscript submissions: closed (31 January 2020)
Interests: algorithmic information theory; quantum computing
Special Issues, Collections and Topics in MDPI journals
Special Issue in Axioms: Editorial Board Members' Collection Series: Quantum Information Theory
2. School of Medicine, Tufts University, Boston, MA 02111, USA
Interests: logic and theory of computation; denotational semantics and lambda-calculus; type theory, category theory and their applications to computer Science; cognitive foundations of mathematics; interfaces mathematics; physics; biology
It all started with the explosion of data generated since the dawn of the digital age in the 1970s. Currently, every two days we create as much data as we did from the beginning of time until 2000. Big data and data science have appeared because of the rise of computers, the Internet and the technology capable of generating, storing, organising and processing data from the world we live in.
Data science is based on the principle that the more data one knows about anything, the more reliably one can gain new insights and make predictions about what will happen in the future. Data science projects use artificial intelligence and machine learning to teach computers to identify what various data represent and learn to spot patterns much more quickly and reliably than humans. Data science has been considered in health care (data-driven medicine), in predicting and responding to natural and man-made disasters, in preventing crime, in developing autonomous devices (cars), and in many other social, business, learning projects. It will be difficult to name an area where data science cannot be, in principle, used.
Data science may give unprecedented insights and opportunities, but it also raises concerns and questions related to ethics, privacy, security and knowledge. These concerns pertain to working exclusively with correlations instead of understanding, the source (and/or origin) of data, the bias in the choice of observables and metrics, the difficulty or even impossibility to understand and verify the correctness of solutions and decisions. As some regularities are just due to the large data size, are there critical sizes beyond which the validity of prediction is questionable, or, conversely, ascertained?
Data science does not build on invariance properties in the way physics makes inert phenomena intelligible mathematically. The collection of data, in biology, is a particularly delicate issue: the phylo- and onto-genetic specification of organisms makes biological in vitro/in vivo measurement sensitive to synchronic as well as to diachronic events. How can data science help to enrich these practices, instead of blurring the complexity of life sciences under the size of databases?
This Special Issue aims to present technical results in mathematics, physics, biology and other areas which can help the understanding of data science but also its limits and potential dangers.
We look forward to your contributions to this Special Issue,
Prof. Dr. Cristian S. Calude
Prof. Dr. Giuseppe Longo
Prof. Dr. Maël Montévil
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- Big data
- Data science
- Spurious correlation
- Data science in physics, biology and beyond