Occam's Razor for Big Data? On Detecting Quality in Large Unstructured Datasets

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12310%2F19%3A43900048" target="_blank" >RIV/60076658:12310/19:43900048 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2076-3417/9/15/3065/htm" target="_blank" >https://www.mdpi.com/2076-3417/9/15/3065/htm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app9153065" target="_blank" >10.3390/app9153065</a>

Result language

angličtina

Original language name

Occam's Razor for Big Data? On Detecting Quality in Large Unstructured Datasets

Original language description

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam&apos;s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10102 - Applied mathematics

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Applied Sciences-Basel

ISSN

2076-3417

e-ISSN

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Volume of the periodical

9

Issue of the periodical within the volume

15

Country of publishing house

CH - SWITZERLAND

Number of pages

27

Pages from-to

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UT code for WoS article

000482134500120

EID of the result in the Scopus database

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