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

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Centre National de la Recherche Scientifique, UMR 7357 ICube Lab, CNRS-Strasbourg University, 67200 Strasbourg, France
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NTNU Trondheim, 7491 Trondheim, Norway
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Institute of Applied Informatics, Faculty of Science, University of South Bohemia Czech Republic, 370 05 České Budějovice, České
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Informatics Department, Kogakkan University, Ise Mie 516-0016, Japan
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Department of Mobility & Energy, University of Applied Sciences Upper Austria, 4232 Hagenberg Austria
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Namics—A Merkle Company, 20357 Hamburg, Germany
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Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(15), 3065; https://doi.org/10.3390/app9153065
Received: 23 June 2019 / Revised: 23 July 2019 / Accepted: 24 July 2019 / Published: 29 July 2019
(This article belongs to the Special Issue Perception and Communication)
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’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. View Full-Text
Keywords: big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor) big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor)
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Dresp-Langley, B.; Ekseth, O.K.; Fesl, J.; Gohshi, S.; Kurz, M.; Sehring, H.-W. Occam’s Razor for Big Data? On Detecting Quality in Large Unstructured Datasets. Appl. Sci. 2019, 9, 3065.

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