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Challenges in data science: a complex systems perspective

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  • Carbone, Anna
  • Jensen, Meiko
  • Sato, Aki-Hiro

Abstract

The ability to process and manage large data volumes has been proven to be not enough to tackle the current challenges presented by “Big Data”. Deep insight is required for understanding interactions among connected systems, space- and time- dependent heterogeneous data structures. Emergence of global properties from locally interacting data entities and clustering phenomena demand suitable approaches and methodologies recently developed in the foundational area of Data Science by taking a Complex Systems standpoint. Here, we deal with challenges that can be summarized by the question: “What can Complex Systems Science contribute to Big Data? ”. Such question can be reversed and brought to a superior level of abstraction by asking “What Knowledge can be drawn from Big Data?” These aspects constitute the main motivation behind this article to introduce a volume containing a collection of papers presenting interdisciplinary advances in the Big Data area by methodologies and approaches typical of the Complex Systems Science, Nonlinear Systems Science and Statistical Physics.

Suggested Citation

  • Carbone, Anna & Jensen, Meiko & Sato, Aki-Hiro, 2016. "Challenges in data science: a complex systems perspective," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 1-7.
    • Handle: RePEc:eee:chsofr:v:90:y:2016:i:c:p:1-7
    DOI: 10.1016/j.chaos.2016.04.020
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    1. Ausloos, Marcel & Cerqueti, Roy & Mir, Tariq A., 2017. "Data science for assessing possible tax income manipulation: The case of Italy," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 238-256.
    2. Paulo Ferreira & Éder J.A.L. Pereira & Hernane B.B. Pereira, 2020. "From Big Data to Econophysics and Its Use to Explain Complex Phenomena," JRFM, MDPI, vol. 13(7), pages 1-10, July.
    3. Fabrizio Bonacina & Alessandro Corsini & Lucio Cardillo & Francesca Lucchetta, 2019. "Complex Network Analysis of Photovoltaic Plant Operations and Failure Modes," Energies, MDPI, vol. 12(10), pages 1-14, May.
    4. Deng, Ziwei & Li, Yuxuan & Zhu, Hongqiu & Huang, Keke & Tang, Zhaohui & Wang, Zhen, 2020. "Sparse stacked autoencoder network for complex system monitoring with industrial applications," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    5. Alvaro Gomez Losada, 2017. "Data science applications to connected vehicles: Key barriers to overcome," JRC Research Reports JRC108572, Joint Research Centre.

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