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

Challenges in data science: a complex systems perspective / Carbone, ANNA FILOMENA; Jensen, Meiko; Sato, Aki Hiro. - In: CHAOS, SOLITONS & FRACTALS. - ISSN 1873-2887. - STAMPA. - 90:(2016), pp. 1-7. [10.1016/j.chaos.2016.04.020]

Challenges in data science: a complex systems perspective

CARBONE, ANNA FILOMENA;
2016

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
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2646560
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