Massive Data Flows - Self-organization of Energy, Material, and Information Flows

نویسندگان

  • Takashi Ikegami
  • Mizuki Oka
چکیده

As opposed to “big data” as a buzz word, we attempt to find a new pattern or structure generated by selforganization in the flow of the massive data. We call this approach Massive Data Flows (MDF). Rather than making use of big data, we are interested in the new phenomena and theory that allows us to deal with the data without losing the autonomy, complexity, dynamics and structure that the data itself has. The concept of MDF provides a new methodology for understanding data flows, including material, energy and information flows. Analogous to the Darwinian evolution and the organization of an ecological system, MDF patterns grow, and this growth determines the organization of system’s own state autonomously, i.e. organization of data by the data for the data, Furthermore, self-organization of MDF is not caused by a closed internal dynamics but it will be found at the interface between the endogenous and exogenous data flows. MDF is the generic term that explains the co-evolution of excess flows and the adaptive system in which self-organizational patterns will successively occur. Composed by the heterogeneous elements, MDF should possess self-referential, selfmodifying, self-sustaining and innovative dynamics. In this paper, we will exemplify five concrete examples of MDF; the default mode network and the excitability of the web, the autonomous sensor network, chemical oil droplets, and court and cave computation with a many-core system as potential MDF systems.

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تاریخ انتشار 2014