JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2019, Vol. 54 ›› Issue (2): 1-29.doi: 10.6040/j.issn.1671-9352.0.2018.698

   

Big data structure-logic characteristics and big data law

SHI Kai-quan   

  1. School of Mathematics, Shandong University, Jinan 250100, Shandong, China
  • Published:2019-02-25

Abstract: P-sets, inverse P-sets are two models obtained by introducing dynamic features into a finite ordinary set X and improving the set X. By using P-sets, inverse P-sets and their dynamic and logical features, ∧-type big data structure and its generation with attribute conjunction extension-contraction feature, ∨-type big data structure and its generation with attribute disjunctive extension-contraction feature, and∧-∨type big data structure and its generation of the mixture of ∧-type big data and ∨-type big data are given respectively. The fusion and its generation of big data and the intelligent mining theorems of big data fusion are given. Granulation characteristics and granulation filtration theorems of big data are given. The heredity and its generation of big data, and heredity intelligence separation and separation theorem of big data are given. Big data hiding-camouflage and intelligent generation under the inference condition of P-augmented matrix, and big data hiding-camouflage theorems are given. The model and method of big data law generation, and the state of big data law are given. By using the state of big data law, the risk estimation of investment-profit big data is given.

Key words: big data structure, big data fusion, big data heredity, big data hiding-camouflage, big data law, risk estimation

CLC Number: 

  • O144
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