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《山东大学学报(理学版)》 ›› 2019, Vol. 54 ›› Issue (2): 1-29.doi: 10.6040/j.issn.1671-9352.0.2018.698

• •    

大数据结构-逻辑特征与大数据规律

史开泉   

  1. 山东大学数学学院, 山东 济南 250100
  • 发布日期:2019-02-25
  • 作者简介:史开泉(1945— ), 男, 教授, 博士生导师, 研究方向为数据智能系统理论与应用. E-mail:shikq@sdu.edu.cn
  • 基金资助:
    山东省自然科学基金资助项目(zr2013aq019);福建省自然科学基金资助项目(2015J05010)

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

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摘要： P-集合、逆P-集合是把动态特征引入到有限普通元素集合X内,改进有限普通元素集合X得到的两个模型,利用P-集合、逆P-集合与它们的动态特征、逻辑特征分别给出具有属性合取扩展-收缩特征的∧型大数据结构与生成、具有属性析取扩展-收缩特征的∨型大数据结构与生成、∧型大数据与∨型大数据混合的∧-∨型大数据结构与生成;给出大数据融合与生成、大数据融合智能挖掘定理;给出大数据的颗粒特征与颗粒过滤定理;给出大数据遗传与生成、大数据遗传智能分离与分离定理;给出在P-增广矩阵推理条件下大数据隐藏-伪装与智能生成、大数据隐藏-伪装定理;给出大数据规律生成模型与方法以及大数据规律状态;利用大数据规律状态,给出投资-利润大数据的风险估计。

关键词: 大数据结构, 大数据融合, 大数据遗传, 大数据隐藏-伪装, 大数据规律, 风险估计

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

中图分类号: 

  • O144
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[1] 陈保会,张凌,史开泉. P-信息智能动态融合与信息规律状态智能识别[J]. 山东大学学报（理学版）, 2018, 53(2): 83-87.
[2] 郭华龙,张凌. 数据分离与属性状态特征[J]. 山东大学学报（理学版）, 2017, 52(12): 89-94.
[3] 张秀全,李小朝. P-信息融合与它的P-矩阵推理智能生成[J]. 山东大学学报（理学版）, 2017, 52(4): 93-99.
[4] 徐凤生,于秀清,史开泉. 属性基数余-亏值定理与信息规律动态内-外分离[J]. 山东大学学报（理学版）, 2017, 52(4): 87-92.
[5] 任雪芳,张凌. 逆P-集合的扰动定理与数据的扰动挖掘[J]. 山东大学学报（理学版）, 2016, 51(12): 54-60.
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