大数据2024,Vol.10Issue(2) :140-151.DOI:10.11959/j.issn.2096-0271.2023015

决策驱动型省级政务大数据治理技术架构及实现

Technical architecture and implementation of decision-driven provincial government big data governance

孟凡 杨群力 高阳
大数据2024,Vol.10Issue(2) :140-151.DOI:10.11959/j.issn.2096-0271.2023015

决策驱动型省级政务大数据治理技术架构及实现

Technical architecture and implementation of decision-driven provincial government big data governance

孟凡 1杨群力 2高阳3
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作者信息

  • 1. 江苏省战略与发展研究中心,江苏 南京 210036;计算机软件新技术国家重点实验室(南京大学),江苏 南京 210093
  • 2. 江苏省战略与发展研究中心,江苏 南京 210036
  • 3. 计算机软件新技术国家重点实验室(南京大学),江苏 南京 210093
  • 折叠

摘要

新时期政务数据在形态、产生机制等方面发生了巨大转变,呈现模态多样、体量巨大、结构复杂、时序演变等特征,给传统治理带来了全新的挑战.针对此问题,以省级政务大数据治理为切入点,分析了传统省级政务大数据治理的技术架构和瓶颈.围绕政府决策场景提出了一种决策驱动型省级政务大数据治理技术架构——凤翔模型,分析其内涵概念、设计原则和总体架构,并对其可行性进行论证.针对凤翔模型技术架构中的4项关键业务问题和关键技术问题,进一步提出针对性的技术实现思路,为各省市后续治理项目建设以及相关技术路线研究选型提供参考.

Abstract

In the new era,government data has undergone tremendous changes in terms of form and generation mechanism,presenting multi-modality,huge volume,complex structure,and time series evolution,which has brought new challenges to traditional governance.This paper takes provincial government big data governance as the starting point,and mainly includes three aspects:(1)analyzed the technical framework and bottlenecks of traditional provincial government big data governance;(2)based on the government decision-making scenario,proposed a decision-driven provincial government big data governance technology architecture(called Fengxiang model),introduced its components,design principles,overall architecture,and demonstrated its feasibility;(3)aiming at the four key business issues and key technical issues in the technical framework of Fengxiang model,further proposed targeted technical implementation ideas to provide a reference for the construction of follow-up governance projects in various provinces and cities and the research and selection of related technical routes.

关键词

政务大数据/数据治理/决策/复杂结构

Key words

government big data/data governance/decision making/complex structure

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基金项目

江苏省社会信用体系建设专项(JSZC-G2018-393)

计算机软件新技术国家重点实验室(南京大学)开放基金(KFKT2022B27)

出版年

2024
大数据
人民邮电出版社

大数据

CSTPCD
ISSN:2096-0271
参考文献量12
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