|
|
|
| Big Data Governance Mode Analysis Method for Urban Disaster Risk Response with Case Support |
| Liu Zhaoge1, Li Xiangyang2, Qiao Limin3, Wu Chong2 |
1.School of Public Affairs, Xiamen University, Xiamen 361005
2.School of Management, Harbin Institute of Technology, Harbin 150001
3.Beijing Beike Connection City Governance Technology Research Institute Co., Ltd., Beijing 100012 |
|
|
|
|
Abstract Big data applications encounter several problems, such as data imbalance, insufficient sharing, and a low level of precision and intelligence, in the field of urban disaster risk response (DRR). The construction and improvement of big data governance mode needs relevant knowledge support urgently; however, it faces the dilemma of lack of knowledge caused by scenario complexity and fragmented knowledge distribution. From the perspective of best practice theory, considering the knowledge learning of historical cases, this paper proposes a DRR big data governance mode analysis method based on case support. The core of the case-based method is to retrieve and transfer the governance modes that can be used for the target scenario through scenario similarity matching and build the available mode set. Targeting the retrieved available modes, the mode application effect data are combined to diagnose the mode application problems and select the governance modes. Subsequently, the integration of multi-case modes is completed from the perspective of management complexity and implementation cost to generate high-quality modes that can effectively solve the actual big data application problems. The rationality of the proposed analysis method is analyzed through a case study of urban community fire prevention in Puyang City, Henan Province. The use case results indicate that the proposed method can accurately transfer and apply historical experience, which is conducive to integrating fragmented knowledge, solving the problem of knowledge scarcity in complex scenarios, and constantly improving the value of big data application and governance mode in the DRR field.
|
|
Received: 01 August 2023
|
|
|
|
1 刘奕, 张宇栋, 张辉, 等. 面向2035年的灾害事故智慧应急科技发展战略研究[J]. 中国工程科学, 2021, 23(4): 117-125.
2 国务院关于印发“十四五”国家应急体系规划的通知[EB/OL]. (2022-02-14). http://www.gov.cn/zhengce/content/2022-02/14/content_ 5673424.htm.
3 张梦茜, 王超. 大数据驱动的重大公共安全风险治理: 内在逻辑与模式构建[J]. 甘肃行政学院学报, 2020(4): 37-45, 125.
4 陈伟珂, 刘国义. 城市内涝灾害防治困境的突破——基于数据治理模式的研究[J]. 灾害学, 2021, 36(1): 42-47.
5 Clark N, Guiffault F. Seeing through the clouds: processes and challenges for sharing geospatial data for disaster management in Haiti[J]. International Journal of Disaster Risk Reduction, 2018, 28: 258-270.
6 Silva M M, Poleto T, Silva L C, et al. A grey theory based approach to big data risk management using FMEA[J]. Mathematical Problems in Engineering, 2016, 2016: 9175418.
7 Cumbie B A, Sankar C S. Choice of governance mechanisms to promote information sharing via boundary objects in the disaster recovery process[J]. Information Systems Frontiers, 2012, 14(5): 1079-1094.
8 Liu Z G, Li X Y, Zhu X H. Scenario modeling for government big data governance decision-making: Chinese experience with public safety services[J]. Information & Management, 2022, 59(3): 103622.
9 Alhassan I, Sammon D, Daly M. Data governance activities: a comparison between scientific and practice-oriented literature[J]. Journal of Enterprise Information Management, 2018, 31(2): 300-316.
10 朱琳, 赵涵菁, 王永坤, 等. 全局数据: 大数据时代数据治理的新范式[J]. 电子政务, 2016(1): 34-42.
11 陈国青, 吴刚, 顾远东, 等. 管理决策情境下大数据驱动的研究和应用挑战——范式转变与研究方向[J]. 管理科学学报, 2018, 21(7): 1-10.
12 Papanastasiou N. ‘Best practice as a governing practice: producing best practice in a European Commission working group.’[J]. Journal of Education Policy, 2021, 36(3): 327-348.
13 Mallesh N, Zhao M, Meintker L, et al. Knowledge transfer to enhance the performance of deep learning models for automated classification of B cell neoplasms[J]. Patterns, 2021, 2(10): 100351.
14 Sunil S. Big data governance: an emerging imperative[M]. Boise: MC Press, 2012.
15 姚鑫, 郭海湘, 顾明赟, 等. 基于案例推理的滑坡灾害应急相似案例智能生成研究[J]. 系统工程理论与实践, 2021, 41(6): 1570-1584.
16 Liu Z G, Li X Y. Full view scenario model of big data governance in community safety service[C]// Proceedings of the 8th International Conference on Information Communication and Management. New York: ACM Press, 2018: 44-49.
17 Janssen M, Konopnicki D, Snowdon J L, et al. Driving public sector innovation using big and open linked data (BOLD)[J]. Information Systems Frontiers, 2017, 19(2): 189-195.
18 Janssen M, van den Hoven J. Big and Open Linked Data (BOLD) in government: a challenge to transparency and privacy?[J]. Government Information Quarterly, 2015, 32(4): 363-368.
19 周毅, 陈必坤, 马江华, 等. 基于文本量化分析的我国公共数据治理政策发展研究[J]. 情报学报, 2023, 42(4): 436-452.
20 柳亦博. 人工智能阴影下: 政府大数据治理中的伦理困境[J]. 行政论坛, 2018, 25(3): 97-103.
21 王刚, 汪杨, 王珏, 等. 基于证据分组合成的企业数据治理评价研究[J]. 系统工程理论与实践, 2016, 36(6): 1505-1516.
22 Xin J, Huang C F. Fire risk analysis of residential buildings based on scenario clusters and its application in fire risk management[J]. Fire Safety Journal, 2013, 62(8): 72-78.
23 刘铁民. 应急预案重大突发事件情景构建——基于“情景-任务-能力”应急预案编制技术研究之一[J]. 中国安全生产科学技术, 2012, 8(4): 5-12.
24 于峰, 李向阳, 刘昭阁. 城市灾害情景下应急案例本体建模与重用[J]. 管理评论, 2016, 28(8): 25-36.
25 Liu Z G, Li X Y, Jomaas G. Effects of governmental data governance on urban fire risk: a city-wide analysis in China[J]. International Journal of Disaster Risk Reduction, 2022, 78: 103138.
26 周利敏, 童星. 灾害响应2.0: 大数据时代的灾害治理——基于“阳江经验”的个案研究[J]. 中国软科学, 2019(10): 1-13.
27 应急管理部消防救援局. 中国消防救援年鉴(2019年卷)[M]. 北京: 应急管理出版社, 2020.
28 国务院安全生产委员会关于印发《“十四五”国家消防工作规划》的通知[EB/OL]. (2022-04-14). https://www.mem.gov.cn/gk/zfxxgkpt/fdzdgknr/202204/t20220414_411713.shtml.
29 复旦大学, 国家信息中心数字中国研究院. 中国地方政府数据开放报告(2020下半年)[EB/OL]. (2021-01-22). http://dsj.guizhou.gov.cn/xwzx/gnyw/202101/t20210122_66269368.html.
30 Abraham R, Schneider J, vom Brocke J. Data governance: a conceptual framework, structured review, and research agenda[J]. International Journal of Information Management, 2019, 49(6): 424-438.
31 Syed Mustapha S M F D. Case-based reasoning for identifying knowledge leader within online community[J]. Expert Systems with Applications, 2018, 97(7): 244-252.
32 张涛, 翁康年, 张倩帆, 等. 基于情境案例推理的播前收视率预测方法[J]. 管理工程学报, 2020, 34(6): 156-164.
33 刘天畅, 李向阳, 于峰. 案例驱动的CI系统应急能力不足评估方法[J]. 系统管理学报, 2017, 26(3): 464-472.
34 谢勇, 王红卫. 基于逆向推理策略的模型集成[J]. 计算机集成制造系统, 2002, 8(9): 690-695.
35 王颜新, 李向阳, 徐磊. 突发事件情境重构中的模糊规则推理方法[J]. 系统工程理论与实践, 2012, 32(5): 954-962.
36 Alhassan I, Sammon D, Daly M. Data governance activities: an analysis of the literature[J]. Journal of Decision Systems, 2016, 25(sup1): 64-75.
37 Janssen M, van der Voort H, Wahyudi A. Factors influencing big data decision-making quality[J]. Journal of Business Research, 2017, 70: 338-345.
38 张琦, 刘人境, 杨晶玉. 知识转移绩效影响因素分析[J]. 科学学研究, 2019, 37(2): 311-319.
39 郑大庆, 黄丽华, 张成洪, 等. 大数据治理的概念及其参考架构[J]. 研究与发展管理, 2017, 29(4): 65-72.
40 刘昭阁. 城市公共安全的基层社会大数据治理分析方法[D]. 哈尔滨: 哈尔滨工业大学, 2021: 1-22. |
|
|
|
