Jing Ni
Jing Ni was born in 1968, holds a Ph.D. in Management and is a Professor and Master's Supervisor. She graduated from the Department of Chemical Engineering at Taiyuan University of Technology in 1991 with a Bachelor of Engineering degree, obtained a Master's degree in Management from the Institute of Scientific and Technical Information of China in 2003, and received a Ph.D. in Management from Agricultural Information Institute, Chinese Academy of Agricultural Sciences, in 2014. From October 2009 to October 2010, she was a Visiting Scholar at Michigan State University in the United States.
She currently teaches courses such as “Python Data Analysis and Applications” and “Multivariate Statistical Analysis”, and has long been engaged in research on business data analysis and knowledge management. She has presided over two projects funded by the Ministry of Education Humanities and Social Sciences Planning Fund and two Beijing Social Science Fund projects, and has led and completed multiple government- and enterprise-commissioned projects. She has also participated in several national-level projects, including the National High Technology Research and Development Program (863 Program) and the National Natural Science Foundation of China. As first author, she has published more than 30 academic papers, nearly 20 of which are indexed by SSCI/SCI/EI/CSSCI, and has authored five monographs. She has been repeatedly recognized as an Excellent Advisor for Undergraduate Research Training Programs, and serves as a peer reviewer for Library and Information Service, a Category-A journal under the Ministry of Education.
I. Main Research Areas
(1) Social commerce data analysis and mining;
(2) User behavior in the new social commerce environment;
(3) Knowledge management and data provenance.
II. Awards and Honors
1. Excellent Advisor of the Beijing First Prize Team, "Innovation, Creativity and Entrepreneurship" Competition, 2019.
2. Excellent Advisor for Outstanding Undergraduate Graduation Theses of Beijing Municipal Universities, 2021.
3. Excellent Advisor for Outstanding Postgraduate Theses (University level), 2023.
4. Excellent Postgraduate Supervisor (University level), 2024.
5. Excellent Advisor of the National College Student Big Data Analysis Technology Skills Competition, 2025.
III. Main Research Projects
1. Ministry of Education Humanities and Social Sciences Planning Fund: “Research on Security Mechanisms and Data Provenance in a Linked Data Environment” (Project No.: 12YJA870014), Principal Investigator, 2012-2014.
2. Beijing Social Science Fund (General Project): “Data Provenance and Monitoring Strategies for Rumors in Social Networks” (Project No.: 14SHB010), Principal Investigator, 2015-2017.
3. Ministry of Education Humanities and Social Sciences Planning Fund: “Research on Data Provenance and Trust Mechanisms in the Evolution of Public Opinion in Social Networks” (Project No.: 15YJAZH052), Principal Investigator, 2016-2018.
4. Beijing Social Science Fund (General Project): “Research on Citizens Privacy Concerns and Compliance-Oriented Personal Data Provenance Mechanisms in Smart Cities” (Project No.: 19XCB006), Principal Investigator, 2019-2022.
5. Enterprise horizontal project: "Knowledge Management Enhancement Project for Sinopec Changling Branch Company", Principal Investigator, 2015–-2016.
6. Enterprise horizontal project: “Data Mining and Error Provenance in Medical Testing”, Principal Investigator, 2017–2020.
7. Daxing Science and Technology Commission commissioned project: "Research on Accelerating the Transformation of Scientific and Technological Achievements to Promote the Development of High-Tech Industries in Daxing District" , October 2023-June 2024.
IV. Representative Academic Papers
1. Ni, J., & Meng, X. (2016). Data provenance based on evolution of similar web page texts. Library and Information Service, 60(13), 134–140, 148. (CSSCI)
2. Ni, J., & Meng, X. (2014). Research on the positioning and query mechanism of provenance information in web applications. Library and Information Service, 58(11), 14–20. (CSSCI)
3. Ni, J., & Meng, X. (2014). The PROV provenance model and its web applications. Library and Information Service, 58(3), 13–19. (CSSCI)
4. Ni, J., & Meng, X. (2013). Comparative study of data provenance description languages in a linked data environment. New Technology of Library and Information Service, 2013(3), 18–23. (CSSCI)
5. Wang, X., Liu, N., Liang, A., & Ni, J. (corresponding author). (2018). Rethinking the definition and theoretical framework of the semantic web from a semiotic perspective. Modern Information, 37(8), 33–40. (CSSCI)
6. Ni, J., Zhao, X., & Qian, Q. (2003). A comparative analysis of the compilation and web application of foreign e-government thesauri. Journal of the China Society for Scientific and Technical Information, 2003(5), 565–571. (CSSCI)
7. Ni, J., Zhao, X., & Li, H. (2003). Research on subject indexing algorithms in an e-government thesaurus application system. High Technology Communications, 2003(10), 15–19. (EI)
8. Ni, J., Jin, B., Ning, S., & Wang, X. (2019). The numerical simulation of the airflow distribution and energy efficiency in data centers with three types of aisle layout. Sustainability, 11(18), 4937. (SCI & SSCI)
9. Ni, J., Zhang, B., Jin, B., & Wang, X. (2017). Simulation of thermal distribution and airflow for efficient energy consumption in small data centers. Sustainability, 9(4), 664. (SCI & SSCI)
10. Ni, J., Gao, G., & Chen, P. (2016). Chinese text auto-categorization on petro-chemical industrial processes. Cybernetics and Information Technologies, 16(6), 69–82. (EI)
11. Ni, J., Hao, J., Li, X., & Zhao, T. (2016). Modeling and tracing web content provenance. International Journal of Database Theory and Application, 9(4), 309–320. (EI)
12. Ni, J., Zhao, X., & Zhu, L. (2007). A semantic web service-oriented architecture for enterprises. In IFIP Advances in Information and Communication Technology, 254 (pp. 535–544). (EI)
13. Ni, J.* (corresponding author). (2022). Sustainable transport in a smart city: Prediction of short-term parking space through improvement of LSTM algorithm. Applied Sciences, 2022, Article 11046.
V. Published Works
Ni, J. (2017). Data provenance based on the PROV model. Beijing: Sinopec Press.
VI. Recruitment Information
I mainly admit postgraduate students specializing in Business Administration (1202, academic degree; Enterprise Management) and Auditing (0257, professional degree; Big Data Auditing).