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Dr Jing Lu joined the University of Winchester Business School in 2016 as a Senior Lecturer in Data Analytics. She is a research-active academic in business analytics and management science with extensive experience of teaching in higher education previously as an Associate Professor at Solent University and in China.

Jing was awarded a PhD from the Department of Computing and Information Systems at the University of Bedfordshire in 2006 and became a Fellow of the HE Academy in 2009. Her research activities have extended across data-driven frameworks and process methodology through computer applications and visualisation to business analytics and knowledge discovery, including modelling of real-world problems using digital technologies.

Jing’s research output comprises over 30 peer-reviewed publications in applied computing and data science. Her recent research has featured in international journals covering management science, modelling and business intelligence as well as international conferences on data science, machine learning, AI, information systems, medical informatics, data engineering, knowledge discovery and data mining.

Jing's experience from research and knowledge exchange has helped to inform her teaching in various ways through supervision of projects and dissertations as well as curriculum development, e.g. for BSc Data Science (Apprenticeship), BSc Digital and Technology Solutions (Data Analytics), MSc Digital Marketing and Analytics as well as the Executive MBA at Winchester Business School.

Areas of expertise

  • Business Analytics and Management Science

  • Data Mining and Modelling

  • Applied Data Science


Journal Publications / Book Chapters

  • Lu, J. (2022) Data science in the business environment: Architecture, process and tools. In: D. Garg et al. (Eds.) Advanced Computing. Communications in Computer and Information Science, Vol. 1528, Springer Cham, pp.279-293. ISBN 978-3-030-95501-4
  • Lu, J. (2022) Data science in the business environment: Insight management for an Executive MBA. The International Journal of Management Education, Vol. 20, No. 1, Elsevier.
  • Lu, J., Cairns, L. and Smith, L. (2021) Data science in the business environment: Customer analytics case studies in SMEs. Journal of Modelling in Management, Vol. 16, No. 2, pp.689-713
  • Lu, J. (2020) Data analytics research-informed teaching in a digital technologies curriculum. INFORMS Transactions on Education Vol. 20, No. 2, pp.57-72
  • Lu, J. (2019) Data science in the business environment: Skills analytics for curriculum development. In: G. Nicosia et al. (Eds.) Machine Learning, Optimization and Data Science. Lecture Notes in Computer Science, Vol. 11331, Springer International Publishing AG, pp.116-128. ISBN 978-3-030-13708-3
  • Lu, J. (2018) A data-driven framework for business analytics in the context of big data. In: A. Benczr et al. (Eds.) New Trends in Databases and Information Systems. Communications in Computer and Information Science, Vol. 909, Springer Cham, pp.339-351. ISBN 978-3-030-00062-2
  • Lu, J., Hales, A., Rew, D. (2017) Modelling of cancer patient records: A structured approach to data mining and visual analytics. In: M. Bursa et al. (Eds.) Information Technology in Bio- and Medical Informatics. Lecture Notes in Computer Science, Vol. 10443, Springer International Publishing AG, pp.30-51. ISBN 978-3-319-64264-2
  • Lu, J., Wang, C.Q., Keech, M. (2017) A novel approach to knowledge discovery and representation in biological databases. International Journal of Bioinformatics Research and Applications Vol. 13, No. 4, pp.352-375
  • Lu, J., Keech, M., Chen, W.R., Wang, C.Q. (2013) Concurrent sequential patterns mining and frequent partial orders modelling. International Journal of Business Intelligence and Data Mining Vol. 8, No. 2, pp.132-154

International Conference Proceedings  

  • Lu, J., Hales, A., Rew, D., Keech, M. (2016) Timeline and episode-structured clinical data: Pre-processing for data mining and analytics. Proceedings of the 32nd IEEE International Conference on Data Engineering, ICDE 2016, Helsinki Workshop on Health Data Management and Mining, pp.64-67. ISBN 978-1-5090-2109-3
  • Mills-Mullett, A., Lu, J. (2015) Mining fuzzy time-interval patterns in clinical databases. Proceedings of the 35th International Conference on AI, Cambridge; Proceedings AI-2015, Springer International Publishing Switzerland, pp.399-404. ISBN 978-3-319-25030-4
  • Lu, J., Hales, A., Rew, D., Keech, M., Fröhlingsdorf, C., Mills-Mullett, A., Wette, C. (2015) Data mining techniques in health informatics: A case study from breast cancer research. Proceedings of the 6th International Conference on IT in Bio- and Medical Informatics, Valencia; LNCS 9267, Springer International Publishing Switzerland, pp.56-70. ISBN 978-3-319-22740-5
  • Lu, J., Keech, M. (2015) Emerging technologies for health data analytics research: A conceptual architecture. 2nd International Workshop on NoSQL Databases, Valencia; Proceedings of DEXA 2015, IEEE Computer Society, pp.225-229. ISBN 978-1-4673-7581-8
  • Lu, J., Keech, M., Wang, C.Q. (2014) Protein data modelling for concurrent sequential patterns. 5th International Workshop on Biological Knowledge Discovery and Data Mining, BIOKDD 2014, Munich; Proceedings of DEXA 2014, IEEE Computer Society, pp.5-9. ISBN 978-1-4799-5721-7
  • Wang, C.Q., Lu, J., Keech, M. (2014) Applications of concurrent sequential patterns in protein data mining. Proceedings of the 10th International Conference on Machine Learning and Data Mining, MLDM 2014, St. Petersburg; LNAI 8556, Springer International Publishing Switzerland, pp.243-257. ISBN 978-3-319-08978-2
  • Lu, J., Keech, M., Wang, C.Q. (2013) Applications of concurrent access patterns in web usage mining. Proceedings of the 15th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2013, Prague; LNCS 8057, Springer-Verlag Berlin Heidelberg, pp.339-348. ISBN 978-3-642-40130-5
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