Professor Gill Dobbie


Gill worked in industry for a couple of years before she became an academic. She has held permanent and visiting positions at the University of Melbourne, Victoria University of Wellington and the National University of Singapore.

Research | Current

Gill has a wide range of research interests, including databases, the web, and software engineering.

She is interested both in structured and semistructured data. More specifically, she is interested in how data can best be organized and managed, how the semantics of the data can be retained and expressed, and how querying can be carried out efficiently.

Her main areas of interest pertain to databases and the web. She has worked in the foundations of database systems, defining logical models for various kinds of database systems, and reasoning about the correctness of algorithms in that setting. With colleagues at the National University of Singapore, she has defined a data model for semistructured data (called ORA-SS), providing a language independent description of the data. The group she was working with has used the ORA-SS data model to define a normal form for ORA-SS schema, defined valid views for semistructured databases, and described a storage structure for semistructured databases using object relational databases.

Selected publications and creative works (Research Outputs)

  • Anderson, R., Koh, Y. S., Dobbie, G., & Bifet, A. (2019). Recurring concept meta-learning for evolving data streams. Expert Systems with Applications, 138.10.1016/j.eswa.2019.112832
    Other University of Auckland co-authors: Yun Sing Koh
  • Bian, R., Koh, Y. S., Dobbie, G., & Divoli, A. (2019). Identifying Top-k Nodes in Social Networks: A Survey. ACM COMPUTING SURVEYS, 52 (1)10.1145/3301286
    Other University of Auckland co-authors: Yun Sing Koh
  • Huang, D. T. J., Koh, Y. S., & Dobbie, G. (2019). Interpreting intermittent bugs in mozilla applications using change angle. Communications in Computer and Information Science. 10.1007/978-981-13-6661-1_25
    Other University of Auckland co-authors: Yun Sing Koh, David Huang
  • Wong, I. S., Dobbie, G., & Koh, Y. S. (2019). Items2Data: Generating synthetic boolean datasets from itemsets. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10.1007/978-3-030-12079-5_6
    Other University of Auckland co-authors: Yun Sing Koh
  • Anderson, R., Koh, Y. S., & Dobbie, G. (2018). Predicting Concept Drift in Data Streams Using Metadata Clustering. Proceedings of the International Joint Conference on Neural Networks. 10.1109/IJCNN.2018.8489606
    Other University of Auckland co-authors: Yun Sing Koh
  • Tu, Y. C., Dobbie, G., Warren, I., & Meads, A. (2018). An experience report on a boot-Camp style programming course. SIGCSE 2018 - Proceedings of the 49th ACM Technical Symposium on Computer Science Education. 10.1145/3159450.3159541
    Other University of Auckland co-authors: Andrew Meads, Yu-Cheng Tu
  • Cai, C.-H., Sun, J., & Dobbie, G. (2018). B-Repair: Repairing B-Models Using Machine Learning. Paper presented at 23rd International Conference on Engineering of Complex Computer Systems (ICECCS), Melbourne, AUSTRALIA. 12 December - 14 December 2018. 2018 23RD INTERNATIONAL CONFERENCE ON ENGINEERING OF COMPLEX COMPUTER SYSTEMS (ICECCS). (pp. 10). 10.1109/ICECCS2018.2018.00012
    Other University of Auckland co-authors: Jing Sun
  • Koh, Y. S., Huang, D. T. J., Pearce, C., & Dobbie, G. (2018). Volatility Drift Prediction for Transactional Data Streams. Paper presented at 18th IEEE International Conference on Data Mining Workshops (ICDMW), Singapore, SINGAPORE. 17 November - 20 November 2018. 2018 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM). (pp. 6). 10.1109/ICDM.2018.00140
    Other University of Auckland co-authors: Yun Sing Koh, David Huang


Contact details

Primary office location

SCIENCE CENTRE 303 - Bldg 303
Level 5, Room 519
New Zealand

Web links