Personal profile
Research Expertise
John Roddick's research is diverse but largely focusses on temporal data mining as applied to health, traffic and defence-related datasets.
Research Biography
Professor John Roddick joined Flinders in April 2000 after 15 years at the Universities of Tasmania and South Australia. This followed 10 years experience in the computing industry. He was Dean of the School of Computer Science, Engineering and Mathematics between January 2008 until July 2017. He is currently the Chair of the University's Academic Senate and Head of Engineering. He is a Fellow of both Engineers Australia and the Australian Computing Society.
Research Interests
Prof. Roddick's research specialises in the fields of:
- Data Mining and Knowledge Discovery - specifically in temporal and spatial data mining and as applied to medical and health data, and to defence and security;
- Conceptual Modelling - the development of conceptual models for complex systems and the manner in which these models can be used to build data-oriented solutions to difficult problems.
Since the late 1980s Professor Roddick has contributed to the area of conceptual modelling and intelligent databases including the development of techniques for data summarization, spatio-temporal databases, query languages, evolution and change in data and metadata management, information semantics and, data mining and knowledge discovery. His papers in these areas have received around 7,000 citations (per Google Scholar) with 15 with over 100 citations. He has an h-index of 37.
His work has resulted in contributions to the design and development of database architectures, query languages and systems that enable the semantics inherent in data to be more readily understood and manipulated, thus enabling systems to adapt. Part of his work in temporal query languages, for example, contributed to the TSQL2 temporal language proposal. His research agenda has a particular focus on complex and large volumes of data, commonly using medical data as the application domain.
Professor Roddick has published widely, including a number of well-cited surveys. He maintains active collaborative links with a number of researchers internationally, and has undertaken commercial research contracts with a number of organisations in his area of expertise including the Defence Science and Technology Organisation (DSTO), the Royal Australasian College of Surgery, EDS Australia, PriceWaterhouseCoopers, the SA Government, rL Solutions and PowerHealth Solutions.
He is a member of the ARC College of Experts. His ORCID ID is 0000-0001-7024-0796.
Completed Supervisions
- Computer Science (8)
- Computer Science (UniSA) (4)
- Computer Science (4)
Research Areas
- Knowledge discovery, AI and data mining
Supervisory Interests
- Data mining and knowledge discovery
- Data semantics
- Database modelling
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 4 Quality Education
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SDG 11 Sustainable Cities and Communities
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- 1 Similar Profiles
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Optimised Clustering Association Rule Mining with Health Data
Das, P., Roddick, J. F. & Williams, P. A. H., 25 Dec 2024, PEEIACON 2024 : International Conference on Power, Electrical, Electronics and Industrial Applications. Institute of Electrical and Electronics Engineers, 6 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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An evaluation of HTM and LSTM for short-term arterial traffic flow prediction
Mackenzie, J., Roddick, J. & Zito, R., 1 May 2019, In: IEEE Transactions on Intelligent Transportation Systems. 20, 5, p. 1847-1857 11 p., 8424074.Research output: Contribution to journal › Article › peer-review
140 Citations (Scopus) -
An implementation of chaotic pseudo-random sequence generator based on pipelined architecture
Feng, K., Huang, X., Chu, S. C., Roddick, J. F. & Ding, Q., May 2019, In: Journal of Network Intelligence. 4, 2, p. 71-79 9 p.Research output: Contribution to journal › Article › peer-review
Open Access3 Citations (Scopus) -
Finger vein recognition scheme based on convolutional neural network using curvature gray image
Zhao, J. Y., Gong, J., Ma, S. T., Lu, Z. M., Chu, S. C. & Roddick, J. F., Aug 2019, In: Journal of Network Intelligence. 4, 3, p. 114-123 10 p.Research output: Contribution to journal › Article › peer-review
Open Access9 Citations (Scopus) -
Spatiotemporal fusion algorithm for single-time phase high resolution remote sensing image based on sparse representationy
Wang, X., Wang, X., Chu, S. C. & Roddick, J. F., Aug 2019, In: Journal of Network Intelligence. 4, 3, p. 100-108 9 p.Research output: Contribution to journal › Article › peer-review
Open Access2 Citations (Scopus)