Dr. Deng was awarded a PhD degree in computer science at The University of Queensland (UQ) in 2007 with focus on data and knowledge engineering. He also hold a Master degree in Information and Communication Technology in 2001 and a Bachelor degree in Electrical Engineering in 1994. He had been an acting lecturer and course convener in the School of Information Technology and Electrical Engineering at UQ during 2008-2012, and the co-supervisor of one PhD student. He had been a postdoctoral research fellow at CSIRO ICT center in 2007. He had been an ARC Australian Postdoctoral Fellow working during 2010-2012. He had been a researcher in Huawei Noah Ark’s Research Lab, Hong Kong from June 2013. The Noah’s Ark Lab conducts state-of-the-art research in data mining and artificial intelligence by exploring theories and building intelligent systems.
More information on Dr Deng's research interests and publications can be found on his personal website.
- COSC1114 Operating System Principles
- 2015 PhD Project (one scholarship, please contact me if you are interested)
The smart city are also known as ubiquitous city, intelligent cities, virtual cities, digital cities, knowledge cities which are all perspectives on the idea that ICT is central to the operation of the future city, even though it has been recognized the use of ICT alone does not make cities smart - other dimensions include cultural and natural amenities, people and skills, knowledge precincts and governance. Many cities in Asian, Europe, and America such as Hong Kong, Amsterdam and Osaka currently have smart city projects. Robust urban computing requires the combination of a huge amount of static knowledge about the city (i.e. urban, social and cultural knowledge) with an even much larger set of dynamic data (originating in real time from heterogeneous and multiple sources). The most dominant dynamic data perhaps are the trajectories of massive humans and vehicles in the city. In the last decade, trajectory-data-intensive urban computing has attracted much attention from ICT researchers and industrial practitioners to address problems, combined with other city information, in urban planning, city transport, social applications, environment, urban energy consumption, economy, public safety and security.
However, urban computing on trajectory data is still in its early stage. First, the concept of smart city encompasses so many aspects of complex city life such that new urban computing problems continuously emerge, for example, the recharge station positioning for electrical vehicles. Second, most exiting urban computing solutions explore single-source data (e.g., using taxi trajectory data only); as a consequence, the robustness of solution is largely constrained by the limited information of the single-source data (e.g., taxi trajectory data has nothing related to the population using public transport systems). In the progress towards smart city, the information about different facets in the same urban space, unknown previously, becomes more and more available, for example, the trajectories of bus/subway passengers using smartcard ticketing system devices, the trajectories of cell phone users, the trajectories of buses, and the trajectories of social media check-ins. While heterogeneous trajectory data provide unprecedented rich information to enable robust solutions of urban computing problems, it introduces enormous technical challenges in order to settle heterogeneity.
Aim 1: Develop a heterogeneous data management system which comprises a distributed data storage and management layer and data access layer.
Aim 2: The comprehensive transport patterns in hierarchical scale levels will be disclosed to enable robust traffic efficiency analysis throughout a city, to learn the impact of city events to local traffic, and to enable other urban computing tasks investigated in this project.
Aim 3: The study provides insight to the characteristics of regions and social communities in the city which helps better facility planning, social interaction, and collaboration of local communities for mutual benefits.
- Masters Project
- Li, C.,Liu, C.,Deng, K.,Yu, X. (2017). (In Press) Data-driven charging strategy of PEVs under transformer aging risk In: IEEE Transactions on Control Systems Technology, , 1 - 14
- Deng, K.,Li, J.,Pang, C.,Li, J.,Zhou, X. (2016). Access time oracle for planar graphs In: IEEE Transactions on Knowledge and Data Engineering, 28, 1959 - 1970
- Zhu, F.,Luo, C.,Yuan, M.,Zhu, Y.,Zhang, Z.,Gu, T.,Deng, K.,Rao, W.,Zeng, J. (2016). City-scale localization with telco big data In: Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), Indianapolis, United States, 24-28 October 2016
- Wang, M.,Li, H.,Cui, J.,Deng, K.,Bhowmick, S.,Dong, Z. (2016). PINOCCHIO: Probabilistic influence-based location selection over moving objects In: IEEE Transactions on Knowledge and Data Engineering, 28, 3068 - 3082
- Liu, C.,Deng, K.,Li, C.,Li, J.,Li, Y.,Luo, J. (2016). The optimal distribution of electric-vehicle chargers across a city In: Proceedings of the 16th IEEE International Conference on Data Mining (ICDM 2016), Barcelona, Spain, 12-15 December 2016
- Deng, K.,Li, X.,Lu, J.,Zhou, X. (2015). Best keyword cover search In: IEEE Transactions on Knowledge and Data Engineering, 27, 61 - 74
- Huang, Y.,Zhu, F.,Yuan, M.,Deng, K.,Li, Y.,Ni, B.,Dai, W.,Yang, Q.,Zeng, J. (2015). Telco churn prediction with big data In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Australia, 31 May-4 June 2015
- Xie, Q.,Pang, C.,Zhou, X.,Zhang, X.,Deng, K. (2014). Maximum error-bounded Piecewise Linear Representation for online stream approximation In: The VLDB (Very Large Data Bases) Journal, 23, 915 - 937
- Yuan, M.,Deng, K.,Zeng, J.,Li, Y.,Ni, B.,He, X.,Wang, F.,Dai, W.,Yang, Q. (2014). OceanST: A distributed analytic system for large-scale spatiotemporal mobile broadband data In: Proceedings of the Very Large Data Bases (VLDM) Endowment, Hangzhou, China, 1-5 September, 2013
- Shang, S.,Yuan, B.,Deng, K.,Xie, K.,Zheng, K.,Zhou, X. (2012). PNN query processing on compressed trajectories In: Geoinformatica: an international journal on advances of computer science for geographic information systems, 16, 467 - 496
- Effective and Efficient Query Processing over Dynamic Social Networks (Administered by Swinburne University of Technology). Funded by: ARC Discovery Projects 2016 from (2016 to 2018)
5 PhD Current Supervisions