Discovery Early Career Researcher Award - Grant ID: DE240100165
Funder
Australian Research Council
Funding Amount
$443,847.00
Summary
Evolving privacy and utility in data storage and publishing. This project aims to develop a distributed evolutionary computation-based framework to optimize data privacy and utility in distributed database systems. It intends to synchronously solve the conflicting challenges of privacy preservation and utility maintenance in multi-objective, dynamic, and multitasking scenarios. Expected outcomes include a new computation framework as a service and freely available distributed computation models, ....Evolving privacy and utility in data storage and publishing. This project aims to develop a distributed evolutionary computation-based framework to optimize data privacy and utility in distributed database systems. It intends to synchronously solve the conflicting challenges of privacy preservation and utility maintenance in multi-objective, dynamic, and multitasking scenarios. Expected outcomes include a new computation framework as a service and freely available distributed computation models, evolutionary algorithms, and knowledge-transfer strategies. Anticipated benefits include theoretical contributions to artificial intelligence, cyber security, distributed computation, and a service to eliminate data owners’ privacy concerns while guaranteeing the value of data in further utilization.Read moreRead less
Fast effective clustering technologies for highly dynamic massive networks. Clustering is a fundamental data mining and analysis task. In an interconnected evolving world, friendships and information flows are modelled as large dynamic networks. Structural clustering and correlation clustering are important and well-studied approaches for static networks; for evolving networks, where links appear and disappear over time, we lack efficient techniques. Anticipated outcomes are new practical cluste ....Fast effective clustering technologies for highly dynamic massive networks. Clustering is a fundamental data mining and analysis task. In an interconnected evolving world, friendships and information flows are modelled as large dynamic networks. Structural clustering and correlation clustering are important and well-studied approaches for static networks; for evolving networks, where links appear and disappear over time, we lack efficient techniques. Anticipated outcomes are new practical clustering algorithms for dynamic networks – with performance guarantees of efficiency and clustering quality – and prototype software, guiding us to pick a good clustering. Expected benefits include better understanding of spread in evolving social networks, accelerating the software testing cycle, and improved topic detection.Read moreRead less
Fast Reconstruction and Real-time Rendering of Immersive Light Field Video. This project aims to develop new learning-based methods for reconstructing and rendering 3D immersive videos from multi-view 2D videos. The project expects to generate new knowledge in the areas of data mining, multimedia, pattern recognition and deep learning. Expected outcomes of this project include new deep neural networks to represent 3D videos, neural methods for high-fidelity video rendering and efficient 3D video ....Fast Reconstruction and Real-time Rendering of Immersive Light Field Video. This project aims to develop new learning-based methods for reconstructing and rendering 3D immersive videos from multi-view 2D videos. The project expects to generate new knowledge in the areas of data mining, multimedia, pattern recognition and deep learning. Expected outcomes of this project include new deep neural networks to represent 3D videos, neural methods for high-fidelity video rendering and efficient 3D video reconstruction and rendering algorithms. This should provide significant benefits to a diverse range of practical applications, such as autonomous driving, virtual reality, healthcare, advanced manufacturing, and many other 3D applications.Read moreRead less
Beyond Query: Exploratory Subgraph Discovery and Search System. Exploring co-working user groups in dynamic network data is a vital challenge in many applications, for example, in online education. This project aims to discover new relationships of users and compute their co-working performance in continuous time periods. The outcomes of the project are to design effective subgraph exploratory models, three novel types of subgraph search solutions, and devise a friendly exploratory subgraph sear ....Beyond Query: Exploratory Subgraph Discovery and Search System. Exploring co-working user groups in dynamic network data is a vital challenge in many applications, for example, in online education. This project aims to discover new relationships of users and compute their co-working performance in continuous time periods. The outcomes of the project are to design effective subgraph exploratory models, three novel types of subgraph search solutions, and devise a friendly exploratory subgraph search system for supporting the real-time network data analytics. The success of the project will make a significant contribution to the scientific foundation of graph data mining and its applications in data engineering domains, as well as benefiting co-working performance of people in Australian labor markets.Read moreRead less
Situation-aware Multi-sided Personalised Analytics in Spatial Crowdsourcing. This project aims to create a next generation recommender system that enables enhanced task allocation and route recommendation on spatial crowdsourcing platforms. It expects to address key challenges in situation-aware reliable recommendation for big spatial crowdsourcing data, which is vital in improving users’ service experience and decision making. Expected outcomes of this project include advanced data models, effi ....Situation-aware Multi-sided Personalised Analytics in Spatial Crowdsourcing. This project aims to create a next generation recommender system that enables enhanced task allocation and route recommendation on spatial crowdsourcing platforms. It expects to address key challenges in situation-aware reliable recommendation for big spatial crowdsourcing data, which is vital in improving users’ service experience and decision making. Expected outcomes of this project include advanced data models, efficient algorithms and query techniques to create a Crowd-guided Advanced Spatial Crowdsourcing Analytics (CASCA) system that is effective, efficient, crowd-guided, and situation-aware. It will benefit crowdsourced media data analysis and big data fields, bringing economic and social benefits to Australian industries and users.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100200
Funder
Australian Research Council
Funding Amount
$428,847.00
Summary
Cohesive Multipartite Subgraph Discovery in Large Heterogeneous Networks. This project aims to devise novel cohesive multipartite subgraph models and corresponding efficient search algorithms based on various applications. Significant advances in understanding big data will be enabled by the proposed novel theories and algorithms, which can leverage the value of heterogeneous network data and serve as the foundation of network analytics. Expected outcomes of this project include novel cohesive m ....Cohesive Multipartite Subgraph Discovery in Large Heterogeneous Networks. This project aims to devise novel cohesive multipartite subgraph models and corresponding efficient search algorithms based on various applications. Significant advances in understanding big data will be enabled by the proposed novel theories and algorithms, which can leverage the value of heterogeneous network data and serve as the foundation of network analytics. Expected outcomes of this project include novel cohesive multipartite subgraph models, efficient searching algorithms and platforms for heterogeneous networks. This should provide significant benefits for different organisations and a myriad of applications dealing with heterogeneous network data, including but not limited to e-commerce, cybersecurity, health and social networks.Read moreRead less
Empowering Next-Generation Spatial Digital Twins with Linked Spatial Data. This project aims to design novel algorithms for aligning and querying of spatial data from heterogeneous sources. Spatial data is being generated at an unprecedented rate due to the prevalence of mobile devices and ubiquitous connectivity, which enables a novel application, spatial digital twins. However, harnessing this data in spatial digital twins is hampered by the isolation of data from different sources. The projec ....Empowering Next-Generation Spatial Digital Twins with Linked Spatial Data. This project aims to design novel algorithms for aligning and querying of spatial data from heterogeneous sources. Spatial data is being generated at an unprecedented rate due to the prevalence of mobile devices and ubiquitous connectivity, which enables a novel application, spatial digital twins. However, harnessing this data in spatial digital twins is hampered by the isolation of data from different sources. The project will investigate algorithms to align and query spatial data from heterogeneous sources for high accessibility. It will enable novel applications with advanced spatial analytical querying needs, such as emergency planning, benefiting location-based service providers, urban planners, and emergency management agencies.Read moreRead less
Next Generation Spatial Data Management for Virtual Spatial Systems. This project aims to design novel spatial data retrieval methods for efficient and accurate querying of large datasets with location information. Spatial data is being generated at an unprecedented rate due to the prevalence of mobile devices and ubiquitous connectivity. However, harnessing this data is hampered by outdated and inefficient methods. The project will investigate data retrieval methods that self-optimise for high ....Next Generation Spatial Data Management for Virtual Spatial Systems. This project aims to design novel spatial data retrieval methods for efficient and accurate querying of large datasets with location information. Spatial data is being generated at an unprecedented rate due to the prevalence of mobile devices and ubiquitous connectivity. However, harnessing this data is hampered by outdated and inefficient methods. The project will investigate data retrieval methods that self-optimise for high query efficiency and accuracy, by utilising underlying real-world data patterns. It will enable novel applications for virtual spatial systems with large-scale querying needs, such as spatial digital twins and metaverses, benefiting location-based service providers, urban planners, and emergency management agencies.Read moreRead less