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
Responsible modelling respecting privacy, data quality, and green computing. With the unprecedented growing impact of data on science, the economy and society, there comes the need for responsible data science practices which are accountable for the social good. This project aims to investigate the challenging problem of how to provide responsible data management, spanning across privacy-aware data exploration, resilient modelling to cope with imperfect data, and efficient model architectures fo ....Responsible modelling respecting privacy, data quality, and green computing. With the unprecedented growing impact of data on science, the economy and society, there comes the need for responsible data science practices which are accountable for the social good. This project aims to investigate the challenging problem of how to provide responsible data management, spanning across privacy-aware data exploration, resilient modelling to cope with imperfect data, and efficient model architectures for resource-constrained environments. This will be achieved by developing theories and techniques for complex real-world multi-modal data retrieval throughout the data life-cycle. The expected outcomes will significantly contribute to building capability in emerging technologies in the context of responsible data science. Read moreRead less
Driving Towards Greener and Safer Roads using Big Spatiotemporal Data. This project aims to design novel techniques for using big spatiotemporal data to reduce the impact of road transport on the environment and improve road safety. This project expects to address key challenges and lay scientific foundations of using the big data for developing a next-generation eco-friendly navigation system and increasing situational awareness for road transport safety. Expected outcomes of this project inclu ....Driving Towards Greener and Safer Roads using Big Spatiotemporal Data. This project aims to design novel techniques for using big spatiotemporal data to reduce the impact of road transport on the environment and improve road safety. This project expects to address key challenges and lay scientific foundations of using the big data for developing a next-generation eco-friendly navigation system and increasing situational awareness for road transport safety. Expected outcomes of this project include novel big data management and analytics techniques, and new edge computing models for vehicular networks. The success of this project should bring several key benefits including reducing greenhouse gas emissions on roads, facilitating urban planning, and improving road safety.Read moreRead less
New Graph Mining Technologies to Enable Timely Exploration of Social Events. This project aims to develop scalable and effective graph mining techniques for the timely exploration of social events that are the hottest happenings in online information networks. The research will primarily exploit the complex network structures and non-structural properties of streaming social data to report what is happening in a timely fashion. This project will lay the theoretical foundations of this emerging f ....New Graph Mining Technologies to Enable Timely Exploration of Social Events. This project aims to develop scalable and effective graph mining techniques for the timely exploration of social events that are the hottest happenings in online information networks. The research will primarily exploit the complex network structures and non-structural properties of streaming social data to report what is happening in a timely fashion. This project will lay the theoretical foundations of this emerging field to strengthen Australia’s world leadership role in data science. Practically, the novel theories and data analytics technologies developed will benefit the Australian economy and society by monitoring emergencies, tracking prevailing sentiments, and spotting investment opportunities through timely event responses.Read moreRead less
Scaling Disk-Resident Learned Indexes For Database Systems. This project aims to investigate new disk-resident learned indexing algorithms to store and process data in database systems by advancing the state-of-the-art in memory-resident learned modeling. This project expects to generate new knowledge in the area of digital storage technologies utilising novel and efficient techniques in learned indexing for big data. This should provide significant benefits to enable modern database systems to ....Scaling Disk-Resident Learned Indexes For Database Systems. This project aims to investigate new disk-resident learned indexing algorithms to store and process data in database systems by advancing the state-of-the-art in memory-resident learned modeling. This project expects to generate new knowledge in the area of digital storage technologies utilising novel and efficient techniques in learned indexing for big data. This should provide significant benefits to enable modern database systems to scale with the massive growth of data, improve the efficiency of data processing, improve the effectiveness of projects that utilise big data, and dramatically reduce energy costs in Australian data centres when storing and retrieving data from databases and lower their carbon footprints.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
Cost-effective Edge Service Provisioning in the Last Mile of 5G. This project aims to deliver a suite of novel approaches for enabling cost-effective last-mile service provisioning in the 5G mobile edge computing (MEC). This project is the world's first attempt to systematically tackle the critical service provisioning challenges in the last mile where base stations link users to MEC applications. It offers a practical solution for provisioning software vendors' MEC services cost-effectively. Th ....Cost-effective Edge Service Provisioning in the Last Mile of 5G. This project aims to deliver a suite of novel approaches for enabling cost-effective last-mile service provisioning in the 5G mobile edge computing (MEC). This project is the world's first attempt to systematically tackle the critical service provisioning challenges in the last mile where base stations link users to MEC applications. It offers a practical solution for provisioning software vendors' MEC services cost-effectively. This project should drive Australia's 5G transition and innovations, promote its post-COVID economic recovery and resilience by enabling various real-time mobile and IoT applications, e.g., telehealth, remote learning/working, industry 4.0, and ensure its pioneering position in the global 5G research.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
Towards knowledge discovery from imperfect and evolving data. Information extraction from data is critical, both to analyse and protect consumer data. However, many learning techniques are developed using perfect, static datasets, quite different to messy, ever-changing real-world data. This project aims to develop data analytics techniques that can extract accurate information in complex structures from imperfect/incomplete data that changes over time. Expected outcomes are a prototype tool, te ....Towards knowledge discovery from imperfect and evolving data. Information extraction from data is critical, both to analyse and protect consumer data. However, many learning techniques are developed using perfect, static datasets, quite different to messy, ever-changing real-world data. This project aims to develop data analytics techniques that can extract accurate information in complex structures from imperfect/incomplete data that changes over time. Expected outcomes are a prototype tool, tested on real datasets, that combines new techniques in data modelling, algorithm development, and system design. Likely benefits are enhanced Australia's competence in data science through student training and new, robust data tools relevant to critical sectors such as cybersecurity, healthcare, and defence.Read moreRead less