Detection of location significance from quality enhanced trajectory data. This project aims to develop effective and efficient methods to utilise large scale Global Positioning System, route and location data to provide individuals, business, government and social groups, the ability to assess the relative significance of locations and associated services, for use in applications such as transportation, logistics, public safety, tourism and utilities.
In-memory moving objects analytics for real-time business applications. This project aims to develop a novel computing foundation based on in-memory technologies to address significant challenges of big data and location-based business intelligence, building upon the well-recognised research excellence in spatiotemporal data management at the University of Queensland, and HANA, SAP's (Systems, Applications, Products in data processing) new in-memory analytics platform.
Building Peer-to-Peer Middleware Infrastructure with Web Services. Peer-to-Peer (P2P) computing is a framework defining the interactions between systems acting as both clients and servers. Web services technology is an evolving set of Web standards based on eXtensible Markup Language (XML), and is considered as the newest approach to distributed computing. In this project, we aim to build a new type of P2P architectural framework that is truly interoperable and distributed, being platform and la ....Building Peer-to-Peer Middleware Infrastructure with Web Services. Peer-to-Peer (P2P) computing is a framework defining the interactions between systems acting as both clients and servers. Web services technology is an evolving set of Web standards based on eXtensible Markup Language (XML), and is considered as the newest approach to distributed computing. In this project, we aim to build a new type of P2P architectural framework that is truly interoperable and distributed, being platform and language independent in an Internet-wide context. Using Web services and message queuing, this project also aims to develop a robust middleware infrastructure consisting of a set of tools and programming libraries to ease the development of verifiable P2P applications on heterogeneous platforms.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE140100679
Funder
Australian Research Council
Funding Amount
$395,220.00
Summary
Real-time query processing over multi-dimensional uncertain data streams. Real-time query processing of multi-dimensional uncertain data streams is fundamental in many applications such as environmental monitoring and location based services. This project aims to develop effective techniques to explore the massive multi-dimensional uncertain data streams in real time. The project will develop, analyse, implement and evaluate novel indexing and query processing techniques to effectively and effic ....Real-time query processing over multi-dimensional uncertain data streams. Real-time query processing of multi-dimensional uncertain data streams is fundamental in many applications such as environmental monitoring and location based services. This project aims to develop effective techniques to explore the massive multi-dimensional uncertain data streams in real time. The project will develop, analyse, implement and evaluate novel indexing and query processing techniques to effectively and efficiently support a set of primitive queries including rank-based queries, dominance-based queries and proximity-based queries. The results of this project will be an important complement to the development of data stream systems and will bring considerable social, economic and technological benefits to Australia.Read moreRead less
Monitoring social events for user online behaviour analytics. This project aims to investigate the influence of public attention on steering user online behaviour. The exponential growth of online behaviour data makes online behaviour analytics increasingly important in social, commercial and political environments, but existing methods rely on user profiles only. The project will unify external social events with user profiles for behaviour analytics, and develop approaches for event database i ....Monitoring social events for user online behaviour analytics. This project aims to investigate the influence of public attention on steering user online behaviour. The exponential growth of online behaviour data makes online behaviour analytics increasingly important in social, commercial and political environments, but existing methods rely on user profiles only. The project will unify external social events with user profiles for behaviour analytics, and develop approaches for event database indexing, event-influenced behaviour modelling and prediction. The success of this project is expected to enhance users’ online experience and improve e-commerce’s market value.Read moreRead less
Taming the uncertainty in trajectory data. This project aims to develop effective and efficient methods to manage large scale uncertain trajectory data. It provides individuals, business, government and social groups the ability to explore significant uncertain trajectories and their patterns, for important usages in location based services, logistic, transportation and tourism.
Deep attribute-aware hashing for cross retrieval. This project aims to enable individuals, industries and governments, to freely access vital and linked information carried in different media types from different sources by developing a deep attribute-aware framework that embeds heterogeneous features into a shared data space to achieve effective and efficient cross system information retrieval. With the proliferation of heterogeneous data sources, there is an urgent need to enable search and re ....Deep attribute-aware hashing for cross retrieval. This project aims to enable individuals, industries and governments, to freely access vital and linked information carried in different media types from different sources by developing a deep attribute-aware framework that embeds heterogeneous features into a shared data space to achieve effective and efficient cross system information retrieval. With the proliferation of heterogeneous data sources, there is an urgent need to enable search and retrieval across different media types and domains. The framework developed by the project uses deep learning methods to develop meaningful image attributes to positively bridge the modality gap and the domain gap when hash functions are affixed to data. This project will significantly advance the research of multimedia retrieval, and benefit a series of related research problems whenever heterogeneous multimedia data are involved in their applications.Read moreRead less
Synergising multimedia content understanding with social data analysis. This project aims to develop novel approaches to explore synergies within big social multimedia data from both social and multimedia perspective. It provides individuals, groups, and businesses the ability to tap into the wisdom of crowds to enlarge knowledge base, enhance user experience, understand the pulse of crowds and make informed decision.
Techniques for active conceptual modelling and guided data mining for rapid knowledge discovery. Quick, accurate responses to rapidly evolving phenomena are essential. This project will develop a platform able to accept data from a variety of sources in advance of the full definition of the associated conceptual model. The project will facilitate rapid querying and direct manipulation of the mining process allowing fast, user-oriented results.
Continuous intent tracking for virtual assistance using big contextual data. Recently launched Virtual Assistant products such as Amazon Echo and Google Home are commanded by voice and can call apps to do simple tasks like setting timers and playing music. The next-generation virtual assistants will recommend things to be done proactively rather than waiting for commands passively. This project aims to develop algorithms that can predict what a user intends to do and therefore help virtual assis ....Continuous intent tracking for virtual assistance using big contextual data. Recently launched Virtual Assistant products such as Amazon Echo and Google Home are commanded by voice and can call apps to do simple tasks like setting timers and playing music. The next-generation virtual assistants will recommend things to be done proactively rather than waiting for commands passively. This project aims to develop algorithms that can predict what a user intends to do and therefore help virtual assistants make recommendations that suit users’ needs accurately. It will benefit many service industry sectors of Australia by enabling virtual assistants to provide services proactively.Read moreRead less