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
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.
Indexing Large Video Databases to Support Efficient Query Processing. This project aims to develop breakthrough database technology that leverages the advances in video data capturing, computer vision based object recognition, multimedia tagging, large scale database systems and parallel processing, to provide the capability of managing massive video data with enriched semantic information and enabling database-like flexible and efficient video information search. It is expected to establish a n ....Indexing Large Video Databases to Support Efficient Query Processing. This project aims to develop breakthrough database technology that leverages the advances in video data capturing, computer vision based object recognition, multimedia tagging, large scale database systems and parallel processing, to provide the capability of managing massive video data with enriched semantic information and enabling database-like flexible and efficient video information search. It is expected to establish a new data management and processing foundation for big video data analytics.Read moreRead less
Probabilistic search over large-scale uncertain graphs. Efficiently conducting structure-based search is fundamental in many real applications. The project aims to develop effective searching techniques for large-scale imprecise and/or uncertain graphs. This project will develop, analyse, implement, and evaluate novel indexing and query processing techniques to efficiently conduct structure-based probabilistic queries over large uncertain graphs, including structure search, structure similarity ....Probabilistic search over large-scale uncertain graphs. Efficiently conducting structure-based search is fundamental in many real applications. The project aims to develop effective searching techniques for large-scale imprecise and/or uncertain graphs. This project will develop, analyse, implement, and evaluate novel indexing and query processing techniques to efficiently conduct structure-based probabilistic queries over large uncertain graphs, including structure search, structure similarity search, all-matches, vertex-pair similarity search and top-k search. The success of this project will be an important complement to the current development of graph database management technology and will bring considerable social, economic and technological benefits to Australia.Read moreRead less
Ranking complex objects in a multi-dimensional space. The project aims to develop novel, advanced techniques to rank complex objects in a multi-dimensional space. The success of the project not only brings a breakthrough in technology development but also provides training for high quality personnel in this important and growing area, and brings considerable economic and social benefits to Australia.
Discovery Early Career Researcher Award - Grant ID: DE120102144
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Continuously monitoring uncertain objects in a multi-dimensional space. The project aims to develop novel, advanced techniques to continuously monitor uncertain objects. The success of the project not only brings breakthroughs in technology development but also provides training for high quality personnel in this important and growing area, and brings considerable economic and social benefits to Australia.
Discovery Early Career Researcher Award - Grant ID: DE140100999
Funder
Australian Research Council
Funding Amount
$395,220.00
Summary
Big Graph Processing in MapReduce. As a large branch of big data processing, big graph processing is becoming increasingly important in both industry and academia, due to the large expressive power of graphs to model complex relationships among entities in the real world. This project will find highly scalable solutions to process big graphs using MapReduce. MapReduce is a big data processing framework that is shown to be scalable to handle structured query language-styled queries but is still o ....Big Graph Processing in MapReduce. As a large branch of big data processing, big graph processing is becoming increasingly important in both industry and academia, due to the large expressive power of graphs to model complex relationships among entities in the real world. This project will find highly scalable solutions to process big graphs using MapReduce. MapReduce is a big data processing framework that is shown to be scalable to handle structured query language-styled queries but is still open when it is used to process big graphs. Most of the problems studied in this project are fundamental graph problems that are not well studied in MapReduce. This project will enhance big graph processing which is beneficial for both science and society.Read moreRead less
Effective, efficient and scalable processing of big geo-textual streams. This project aims to develop novel approaches to realise the value of geo-textual data, which carries both location and textual information. The project expects to address three key challenges brought by massive volumes and high speeds of big geo-textual streams: better user experiences; increased efficiency; and greater scalability in query processing. The project should provide individuals, business and government agencie ....Effective, efficient and scalable processing of big geo-textual streams. This project aims to develop novel approaches to realise the value of geo-textual data, which carries both location and textual information. The project expects to address three key challenges brought by massive volumes and high speeds of big geo-textual streams: better user experiences; increased efficiency; and greater scalability in query processing. The project should provide individuals, business and government agencies with the ability to unlock key values in the overwhelming volume of high-speed, big geo-textual streams for important usage in many emerging key applications, such as social media analytics, location-based services, social networks, e-marketing and cybersecurity.Read moreRead less