Linkage Infrastructure, Equipment And Facilities - Grant ID: LE180100158
Funder
Australian Research Council
Funding Amount
$348,026.00
Summary
A large-scale distributed experimental facility for the internet of things. This project aims to establish a large-scale, real-world experimental facility for the Internet of Things (IoT), which is currently missing in Australia, as well as in the rest of the world. The project is expected to be an essential instrument to achieve Australia’s leadership on key enabling technologies of the IoT, and to provide Australian research community with a unique platform for large-scale experimentation and ....A large-scale distributed experimental facility for the internet of things. This project aims to establish a large-scale, real-world experimental facility for the Internet of Things (IoT), which is currently missing in Australia, as well as in the rest of the world. The project is expected to be an essential instrument to achieve Australia’s leadership on key enabling technologies of the IoT, and to provide Australian research community with a unique platform for large-scale experimentation and evaluation of IoT technologies and services. The project will also serve as a vehicle for the education and training of Australia’s next generation of scholars and engineers, and contribute to Australia’s scientific visibility.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.
Directionality-Aware Cohesive Subgraph Search over Directed Graphs. Searching cohesive subgraphs around a set of user-specified seed vertices in big graphs has many applications including cybersecurity, crime detection, social marketing and public health. This project aims to investigate directionality-aware search of cohesive subgraphs over directed graphs by designing effective models and developing efficient and scalable algorithms. This project expects to address key challenges and lay scien ....Directionality-Aware Cohesive Subgraph Search over Directed Graphs. Searching cohesive subgraphs around a set of user-specified seed vertices in big graphs has many applications including cybersecurity, crime detection, social marketing and public health. This project aims to investigate directionality-aware search of cohesive subgraphs over directed graphs by designing effective models and developing efficient and scalable algorithms. This project expects to address key challenges and lay scientific foundations for searching big directed graphs. The expected outcomes include novel models, computing paradigms, algorithms, indexing techniques, and distributed solutions. The success of the project will not only provide technological breakthroughs but also benefit the development of key industries in AustraliaRead moreRead less
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
Efficient and Scalable Subgraph Search from Big Graphs in Cloud. This project seeks to develop knowledge that will form the foundation for next-generation graph search engines of structural knowledge. Subgraph search is in high demand for many applications that deal with big graphs, such as social network marketing, crime detection and sales pattern discovery. However, there are many challenges when the search needs to be conducted in cloud environments. This project aims to develop efficient an ....Efficient and Scalable Subgraph Search from Big Graphs in Cloud. This project seeks to develop knowledge that will form the foundation for next-generation graph search engines of structural knowledge. Subgraph search is in high demand for many applications that deal with big graphs, such as social network marketing, crime detection and sales pattern discovery. However, there are many challenges when the search needs to be conducted in cloud environments. This project aims to develop efficient and scalable query processing algorithms to search subgraphs from a big graph stored in a cluster of machines in the cloud. The expected outcomes may lay the theoretical foundations for subgraph search in the cloud, and establish a unified graph search engine that integrates various query semantics for use in many applications.Read moreRead less
Continuous Loyalty-based Similarity Queries over Moving Objects. Efficient moving object data processing is highly demanded in many key real applications. This project aims to develop, analyse, implement and evaluate novel techniques to effectively and efficiently monitor moving objects in real time based on a novel query model, loyalty based model. Anticipated outcomes include new indexing, query processing, and approximation techniques, as well as a set of novel theorems. The project is expect ....Continuous Loyalty-based Similarity Queries over Moving Objects. Efficient moving object data processing is highly demanded in many key real applications. This project aims to develop, analyse, implement and evaluate novel techniques to effectively and efficiently monitor moving objects in real time based on a novel query model, loyalty based model. Anticipated outcomes include new indexing, query processing, and approximation techniques, as well as a set of novel theorems. The project is expected to significantly contribute to the technology development of big data regarding streaming and spatial-data processing techniques.Read moreRead less
Efficiently Processing Pattern-based Structure Queries over Large Graphs. Advances in electronic data collections are leading to an exciting new research area - Big Data. Driven by a number of key applications, this project aims at a major field in Big Data: pattern-based structure matching. The problems involved are computationally hard (NP-Complete or NP-Hard). The investigation aims to cover the three key components: fundamentals, indexing, and query processing. The anticipated outcome includ ....Efficiently Processing Pattern-based Structure Queries over Large Graphs. Advances in electronic data collections are leading to an exciting new research area - Big Data. Driven by a number of key applications, this project aims at a major field in Big Data: pattern-based structure matching. The problems involved are computationally hard (NP-Complete or NP-Hard). The investigation aims to cover the three key components: fundamentals, indexing, and query processing. The anticipated outcome includes a set of new theorems and novel data processing techniques. If successful the project is expected to significantly contribute to technology development and the scientific foundations of Big Data analysis.Read moreRead less