Efficient processing of large scale multi-dimensional graphs. This project aims to develop novel approaches to process large scale multi-dimensional graphs. The project will focus on the three most representative types of problems against multi-dimensional graphs, namely cohesive subgraph computation, frequent subgraph mining, and subgraph matching. The project outcome will include a set of new theories, novel indexing and data processing techniques, including distributed and single node computa ....Efficient processing of large scale multi-dimensional graphs. This project aims to develop novel approaches to process large scale multi-dimensional graphs. The project will focus on the three most representative types of problems against multi-dimensional graphs, namely cohesive subgraph computation, frequent subgraph mining, and subgraph matching. The project outcome will include a set of new theories, novel indexing and data processing techniques, including distributed and single node computation. The success of the project will significantly contribute to the technology development and the scientific foundation of big graph processing.Read moreRead less
Towards efficient processing of big graphs. This project aims to develop theory and techniques for efficient and scalable processing of Big Graph, a major field in Big Data. The project will focus on primitive graph queries covering many applications. Anticipated outcomes include a set of theories, indexing and data processing (including distributed and approximate) techniques. The success of the project is expected to contribute to the technology development and the scientific foundation of Big ....Towards efficient processing of big graphs. This project aims to develop theory and techniques for efficient and scalable processing of Big Graph, a major field in Big Data. The project will focus on primitive graph queries covering many applications. Anticipated outcomes include a set of theories, indexing and data processing (including distributed and approximate) techniques. The success of the project is expected to contribute to the technology development and the scientific foundation of Big Graph processing.Read moreRead less
Structure Search Over Large Scale Heterogeneous Information Networks . Structure search on heterogeneous information networks (HINs) has many applications including cybersecurity, crime detection, social media, marketing recommendation, and public health. The project aims to develop novel techniques for efficiently conducting structure search on large scale HINs and lay the scientific foundations. The anticipated outcomes include novel computing paradigms, algorithms, indexing, incremental compu ....Structure Search Over Large Scale Heterogeneous Information Networks . Structure search on heterogeneous information networks (HINs) has many applications including cybersecurity, crime detection, social media, marketing recommendation, and public health. The project aims to develop novel techniques for efficiently conducting structure search on large scale HINs and lay the scientific foundations. The anticipated outcomes include novel computing paradigms, algorithms, indexing, incremental computation, and distributed solutions. The success of the project will directly contribute to the scientific foundation of Big Data computation. It will also contribute to the development of local industry involving cybersecurity, social media-based recommendation, network management, knowledge graphs, and E-business. Read moreRead less
Cohesive Subgraph Discovery on Big Bipartite Graphs. This project aims to develop novel technology for efficient and scalable cohesive subgraph discovery on big bipartite graphs, including new theories, indexing techniques, and data processing algorithms. We anticipate addressing key challenges and laying scientific foundations of big graph computation, as well as delivering high-impact technologies. The success of the project will directly benefit the key applications in Australia such as cyber ....Cohesive Subgraph Discovery on Big Bipartite Graphs. This project aims to develop novel technology for efficient and scalable cohesive subgraph discovery on big bipartite graphs, including new theories, indexing techniques, and data processing algorithms. We anticipate addressing key challenges and laying scientific foundations of big graph computation, as well as delivering high-impact technologies. The success of the project will directly benefit the key applications in Australia such as cyber-security, health, bio-informatics, social networks, and E-commerce. The success of the project will also facilitate the training of PhD graduates and postdoctoral research associates in the area of Big Data.
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Discovery Early Career Researcher Award - Grant ID: DE130101002
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Efficiently querying uncertain spatial space. Location-based services are becoming increasingly popular due to exponentially increased usage of smartphones and cheap wireless network. This project aims to provide efficient solutions for various location-based queries applicable to different travelling domains such as road networks, Euclidean space with obstacles and indoor space.
Next-generation search on social networks. This project aims to design effective and intelligent search techniques for large scale social network data. The project expects to advance existing social network search systems in utilizing the geographical locations of queries and social network data to provide more relevant results, acknowledging and handling inherent uncertainties in the data, and exploiting knowledge graphs to produce intelligent search results. Expected outcomes of this project i ....Next-generation search on social networks. This project aims to design effective and intelligent search techniques for large scale social network data. The project expects to advance existing social network search systems in utilizing the geographical locations of queries and social network data to provide more relevant results, acknowledging and handling inherent uncertainties in the data, and exploiting knowledge graphs to produce intelligent search results. Expected outcomes of this project include a next-generation social network search system. The success of this project will support and enhance a wide range of applications such as law enforcement, health, national security, marketing, and advertisement.Read moreRead less
Efficient processing of distance-based spatial queries on multi-valued objects. This project aims to develop effective and efficient algorithms to analyse large scale multi-valued objects. The success of this project will not only be an important complement to the current spatial database systems but also bring considerable economic and social benefits to Australia.
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.