Managing uncertainty in RFID traceability networks. Australia suffers 5.4 million cases of food-borne illness every year, which leads to 2.1 million days of lost work, 1.2 million people visiting a doctor, and 120 deaths annually. This has revealed the urgent need for improved ways of locating and recalling problematic products that have been released into the community. The project will develop novel techniques driven by Radio Frequency Identification (RFID) technology for improving the effici ....Managing uncertainty in RFID traceability networks. Australia suffers 5.4 million cases of food-borne illness every year, which leads to 2.1 million days of lost work, 1.2 million people visiting a doctor, and 120 deaths annually. This has revealed the urgent need for improved ways of locating and recalling problematic products that have been released into the community. The project will develop novel techniques driven by Radio Frequency Identification (RFID) technology for improving the efficiency and accuracy of product tracking in distribution networks. This project will place Australia at the forefront of RFID research. It will also be an excellent vehicle for educating young researchers and engineers in Australia.Read moreRead less
Managing private location data in a mobile and networked world: getting the balance right. Location based data are transforming the mobile service industry and this project will develop novel approaches to safeguard the location privacy of mobile individuals. This will facilitate the development of privacy-aware services which can be used for real time traffic monitoring, care for the elderly and smartphone enabled location services.
Managing data with high redundancy and low value density. This project aims to develop a database for data storage, cleaning, compression, hierarchal summarisation, indexing and query processing for machination data.Database management systems are needed to support stream query processing and manage historical data to support complex data analytics, data mining and data-driven decision making. Machination data, often found in sensor networks, GPS and RFID applications, vehicle on-board devices a ....Managing data with high redundancy and low value density. This project aims to develop a database for data storage, cleaning, compression, hierarchal summarisation, indexing and query processing for machination data.Database management systems are needed to support stream query processing and manage historical data to support complex data analytics, data mining and data-driven decision making. Machination data, often found in sensor networks, GPS and RFID applications, vehicle on-board devices and medical monitoring devices are difficult to manage and process because of large volumes and streaming, high redundancy and low value density. This project is expected to stream machination data management to support scalable query processing and data analytics.Read moreRead less
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
View-based processing of pattern matching queries in large graphs. Graph data exist ubiquitously in modern information systems. Graph pattern matching (GPM) finds parts of the data graph that match a given pattern. It has applications in many areas including knowledge discovery, public health, and crime detection. This project will develop novel techniques for the efficient processing of GPM queries in large graphs.
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