Building crowd sourced data curation processes. This project aims to tackle the growing problem of data curation. The capacity to effectively utilise the increasing number of datasets available to organisations for timely decision making is diminishing, due to onerous data preparation and curation tasks that have to be performed before the data can be consumed by analytics platforms. The project will be a first attempt at using a novel process-oriented approach in micro-task crowdsourcing, and w ....Building crowd sourced data curation processes. This project aims to tackle the growing problem of data curation. The capacity to effectively utilise the increasing number of datasets available to organisations for timely decision making is diminishing, due to onerous data preparation and curation tasks that have to be performed before the data can be consumed by analytics platforms. The project will be a first attempt at using a novel process-oriented approach in micro-task crowdsourcing, and will create new knowledge to harness the full potential of crowd sourced data curation. This is expected to make a significant benefit towards enhanced organisational capacity to accelerate the time-to-value from data analytics projects.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101610
Funder
Australian Research Council
Funding Amount
$403,398.00
Summary
Towards Explainable Multi-source Multivariate Time-series Analysis. The aim of this project is to build deep learning models with transparent reasoning behind the results that can be easily interpreted by humans. The research rests on translating pertinent knowledge from multiple sources of complex data containing event sequences into graph form and embedding those knowledge graphs into a sophisticated deep learning model. Such an accomplishment represents the next great advance in machine intel ....Towards Explainable Multi-source Multivariate Time-series Analysis. The aim of this project is to build deep learning models with transparent reasoning behind the results that can be easily interpreted by humans. The research rests on translating pertinent knowledge from multiple sources of complex data containing event sequences into graph form and embedding those knowledge graphs into a sophisticated deep learning model. Such an accomplishment represents the next great advance in machine intelligence and will lay the theoretical foundations for building intelligent analysis tools that truly work in tandem with people. The potential benefits to science, society, and the Australian economy, particularly in finance, sensor technologies, and emergency health services would be appreciable.Read moreRead less