Towards knowledge discovery from imperfect and evolving data

从数据中提取信息对于分析和保护消费者数据都至关重要。然而，许多学习技术都是使用完美的静态数据集开发的，这与凌乱的、不断变化的现实世界数据截然不同。该项目旨在开发数据分析技术，能够从随时间变化的不完美/不完整数据中提取复杂结构中的准确信息。预期结果是一个原型工具，在真实数据集上进行了测试，它结合了数据建模、算法开发和系统设计方面的新技术。可能的好处是，通过学生培训和与网络安全、医疗保健和国防等关键行业相关的新的、强大的数据工具，增强澳大利亚在数据科学方面的能力。

Information extraction from data is critical, both to analyse and protect consumer data. However, many learning techniques are developed using perfect, static datasets, quite different to messy, ever-changing real-world data. This project aims to develop data analytics techniques that can extract accurate information in complex structures from imperfect/incomplete data that changes over time. Expected outcomes are a prototype tool, tested on real datasets, that combines new techniques in data modelling, algorithm development, and system design. Likely benefits are enhanced Australia's competence in data science through student training and new, robust data tools relevant to critical sectors such as cybersecurity, healthcare, and defence.