Pioneering the next generation of complex, real time, borderless integration of knowledge in support of science and society

Abstract:

The pace, with which government agencies, researchers, industry and the public need to react to national and international challenges of economic, environmental and social natures, is constantly changing and rapidly increasing. Responses to the 2020 Australian bushfire crisis and the global COVID-19 pandemic events are recent examples of these requirements.

Decisions are no longer made on information or data coming from a single source or discipline or a solitary aspect of life: the issues of today are too complex. Solving complex issues requires seamless integration of data across multiple scientific domains and also demands codesign from project initiation with local experts, citizens and indigenous populations with potential impacts on businesses, the economy and the environment also being considered.

Modern technologies, easy access to information on the web, abundance of openly available data shifts are not enough to overcome previous limitations of dealing with data and information. Data and software have to be FAIR, processes need to be trusted and verifiable, decisions to be rapid and supported by facts coming from multiple data sources and disciplines. We need to re-think the approaches toward data integration, analysis, evaluation and access and to start building flexible re-usable and re-purposeful data and information solutions serving multiple domains and communities.

This presentation will discuss the challengers of modern knowledge integration from multiple disturbed sources, sectors and disciplines and will present current trends in resolving those challengers such as:

• new methodologies for big and small data integration focused on actual location
• linking transdisciplinary data through knowledge graphs
• modernising metadata approaches for enabling data discovery and access
• enabling machine-to-machine data exchange and assessment
• employing new techniques for understanding multi-purpose fitness of data