Solutions for addressing complex knowledge problems
SURROUND’s solutions combine semantic data enrichment and machine learning (ML), applying open standards.
Our vision is to solve the world’s most complex problems ethically, to achieve real-world outcomes for people and society.
Our mission is to achieve this through sense-making by linking knowledge across policy, planning, and information holdings to improve organisational performance.
Other foundations that support SURROUND’s technology offerings are
Comparable eXplainable Reasoning (CXR) provides the ability to use different types of reasoning styles which can be cross-compared to optimise the results produced. CXR uses contextual metadata and data from either SOP or across the data fabric to provide knowledge insights. In addition, CXR enables the reshaping of metadata and data within SOP. Ensembles of algorithms are employed to analyse and parse information in these components. These components can also be employed to parse and transform semantic information from SOP to meet a particular information consumption requirement.
Discrete Global Grid System (DGGS) Converter (DGGSC) the DGGS provides a canonical view of geospatial data as a set of region points. The DGGSC transforms latitude and longitude into two-dimensional region points. It also converts latitude, longitude, and elevation into three-dimensional region points. The DGGSC can also perform these transformations in the opposite direction. When combined with the dimension of time in the SOP, the result enables the ability to reason over the rate of change over space and time. This capability is very useful in predicting future scenarios such as the impacts of natural disasters, and concepts such as long-term unemployed people or community health.