Multicore Dynamics are committed to review the performance and functionality of existing modules as well the development of new. Modules in the pipeline for future development include –
Machine Learning can be viewed as a form of artificial intelligence and is based around the process of studying and learning from the underlying data in order to make sense of. Machine Learning is significant when dealing with large volumes of complex data as it can be trained to build predictive models and discover patterns within the data.
Graph Theory & Persistent Homology
Graph Theory remains one of the fastest growing areas in modern mathematics which in part, can be attributed to its relevance and flexibility when applied to data analysis.
Persistent Homology provides extra dimensions to the topology results as it can differentiate between what is a true signal and what is not. Its power stems from its ability to show results over a user defined period of time / parameter range rather than a restricted single ‘snapshot’ approach which can change over time thereby providing misleading results.
Arion will employ the power of graph theory and persistent homology to facilitate the visual representation of analysed data thereby providing researchers with the ability to make immediate visual connections and discover previously unobserved relationships within their data.