7 – Modelling Physical Systems Using Graphs

20 Jul, 2021

Never has understanding the environment been more crucial, especially when it comes to natural resources and energy. Challenge: environmental data is fragmented across different systems and formats. Proposition: Bring disparate data together into a spatial knowledge graph ready for extensions. Mike Morley Director AI/ML Technology, Arcurve Mike has delivered innovation and insight through implementing leading-edge technology in the mining, environmental, AEC and engineering market sectors. For more than 25 years, Mike has helped organizations realize the value of their data as both an entrepreneur and in strategic leadership roles. He began working with Neo4j in the context of developing environmental knowledge graphs as the Dir. of Knowledge and Technology at Matrix Solutions in 2012 to addressing the complex and growing challenges associated with effectively deriving insight from environmental data management. Traditional relational models had proven to be limited. Peter Tunkis Data Scientist, Arcurve Pete Tunkis is a Data Scientist with the Arcurve Advanced Analytics team. He has over a decade of experience in data-driven research, analytics, and project management. His strengths lie in combining deep knowledge of research best practices with industry application, adapting to changing scenarios and quickly learning and understanding new skills and complex systems. Pete earned a Ph.D. in Political Science from The Ohio State University with a focus on behavioural analytics, and he brings with him a strong understanding in both statistical/machine learning approaches and qualitative research methodology. Before joining the Arcurve family, Pete worked in government, academia, banking, and insurance in Europe and the United States. Otherwise, he enjoys yelling at the TV when the Calgary Flames are losing and is always up for a game of darts.

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