Connecting Data, People and Ideas since 2016.
25 April 2018

Connected Data London as a Graph

The visual graph of CDL18, plus new speakers from JP Morgan and Expero.


In CDL we like to practice what we preach. We decided to give you a visual overview of our themes and lineup so far, and what better way to do this than as a graph. If you’re visual types like we are, this should help keep track of everything we got going for Connected Data London this year.



Connected Data London as a graph

As we get closer to the event in November, we will update our graph with the program as it’s being shaped. In fact, we already have two new speakers confirmed: Paraskevi Zerva from JP Morgan, and Steve Purves from Expero.


Paraskevi Zerva is an Information Architect at JP Morgan Chase & Co Corporate Technology in Glasgow and has over eight years of work and research experience in ontology modelling and formal languages for data and web/cloud-based services, Semantic Web technologies, and AI using rule-based reasoning and ontology inferencing.


Paraskevi has worked on various projects in JP Morgan Chase focusing on metadata management and metadata/data governance using semantic and linked-data technologies. She has focused on various aspects related to data governance such as Data Lineage/Provenance and Data Quality using semantic metadata, knowledge engineering and ontology data modelling languages and techniques.


Steve is a Technical Lead in Expero, and describes himself as an engineer first and foremost. He is comfortable working full-stack, cross-platform in a range of languages and is happiest when there is some mathematical or scientific analysis sprinkled in. He graduated in electrical engineering specializing in signal and image processing, which he took into the scientific computing field in the Oil and Gas industry.


Steve has used graph representations and graph analytics to enable organisations to understand their data in new and powerful ways. Many datasets are naturally graphs, and some benefit from being treated as a graph. There is significant potential then for graph analytics and machine learning to be used together, and Steve will expand on this in his talk.

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