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Opensource Graph Technologies: Applications Built With Graphs

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Opensource Graph Technologies: Applications Built With Graphs

Understand the recent advancements in the Graph technologies from the slides presented at the Opensource Graph Technologies Meetup.

· Big Data Zone ·
Free Resource

We recently held our second Opensource Graph Technologies Meetup at Google’s Campus London. This time, the session’s focus was on different applications that can be built on top of graph technologies and frameworks. We were lucky enough to have our largest turnout yet. Many thanks to everyone who came to support the event!

For those who missed the meetup, here’s a brief recap of the talks.

Spinner: Scalable Graph Partitioning in the Cloud

Claudio Martella of Google came in to chat to us about how the Spinner algorithm can be used to scale graph partitioning on large-scale graph computations. Large social networks like Twitter and Facebook have to routinely analyze large graphs and these graphs are often distributed across multiple servers. In his talk, Claudio outlined how Spinner deals with the dynamicity and scale of such graphs when partitioning the graph computations in the cloud.

The slides can be downloaded from here.

GRAKN.AI: Creating Biomedical and Financial Data Knowledge Graphs

Haikal models the GRAKN.AI hoodie.

Our own Haikal Pribadi spoke about two prototype knowledge graphs that we have created: one in the biomedical space and the other in managing financial and other data in the oil and gas investments space. The key use case highlighted how information assets stored in different data silos can be easily integrated into one knowledge graph using GRAKN.AI. The resultant knowledge graph makes the body of data assets accessible and easy to query.

The slides can be downloaded from here.

Fighting Financial Crime With Connected Data

James Phare from Data to Value, a London-based data consultancy and Neo4j Solution Partner focused on the financial sector, describing how using graph databases have helped them find suspicious patterns in trade finance transactions. It helped the bank identify suspicious patterns to stem the flow of payments for illicit goods.

The slides can be downloaded from here.

graphs ,grakn.ai ,data analytics ,big data

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