Operating a distributed database on a single Kubernetes cluster is interesting, but how about transparently migrating it from one cluster to anotherвЂ“potentially between different cloud providersвЂ“ without impacting user workloads? Kubernetes has become the de facto default deployment for ArangoDB, a distributed Graph database. The talk describes these differences and ways to address them in a systematic way.

They use different forms of inference, and they deal with imprecision in different ways. They use dissimilar graph structures and vocabularies. They have different key performance metrics. The knowledge graph and machine learning communities use different approaches to data transformation and problem solving.

By analogy, the ideal of the knowledge graph is the perfect crystalline structure of a diamond representing everything that is known about a domain in a logical way, whereas machine learning values flexibility of models trained on a subset of the data that can stretch like rubber to accommodate data never encountered before.

Knowledge graphs and graph machine learning seem like a perfect match, though in practice there are subtle differences between the two domains that can cause friction. This talk describes a story of lessons learned in a journey that started with the objective of developing an application that required integration of a knowledge graph with multiple machine learning models, which rapidly encountered the hard reality of impedance mismatches between the technologies, and how these differences were addressed using semantic models.

Nosql arangodb series iris