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Knowledge graphs are the basis for data integration, power search engines and are the best fit to train AI models upon. They are designed to be published on the web (public or private), so consumers can easily access and query these knowledge graphs. Knowledge graphs are typically constructed from various data sources spread across organisations, departments, governments, the web…

What happens when the Linked Data Event Streams (LDES) keeps increasing in volume? Or, what if I want to handle fast data? Or, what if my data is increasingly popular and it needs to handle more subscribers? Or, what if I want to handle an increasing amount of LDESs as a consumer?

Linked Data Event Streams (LDES) is a relatively new protocol designed for exchanging interoperable datasets within and across dataspaces. A dataspace is an ecosystem of actors—such as providers, consumers, and intermediary participants—that facilitates data flows to achieve specific goals.

A Linked Data Event Stream (LDES) is a collection of immutable events, representing changes that occur in a certain dataset/data collection. An event, also referred to as a member, is represented as a group of triples within the Representation Description Framework (RDF). Each one has a URI as a subject, which makes sure it is unique.