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aghillo

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I like this. Previously I’ve used notebooks to explain a data pipeline from different perspectives. One notebook showing the ETL process going from raw data to RDF triples in a store; one SPARQL notebook showing the raw queries; and then a final decision support notebook using a Python binding to the underlying SPARQL query library. It seemed to work well.

Is there any accessible work using Category Theory that looks at API composition? I’m thinking here about data intensive systems that are often composed of different elements (e.g. Kafka, Cassandra, bespoke microservices, ...) and are glued together in an application. Could Category Theory help in getting a grip on this complexity?

Can we ever imagine dna sequencing been done like a remoter sensor? E.g. automated sampling for e(environmental) dna,in-situ sequencing (single and multi species targeting), results transmitted back wirelessly. A biodiversity IoT system?

It's specifically focused on the domain of Art and argues that Art is essentially a cooperative networked activity - a network of producers, suppliers, distributers, influencers etc who all contribute to a final piece of Art.

Has anyone characterised server less architectures? I'm interested in testing / exploring the limits with some predictive modelling work and implementing (mathematical) functions as FAAS? No doubt, it 'depends' (e.g. nature of data and its locality or otherwise) but interested to hear if anyone is looking at very fine-granularity server less systems?