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Interesting paper but the repo (https://github.com/ground-context/ground) seems abandoned. Have seen companies losing out on a lot of value they can extract from their data because metadata context (discoverability, lineage and semantics) is the last thing on plate or not considered important across the org.

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The primary author is Joseph Hellerstein, head of the Berkeley RISELab (the new Spark/AMPLap). Their primary project is called Fluent (which until recently was called Anna [4])...it's the all encompassing name changes that get ya! ;-)

Similarly, see Matei Zaharia's (the original Spark lead) and his team at MIT (he's now at Stanford) SIGMOD 2018 paper called MISTIQUE [4]...

[1] Joe Hellerstein https://github.com/jhellerstein

[2] Berkeley RISELab https://rise.cs.berkeley.edu https://github.com/ucbrise

[3] Fluent Compute Platform https://github.com/fluent-project/fluent

[3] Anna: A Crazy Fast, Super-Scalable, Flexibly Consistent KVS https://databeta.wordpress.com/2018/03/09/anna-kvs/ Discussion: https://news.ycombinator.com/item?id=16551072

[4] MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis https://cs.stanford.edu/~matei/papers/2018/sigmod_mistique.p...

Or that it's so hard to do well enough to be useful, that hardly anyone can, except possibly Google, as a special case.

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