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The convergence of 3 big ideas in graph computing:

1. D4M: Dynamic Distributed Dimensional Data Model

http://www.mit.edu/~kepner/D4M/ GraphBLAS: http://graphblas.org

Achieving 100M database inserts per second using Apache Accumulo and D4M https://news.ycombinator.com/item?id=13465141

MIT D4M: Signal Processing on Databases [video] https://www.youtube.com/playlist?list=PLUl4u3cNGP62DPmPLrVyY...

2. Topological / Metric Space Model

Fast and Scalable Analysis of Massive Social Graphs http://www.cs.ucsb.edu/~ravenben/temp/rigel.pdf

Quantum Processes in Graph Computing - Marko Rodriguez [video] https://www.youtube.com/watch?v=qRoAInXxgtc

3. Propagator Model

Revised Report on the Propagator Model https://groups.csail.mit.edu/mac/users/gjs/propagators/

Constraints and Hallucinations: Filling in the Details - Gerry Sussman [video] https://www.youtube.com/watch?v=mwxknB4SgvM

We Really Don't Know How to Compute - Gerry Sussman [video] https://www.youtube.com/watch?v=O3tVctB_VSU

Propagators - Edward Kmett - Boston Haskell [video] https://www.youtube.com/watch?v=DyPzPeOPgUE

So many good links here. Most interested in Dynamic Distributed Dimensional Data Model.

What are you working on?

PUFR http://pufr.io (IoT security startup), and for the last few years I've been doing R&D on the design of a graph computing model that unifies some of the ideas above.

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