Great taxonomy breakdown. The low temporal locality part of the design space is particularly interesting. It is extremely general but for many data models "low

Great taxonomy breakdown. The low temporal locality part of the design space is particularly interesting. It is extremely general but for many data models "low temporal locality" can mean highly variable windows on a per entity basis and not on the entire collection, which is a challenging criteria for the way most systems are designed. Physical world sensor observation data models are a good example of this. Existing solutions for low temporal locality data models scale quite poorly in practice, even on systems nominally designed for them. At the limit, low temporal locality platforms converge on the same unusual design requirements as symmetric mixed-workload database kernels. The latter is something we really don't (know how to) build, since it has some interesting computer science constraints. It is a missing piece of technology to make this stuff really work well at scale.