Month: January 2019

The case for network-accelerated query processing

~ Adrian Colyer ~ 2 Comments
The case for network-accelerated query processing Lerner et al., CIDR'19 Datastores continue to advance on a number of fronts. Some of those that come to mind are adapting to faster networks (e.g. ‘FARM: Fast Remote Memory’) and persistent memory (see e.g. ‘Let’s talk about storage and recovery methods for non-volatile memory database systems’), deeply integrating ... Continue Reading

The data calculator: data structure design and cost synthesis from first principles and learned cost models

~ Adrian Colyer ~ 10 Comments
The Data Calculator: data structure design and cost synthesis from first principles and learned cost models Idreos et al., SIGMOD'18 This paper preceded the work on data continuums that we looked at last time, and takes a more general look at interactive and semi-automated design of data structures. A data structure here is defined as ... Continue Reading

Design continuums and the path toward self-designing key-value stores that know and learn

~ Adrian Colyer ~ 3 Comments
Design continuums and the path toward self-designing key-value stores that know and learn Idreos et al., CIDR'19 We’ve seen systems that help to select the best data structure from a pre-defined set of choices (e.g. ‘Darwinian data structure selection’), systems that synthesise data structure implementations given an abstract specification (‘Generalized data structure synthesis’), systems that ... Continue Reading

Towards a hands-free query optimizer through deep learning

~ Adrian Colyer ~ 3 Comments
Towards a hands-free query optimizer through deep learning Marcus & Papaemmanouil, CIDR'19 Where the SageDB paper stopped— at the exploration of learned models to assist in query optimisation— today’s paper choice picks up, looking exclusively at the potential to apply learning (in this case deep reinforcement learning) to build a better optimiser. Why reinforcement learning? ... Continue Reading

Unsupervised learning of artistic styles with archetypal style analysis

~ Adrian Colyer ~ 2 Comments
Unsupervised learning of artistic styles with archetypal style analysis Wynen et al., NeurIPS'18 I’ve always enjoyed following work on artistic style transfer. The visual nature makes it easy to gain an appreciation for what is going on and the results are very impressive. It also something that’s been unfolding within the timespan of The Morning ... Continue Reading