The author walks through how to build a metrics system for a high performance data platform, taking a look at some of the important factors to consider when choosing what open source offerings to use.
Distributed transactions suffer from poor performance due to two major limiting factors. First, distributed transactions suffer from high latency because each of their accesses to remote data incurs a long network delay. Second, this high latency increases the likelihood of contention among distributed transactions, leading to high abort rates and low performance. The authors present Sundial, an in-memory distributed optimistic concurrency control protocol that addresses these two limitations.
MiSoSouP is a suite of algorithms for extracting high-quality approximations of the most interesting subgroups, according to different interestingness measures, from a random sample of a transactional dataset.
In the machine learning research community, it is generally believed that there is a tension between memorization and generalization. This paper examines the extent to which this tension exists, by exploring whether it is possible to generalize by memorizing alone.
The authors survey and discuss methods proposed in the literature for estimating the Sharpe ratio; computing confidence intervals around a point estimation of the Sharpe ratio; and performing hypothesis testing on a single Sharpe ratio and on the difference between two Sharpe ratios.
The authors provide an overview of the Two Sigma Factor Lens, designed for analyzing multi-asset portfolios and derived from returns of broad, liquid asset class proxy indexes.
The NERF and Heads projects bring Linux back to the cloud servers' boot ROMs by replacing nearly all of the vendor firmware with a reproducible built Linux runtime that acts as a fast, flexible, and measured boot loader.
Architecture overview for the future of the Python Pandas data analytics library.
An overview of BeakerX, a collection of kernels and extensions to the Jupyter interactive computing platform.