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Month: October 2017

Poisson Processes and Data Loss

Poisson Processes and Data Loss

There are many applications for counting arrivals over time. Perhaps I want to count the arrivals into a store, or shipments into a postal distribution center, or node failures in a cloud cluster, or hard drive failures in a traditional storage array. It's rare that these events come neatly, one after the other, with a…

Commentary: Technical Debt in Machine Learning

Commentary: Technical Debt in Machine Learning

I recently had the opportunity to be a guest on an episode of the On-Premise IT Roundtable podcast, the topic of which was technical debt. (You can listen to the twenty minute episode here, or watch the video version here.) The conventional definition of technical debt, for both consumer and enterprise technology, is the lagging of upgrades,…

Commentary: On Straight As and Salaries

Commentary: On Straight As and Salaries

(Fair warning: this is a personal account.) The systems were designed well, I think. When we were in school or college, passing was supposed to mean you knew the material, basically. A B showed you were pretty good, and an A was only for the smartest students. Not relatively the smartest, but objectively the smartest.…

Commentary: High Level Data Filtration

Commentary: High Level Data Filtration

The consensus over the last five or so years has converged on a conclusion regarding data: we're drowning in it. We have more than we can possibly monitor with our own eyeballs, and certainly more than we know what to do with intelligently. The motto for data scientists has been "More is better." Well, ask…

On Server Efficiency

On Server Efficiency

For the full text of the paper, including all proofs and supplementary lemmata, click to download  Abstract Editor's note: This paper comprises the second chapter of the PhD dissertation by Rachel Traylor. Cha and Lee defined a mathematical notion of server performance by measuring efficiency defined as the long run average number of jobs completed per…