Write-limited sorts and joins for persistent memory Viglas, VLDB 2014 This is the second of the two research-for-practice papers for this week. Once more the topic is how database storage algorithms can be optimised for NVM, this time examining the asymmetry between reads and writes on NVM. This is premised on Viglas’ assertion that: Writes … Continue reading Write-limited sorts and joins for persistent memory
Month: September 2016
Let’s talk about storage and recovery methods for non-volatile memory database systems
Let's talk about storage and recovery methods for non-volatile memory database systems Arulraj et al., SIGMOD 2015 Update: fixed a bunch of broken links. I can't believe I only just found out about this paper! It's exactly what I've been looking for in terms of an analysis of the impacts of NVM on data storage … Continue reading Let’s talk about storage and recovery methods for non-volatile memory database systems
Distributed consensus and the implications of NVM on database management systems
Distributed consensus and the implications of NVM on database management systems Fournier, Arulraj, & Pavlo ACM Queue Vol 14, issue 3 As you may recall, Peter Bailis and ACM Queue have started a "Research for Practice" series introducing "expert curated guides to the best of CS research." Aka, reading lists for The Morning Paper! I … Continue reading Distributed consensus and the implications of NVM on database management systems
Flexible Paxos: Quorum intersection revisited
Flexible Paxos: Quorum intersection revisited Howard et al., 2016 Paxos has been around for 18 (26) years now, and extensively studied. (For some background, see the 2 week mini-series on consensus that I put together last year). In this paper, Howard et al. make a simple(?) observation that has significant consequences for improving the fault-tolerance … Continue reading Flexible Paxos: Quorum intersection revisited
Time evolving graph processing at scale
Time evolving graph processing at scale Iyer et al., GRADES 2016 Here's a new (June 2016) paper from the distinguished AMPlab group at Berkeley that really gave me cause to reflect. The work addresses the problem of performing graph computations on graphs that are constantly changing (because updates flow in, such as a new follower … Continue reading Time evolving graph processing at scale
Texture networks: feed-forward synthesis of textures and stylized images
Texture Networks: Feed-forward synthesis of textures and stylized images Ulyanov et al., arXiv, March 2016 During the summer break I mostly stayed away from news feeds and twitter, which induces terrible FOMO (Fear Of Missing Out) to start with. What great research was published / discussed that I missed? Was there a major industry announcement … Continue reading Texture networks: feed-forward synthesis of textures and stylized images
Why should I trust you? Explaining the predictions of any classifier
“Why Should I Trust You? Explaining the Predictions of Any Classifier Ribeiro et al., KDD 2016 You’ve trained a classifier and it’s performing well on the validation set - but does the model exhibit sound judgement or is it making decisions based on spurious criteria? Can we trust the model in the real world? And … Continue reading Why should I trust you? Explaining the predictions of any classifier
The Morning Paper on Operability
I gave a 30 minute talk at the Operability.io conference yesterday on the topic of “The Morning Paper meets operability.” In a first for me, I initially prepared the talk as a long blog post, and then created a set of supporting slides at the end. Today’s post is the text of that talk - … Continue reading The Morning Paper on Operability
Mastering the game of Go with deep neural networks and tree search
Mastering the Game of Go with Deep Neural Networks and Tree Search Silver, Huang et al., Nature vol 529, 2016 Pretty much everyone has heard about AlphaGo’s tremendous Go playing success beating the European champion by 5 games to 0. In all the excitement at the time, less was written about how AlphaGo actually worked … Continue reading Mastering the game of Go with deep neural networks and tree search
Deep neural networks for YouTube recommendations
Deep Neural Networks for YouTube Recommendations Covington et al, RecSys '16 The lovely people at InfoQ have been very kind to The Morning Paper, producing beautiful looking "Quarterly Editions." Today's paper choice was first highlighted to me by InfoQ's very own Charles Humble. In it, Google describe how they overhauled the YouTube recommendation system using … Continue reading Deep neural networks for YouTube recommendations
the morning paper 