[Links of the Day] 02/07/2020 : Database query optimization, Deep Learning Anomaly detection survey, Large scale packet capture system

• event-reduce : accelerate query result after write. Basically if cache part of the write and recalculate the new query result using past query result and the recent write event. The authors observe an up to 12 times faster displaying of new query results after a write occurred.
• Deep Learning for Anomaly Detection: A Survey : comprehensive survey of anomaly detection techniques out there. 
• Moloch : Large scale, open-source, indexed packet capture and search.

[Links of the Day] 09/06/2020 : #WASM on #K8S , Fast Anomaly Detection on Graphs, Linux one ring

• Krustlet : it seems that web assembly is getting more pervasive, we have kernel WASM, WASM for Deep learning, now Krustlet offer WASM for Kubernetes via Kubelet.
• Fast Anomaly Detection in Graphs :  Really cool real-time anomaly detection on dynamic graphs.  the authors claim to be 644 times faster than SOTA with 42-48% higher accuracy. What is even more attractive is the constant memory usage which is fantastic for production deployment. [github] 
• io_uring : this will dominate the future of the Linux interface. It is currently eating up every single IO interface and probably won't stop just there. 

[Links of the Day] 28/05/2020 : Reverse Oauth proxy , Prometheus timeseries backend, Google use machine learning to improve audio chat

• Oauth Proxy : A reverse proxy that provides authentication with Google, Github or other providers.
• Zebrium : Prometheus backend project, not sure why you don't want to just export the data from Prometheus into a distributed column-store data warehouse like Clickhouse, MemSQL, Vertica. This gives you fast SQL analysis across massive datasets, real-time updates regardless of order, and unlimited metadata, cardinality and overall flexibility. Maybe because they want to focus on the monitoring / reactive aspect and less on the analytics.
• Improving Audio Quality with WaveNetEQ : Google uses machine learning to deal with packet loss, jitter, and delays. An interesting bit of info: " 99% of Google Duo calls need to deal with packet losses, excessive jitter or network delays. Of those calls, 20% lose more than 3% of the total audio duration due to network issues, and 10% of calls lose more than 8%."

Links of the day 20 - 05 - 2015

Today's links 20/05/2015: #BigData anomaly detection, Machine learning, Secure #Containers

Generic and Scalable Framework for Automated Time-series Anomaly Detection : Yahoo time series anaomaly detection framework.
A Few Useful Things to Know about Machine Learning : very good overview of machine learning, it summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.
Clear Containers : Intel effort to improve security of containers by using the VT-x technology.[LWN]

 

Links of the day 27 - 10 - 2014

Today's links 27/10/2014: #latex, #twitter anomaly detection toolkit, #git

• Breakout detection in the wild : R package for anomaly detection by twitter [ git repo ] 
• Pro #Git book, written by Scott Chacon and Ben Straub
• latex-templates : lightweight Community-driven minimalist LaTeX