Many of the bike-sharing systems introduced around the world in the past 15 years have the same problem: Riders tend to take some routes and not others. As a result, the bikes tend to collect in a few places, which is a drag for users and a costly problem for the operators, who “rebalance” the system using trucks that take bikes from full stations to empty ones. Now, scientists are coming up with special algorithms to improve this process. One of them, developed by scientists at the Vienna University of Technology and the Austrian Institute of Technology, is now being tested in Vienna’s bike-sharing system; another, developed at Cornell University, is already in use in New York City.Timely — here’s what Dublin Bikes looked like this morning: https://twitter.com/jmason/status/503828246086295552 (via Andrew Caines)
We proposed the JIQ algorithms for web server farms that are dynamically scalable. The JIQ algorithms significantly outperform the state-of-the-art SQ(d) algorithm in terms of response time at the servers, while incurring no communication overhead on the critical path. The overall complexity of JIQ is no greater than that of SQ(d). The extension of the JIQ algorithms proves to be useful at very high load. It will be interesting to acquire a better understanding of the algorithm with a varying reporting threshold. We would also like to understand better the relationship of the reporting frequency to response times, as well as an algorithm to further reduce the complexity of the JIQ-SQ(2) algorithm while maintaining its superior performance.
I often need to do rough back-of-the-envelope reasoning about things, and I find that doing a bit of work to develop an intuition for how a new technique performs is usually worthwhile. So, here are three broad rules of thumb to remember when discussing Bloom filters down the pub: One byte per item in the input set gives about a 2% false positive rate. The optimal number of hash functions is about 0.7 times the number of bits per item. 3 – The number of hashes dominates performance.
This sounds pretty neat:
With Logentries Anomaly Detection, users can: Set-up real-time alerting based on deviations from important patterns and log events. Easily customize Anomaly thresholds and compare different time periods. With Logentries Inactivity Alerting, users can: Monitor standard, incoming events such as an application heart beat. Receive real-time alerts based on log inactivity (i.e. receive alerts when something does not occur).
This is actually quite educational