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Willie Wheeler's Blog - wwblog

Willie Wheeler's personal blog. Mostly tech. Monitoring Bookings and the Law of Large Numbers. When monitoring bookings, one common approach is to use historical levels as a baseline, and then alert if the current level is. Lower than the historical, where. Is a static value that tries to strike a balance between capturing as many problems. Basic Time Series Analysis in R. This is really just a set of personal notes for working with time series in R. But if it's useful to you, great. I'm using the xts.

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Willie Wheeler's Blog - wwblog | williewheeler.com Reviews

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Willie Wheeler's personal blog. Mostly tech. Monitoring Bookings and the Law of Large Numbers. When monitoring bookings, one common approach is to use historical levels as a baseline, and then alert if the current level is. Lower than the historical, where. Is a static value that tries to strike a balance between capturing as many problems. Basic Time Series Analysis in R. This is really just a set of personal notes for working with time series in R. But if it's useful to you, great. I'm using the xts.

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1 wwblog

2 folding in haskell

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7 or left fold

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Willie Wheeler's Blog - wwblog | williewheeler.com Reviews

https://williewheeler.com

Willie Wheeler's personal blog. Mostly tech. Monitoring Bookings and the Law of Large Numbers. When monitoring bookings, one common approach is to use historical levels as a baseline, and then alert if the current level is. Lower than the historical, where. Is a static value that tries to strike a balance between capturing as many problems. Basic Time Series Analysis in R. This is really just a set of personal notes for working with time series in R. But if it's useful to you, great. I'm using the xts.

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williewheeler.com

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- wwblog

http://williewheeler.com/tags/rules

Willie Wheeler's personal blog. Mostly tech. Diagnosing Bookings Drops with Drools. Nonlinear regression in R. Jul 7, 2016. Polynomial regression in R. Jul 6, 2016. May 11, 2016. Java/Fortran Integration Using JNI. Mar 6, 2016. Anomaly Detection Using STL. Mar 3, 2016. Feb 22, 2016. Monitoring Bookings and the Law of Large Numbers. May 16, 2015. Basic Time Series Analysis in R. May 14, 2015. Diagnosing Bookings Drops with Drools. Apr 11, 2015. Mar 28, 2015. Spring data rest (1). Time series analysis (3).

2

Mergesort in Haskell - wwblog

http://williewheeler.com/2015/03/17/mergesort-in-haskell

Willie Wheeler's personal blog. Mostly tech. Mar 17, 2015. After yesterday's post on quicksort in Haskell. I was curious whether mergesort is as elegant. Not quite, but I learned a bit along the way. Here was my initial mergesort implementation:. Function itself is easy enough to understand. Split the list in half, sort the halves individually, and then merge them. But unlike quicksort, we have a few extra helper functions to accomplish the splitting and merging. It turns out that the. Merge same as above.

3

Redirecting HTTP to HTTPS in Spring Boot - wwblog

http://williewheeler.com/2015/03/25/redirecting-http-to-https-in-spring-boot

Willie Wheeler's personal blog. Mostly tech. Redirecting HTTP to HTTPS in Spring Boot. Mar 25, 2015. When using the embedded Tomcat server with Spring Boot, by default you choose either HTTP or HTTPS. But sometimes you want to allow users to come in through HTTP and then redirect them to HTTPS. Spring Boot supports this. It would be nice if you could do the whole thing through. If you want to have both then you’ll need to configure one of them programmatically. It’s recommended to use. This simple config...

4

Diagnosing Bookings Drops with Drools - wwblog

http://williewheeler.com/2015/04/11/diagnosing-bookings-drops-with-drools-1

Willie Wheeler's personal blog. Mostly tech. Diagnosing Bookings Drops with Drools. Apr 11, 2015. I've been thinking of using a rules engine to help diagnose unexpected bookings drops. Rules are a good way to simplify reasoning about complex domains, and there are lots of different things that can lead to bookings drops. Examples include:. Major sporting events (Super Bowl, World Cup, etc.). A/B tests gone bad. Moving from standard time to daylight saving time and vice versa. Here's one that's slightly (...

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- wwblog

http://williewheeler.com/tags/haskell

Willie Wheeler's personal blog. Mostly tech. Specifications vs Algorithms in Haskell. Haskell Reveals the Essence of Quicksort. Four Flavors of Functional Programming with Map and Filter. Nonlinear regression in R. Jul 7, 2016. Polynomial regression in R. Jul 6, 2016. May 11, 2016. Java/Fortran Integration Using JNI. Mar 6, 2016. Anomaly Detection Using STL. Mar 3, 2016. Feb 22, 2016. Monitoring Bookings and the Law of Large Numbers. May 16, 2015. Basic Time Series Analysis in R. May 14, 2015.

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Category | Devops | Expedia Engineering Blog

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Science of Travel blog. Category “Devops”. December 6, 2016. Kuldeep Chowhan ( @this is kuldeep. Estimated reading time: 4 minutes. The Inside Scoop on Primer, Expedia’s Internal Cloud Deployment Tool. By Kuldeep Chowhan ( @this is kuldeep. On December 6, 2016. A small group of us came together to look at moving a microservice to the cloud. We focused on an application within the shopping path starting…. November 3, 2016. Estimated reading time: 5 minutes. AWS Lessons Learned for Data Processing Pipelines.

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Willie Wheeler's Blog - wwblog

Willie Wheeler's personal blog. Mostly tech. Monitoring Bookings and the Law of Large Numbers. When monitoring bookings, one common approach is to use historical levels as a baseline, and then alert if the current level is. Lower than the historical, where. Is a static value that tries to strike a balance between capturing as many problems. Basic Time Series Analysis in R. This is really just a set of personal notes for working with time series in R. But if it's useful to you, great. I'm using the xts.

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