When you want to spot hosts, applications, containers, plant equipment, or sensors that are behaving differently from others, you can use the Median Absolute Deviation (MAD) algorithm to identify when a time series is “deviating from the pack”. In this tutorial, we’ll identify anomalous hosts using mad() — the Flux implementation of MAD — from a Third Party Flux Package called anaisdg/anomalydetection.
The fleet management industry has been quick to embrace technology. They want to understand the current state of vehicles and drivers to improve operations and safety. Data privacy laws are especially important as fleet managers expand their reach into new territories. WayKonnect is using InfluxDB Enterprise to improve the fleet management industry.
This article was written by Gregory Scafarto, Data Scientist intern at Supralog, in collaboration with InfluxData’s DevRel Anais Dotis-Georgiou. At InfluxData, we pride ourselves on our awesome InfluxDB Community. We’re grateful for all of your contributions and feedback. Whether it’s Telegraf plugins, community templates, awesome InfluxDB projects, or Third Party Flux Packages, your contributions continue to both impress and humble us.
In a factory environment, collecting data to gain useful insights from various sources is challenging because it requires connecting to many different types of automation systems, plcs and devices that often speak different languages. This is the problem that German industrial software company, inray (specialized in data communication between software systems and components in Industry 4.0, IoT and IIoT) set out to solve for its customers.
