Industrial IoT

The capabilities of the Crosser solution opens up for a large number of cloud streaming use cases. Here are the top four:

A common problem in our industry is data mismatch, data from different sources, to send it to other systems that expects data in another format. These situations are where data transformation and harmonization...

An important use of machine data is to calculate different KPIs to measure the performance and behaviour of machines or processes. Edge analytics is a perfect tool both for collecting the relevant data and calculating...

In this blog we will discuss how you can build a modern historian by combining best-of-breed tools in a flexible and efficient way.

Siemens Mindsphere has taken the lead as the open operating system for Industrial IoT, according to Forrester. The cloud-based platform is built to connect and integrate the entire manufacturing process, to give manufacturers the capability to implement a wide array of solutions

Industry 4.0 is based on the convergence of OT and IT using modern technologies that are significantly easier to use and faster to deploy in order to extract business value from integrating machine data, shop floor data, enterprise data and cloud applications.

OSIsoft PI System is one of the most widely deployed solutions for capturing and storing machine data within manufacturing, energy, utilities, pharma, transportation and facility management. 

Kepware is one of the leaders in industrial connectivity, by providing drivers for almost any type of machine-level protocol. The main use case is to collect data from machines and deliver it to centralized platforms for analysis - but what if you want to take action on your data already on the factory floor? This is where Crosser comes in.

Using video cameras as sensors in industrial IoT applications is becoming more and more popular. The reason is of course that it opens up for many interesting applications.

Extracting valuable insights out of data collected from machine sensors can be hard, often requiring analyzing data from many sensors in parallel.
Due to the complexity, machine learning (ML) methods are becoming more and more popular to analyze these datasets.