Gain actionable insights from streamed data analytics
- Anomaly detection and correction, for clean data with meaningful results
- Event or feature extraction based on sophisticated conditions, so only specific situations are considered
- Data compression for volume reduction and increased performance
- Data reduction to summarize particular aspects of data in a histogram or spectrum
- Signal processing such as sampling, filters, Fourier analysis or arithmetic operations, to transform the data
- Integration of machine learning for applications such as virtual and inferential sensing
- Deliver better insights and enable greater understanding of how products are used
- Reduce cost of unnecessary testing
- Improve target setting for design loads and test scenarios
- Reduce warranty claims without overdesigning and adding unncessary cost
- Optimize designs that deliver reduced overall cost, weight, and energy consumption
Download a whitepaper on key application areas of nCodeDS
Analysis of Digital Bus and Network Data
Bus data is increasingly used for many applications beyond its initial purpose. These applications include usage monitoring, design validation, design enhancement, safety validation, etc.
Connected vehicles, for example, are making these sources of data readily available and offer the promise of transformational information. However, bus data raises a number of challenges, essentially due to the quality and quantity of data.
The next generation solution for these challenges is nCodeDS, a scalable analysis framework enabling engineers to gain actionable insights from streamed data analytics.
Big Data Analytics in Engineering Applications
With the growing utilization of sensors, “big data” is a reality of life for engineering applications. In the pursuit of storing, searching and retrieving this data at scale, industries are looking at big data systems for data management solutions.
But the real question is: how do you extract value from big data? Many engineering applications require the application of sophisticated analyses and while generic big data tools are necessary for basic data management, they are inadequate for performing real-world engineering analysis.
This whitepaper introduces nCodeDS, a new software solution, designed specifically for efficiently analyzing large volumes of engineering time series data. Through examples, it will illustrate how to achieve greater value from insights gained through superior analytics in an engineering context.