ESI Group has just purchased another big data analytics provider, Mineset Inc., a subsidiary of SGI. The purchase aims to complement ESI’s recent INENDI release on big data correlation detection. Mineset’s technology performs data analytics and integrates it with machine-learning technology for pattern recognition.
“Combining INENDI’s data correlation detection with Mineset’s pattern recognition and linking both to ESI Group’s Virtual Prototyping solutions provides a new transformative process and source of value creation, particularly in the traditional Virtual Engineering domain,” explained Alain de Rouvray, ESI Group’s chairman and CEO.
The Mineset program will also be integrated into ESI’s Virtual Engineering offerings, bringing big data analytics into the product design loop. This will help to inform decision making and to find hidden correlations, faults, design optimizations and maintenance strategies in simulation results.
“This combination of talent and technology, building on other recent acquisitions, will contribute to revolutionize the field of simulation results analytics with extensions such as predictive maintenance and cyber-security,” said de Rouvray.
Currently, Mineset’s big data pattern recognition software is available over the cloud through a web-based interface. ESI explains that the GUI is designed for use by non-programming professionals. This will help to expand the use of the big data analysis program throughout the engineering community.
“While the exponential growth of the Information and Communication Technologies (ICT) finds an accelerated usage in all economic domains, it also generates massive amounts of [big] data,” said de Rouvray. “This trend imposes a critical pressure on industrialists confronted with the need to link virtual simulation models to the real world data of the Internet of Things (IoT). To innovate effectively and competitively, it has become mandatory to generate a multitude of virtual models and to compare them between themselves and versus real-world information.”
This link to the physical and the real world via virtual models is what gives big data analytics so much potential for simulation experts. This is especially true in the growing age of the IoT. As your products are out there in the real world being operated by real customers you can gather a significant amount of usage information.
This information can then be crunched using ESI’s new big data software to inform the loads and boundary conditions of your models. This will allow for products to be optimized for realistic uses, as opposed to expected uses.
Since simulations create large amounts of data as well, using big data analytics software can feedback into the development cycle. The software can find patterns in the simulation results that would otherwise go unnoticed by human analysts. Once these patterns are found, further optimizations are possible for even more robust products.
This path to the digital twin or virtual model is becoming a significant trend in the simulation industry with PTC, Dassault Systems and ANSYS already riding the wave. So expect to hear more about IoT in your simulation news.