USU Software AG is a mid-tier business competing in many areas of IT. Based on the insights of several research projects, we are currently improving our big data analytics and machine learning expertise. In the past, we were able to provide solutions for industrial big data analysis, applying machine learning and complex-event-processing technologies on sensor data (historical data and streams) from production and machine tools . E.g. we carried out Germany’s largest project for predictive maintenance at our customer Heidelberger Druckmaschinen AG, predicting the status of more than 10,000 printing machines, analyzing more than 1,500 sensors per machine .
In a previous blog post we have described how we used several HOBBIT benchmarks to measure and improve the performance of our machine learning applications. In contrast, this blog post will focus on the influence of HOBBIT on our new Big Data Analytics Platform Katana.
Katana provides a holistic platform for our customers to store and process their data, develop custom machine learning pipelines, visualize results and finally transfer the developed algorithms into their productive environment. This product is one of the first in an evolving market. To provide the best solution for our customers and stay competitive or even top of the market, we need to evaluate and select the best solutions within our technology stack. HOBBIT helped us to evaluate and select the core technology of Katana based on the defined benchmarks, their KPIs and measure results based on the defined gold standards. Furthermore, the development of the web-based graphical user interface of HOBBIT by USU as well as the evaluation of frameworks for identity and access management highly influenced the realization of our own products.
By locally installing our own instance of the HOBBIT platform we created an environment to independently test and evaluate core technologies. This enabled us to measure the performance of storage solutions like databases and distributed file systems based on the Data Storage Benchmark. Furthermore, this helps us to evaluate newly developed algorithms for data processing based on the Machine Learning Benchmarks as well as the Streams Benchmarks.
We see a high potential of the platform in academia as well as industry and expect more developers, solution providers and technology users to get involved in the association, the development of new Benchmarks as well as the further improvement of the platform.