Softcom has contributed to the reengineering of a correlation engine for a big data use case, aimed at enhancing system performance assessment through data mining and process mining techniques. Originally developed for the LLMS project, this engine was adapted for a larger-scale application using distributed processing and cloud technologies. The project included a detailed characterization and scalability analysis, specifically applied to CNES GAIA Data system log files.

As a subcontractor, Softcom was responsible for analyzing the LLMS engine’s scalability, selecting appropriate technologies, and designing, developing, testing, and characterizing the performance of a cloud-scalable solution. Our team utilized advanced technologies, including Java 8, Apache Spark, Apache Hadoop, Maven, Microsoft Azure cloud, and Linux VMs, to deliver a robust and efficient solution.

This project underscores Softcom’s proficiency in developing high-performance, scalable data solutions, supporting ESA and CNES in managing complex data requirements.

End Customer:  ESOC – CNES

Team:   ATOS (FR), Softcom-Int  

Position: Subcontractor

under a programme of, and funded by, the European Space Agency