Filter Large-scale Engine Data using Apache Spark

This paper introduces a minimum viable softwareproduct to filter large datasets of engine data recorded duringlaboratory experiments of combustion engines. The aim is to supportanalysts in the identification and analysis of specific physicalphenomenon within hours of recorded engine experimental data.Specifically, the tool has been designed considering the use case ofidentifying Low Speed Pre-Ignition events. This work describesthe tool's graphical user interface and its scalable architecturebased on mainstream web and big-data technologies as wellas the practical application to pre-ignition events identification.The paper provides details on the architecture's performance,providing evidence of its scalability by increasing the number ofavailable computing workers.