Comparing performances of big data stream processing platforms with RAM3S

Nowadays, Big Data platforms allow the analysis of massive data streams in an efficient way. However, the services they provide are often too raw, thus the implementation of advanced real-world applications requires a non-negligible effort for interfacing with such services. This also complicates the task of choosing which one of the many available alternatives is the most appropriate for the application at hand. In this paper, we present a comparative study of the three major open-source Big Data platforms for stream processing, as performed by using our novel RAM^3S framework. Although the results we present are specific for our use case (recognition of suspect people from massive video streams), the generality of the RAM^3S framework allows both considering such results as valid for similar applications and implementing different use cases on top of Big Data platforms with very limited effort.