TY - CONF T1 - Helping your docker images to spread based on explainable models T2 - Joint European Conference on Machine Learning and Knowledge Discovery in Databases Y1 - 2018 A1 - Riccardo Guidotti A1 - Soldani, Jacopo A1 - Neri, Davide A1 - Brogi, Antonio A1 - Dino Pedreschi AB - Docker is on the rise in today’s enterprise IT. It permits shipping applications inside portable containers, which run from so-called Docker images. Docker images are distributed in public registries, which also monitor their popularity. The popularity of an image impacts on its actual usage, and hence on the potential revenues for its developers. In this paper, we present a solution based on interpretable decision tree and regression trees for estimating the popularity of a given Docker image, and for understanding how to improve an image to increase its popularity. The results presented in this work can provide valuable insights to Docker developers, helping them in spreading their images. Code related to this paper is available at: https://github.com/di-unipi-socc/DockerImageMiner. JF - Joint European Conference on Machine Learning and Knowledge Discovery in Databases PB - Springer UR - https://link.springer.com/chapter/10.1007/978-3-030-10997-4_13 ER -