@conference {1136,
	title = {Explaining successful docker images using pattern mining analysis},
	booktitle = {Federation of International Conferences on Software Technologies: Applications and Foundations},
	year = {2018},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Docker is on the rise in today{\textquoteright}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 directly impacts on its usage, and hence on the potential revenues of its developers. In this paper, we present a frequent pattern mining-based approach for understanding how to improve an image to increase its popularity. The results in this work can provide valuable insights to Docker image providers, helping them to design more competitive software products.},
	doi = {10.1007/978-3-030-04771-9_9},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-04771-9_9},
	author = {Riccardo Guidotti and Soldani, Jacopo and Neri, Davide and Antonio Brogi}
}
@conference {1292,
	title = {Helping your docker images to spread based on explainable models},
	booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Docker is on the rise in today{\textquoteright}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.},
	doi = {10.1007/978-3-030-10997-4_13},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-10997-4_13},
	author = {Riccardo Guidotti and Soldani, Jacopo and Neri, Davide and Brogi, Antonio and Dino Pedreschi}
}
