The BOOM! workshop solicits presentations of ongoing or previously published work to enable discussions on a broad range of topics, without formal proceedings. Selected abstracts will be invited for full submission as part of a special issue of the Journal on Reliable Intelligent Environments.

Big data is booming. Advanced data analytic techniques, like deep learning, predict the weather, buy and sell stocks, beat the humans in chess playing, diagnose diseases from CT scans, and recommend us which books to buy. 

Big data is gaining more and more importance in the area of ​​formal methods: quantitative analysis techniques largely depends on the values ​​of the input parameters, which could be learned from historic data. Also, automatic learning formal models themselves, or model skeletons as well as Algorithms have gained a lot of momentum with techniques like model mining, and reinforcement learning. In the other direction, formal methods have also a lot to offer for the development of big data analytic techniques, such as the verification of deep learning algorithms, model- Based classification algorithms, methods and models for data cleaning etc.