  In order to achieve the data rates proposed for the future Run 3 upgrade of
the LHCb detector, new processing models must be developed to deal with the
increased throughput. For this reason, we aim to investigate the feasibility of
purely data-driven holistic methods, with the constraint of introducing minimal
computational overhead, hence using only raw detector information. These
filters should be unbiased - having a neutral effect with respect to the
studied physics channels. In particular, the use of machine learning based
methods seems particularly suitable, potentially providing a natural
formulation for heuristic-free, unbiased filters whose objective would be to
optimize between throughput and bandwidth.
