  Data-intensive science is increasingly reliant on real-time processing
capabilities and machine learning workflows, in order to filter and analyze the
extreme volumes of data being collected. This is especially true at the energy
and intensity frontiers of particle physics where bandwidths of raw data can
exceed 100 Tb/s of heterogeneous, high-dimensional data sourced from hundreds
of millions of individual sensors. In this paper, we introduce a new
data-driven approach for designing and optimizing high-throughput data
filtering and trigger systems such as those in use at physics facilities like
the Large Hadron Collider (LHC). Concretely, our goal is to design a
data-driven filtering system with a minimal run-time cost for determining which
data event to keep, while preserving (and potentially improving upon) the
distribution of the output as generated by the hand-designed trigger system. We
introduce key insights from interpretable predictive modeling and
cost-sensitive learning in order to account for non-local inefficiencies in the
current paradigm and construct a cost-effective data filtering and trigger
model that does not compromise physics coverage.
