  Oak Ridge National Laboratory (ORNL) experimental neutron science facilities
produce 1.2\,TB a day of raw event-based data that is stored using the standard
metadata-rich NeXus schema built on top of the HDF5 file format. Performance of
several data reduction workflows is largely determined by the amount of time
spent on the loading and processing algorithms in Mantid, an open-source data
analysis framework used across several neutron sciences facilities around the
world. The present work introduces new data management algorithms to address
identified input output (I/O) bottlenecks on Mantid. First, we introduce an
in-memory binary-tree metadata index that resemble NeXus data access patterns
to provide a scalable search and extraction mechanism. Second, data
encapsulation in Mantid algorithms is optimally redesigned to reduce the total
compute and memory runtime footprint associated with metadata I/O
reconstruction tasks. Results from this work show speed ups in wall-clock time
on ORNL data reduction workflows, ranging from 11\% to 30\% depending on the
complexity of the targeted instrument-specific data. Nevertheless, we highlight
the need for more research to address reduction challenges as experimental data
volumes increase.
