  Reducing the computational time to process large data sets in Data
Envelopment Analysis (DEA) is the objective of many studies. Contributions
include fundamentally innovative procedures, new or improved preprocessors, and
hybridization between - and among - all these. Ultimately, new contributions
are made when the number and size of the LPs solved is somehow reduced. This
paper provides a comprehensive analysis and comparison of two competing
procedures to process DEA data sets: BuildHull and Enhanced Hierarchical
Decomposition (EHD). A common ground for comparison is made by examining their
sequential implementations, applying to both the same preprocessors - when
permitted - on a suite of data sets widely employed in the computational DEA
literature. In addition to reporting on execution time, we discuss how the data
characteristics affect performance and we introduce using the number and size
of the LPs solved to better understand performances and explain differences.
Our experiments show that the dominance of BuildHull can be substantial in
large-scale and high-density datasets. Comparing and explaining performance
based on the number and size of LPS lays the groundwork for a comparison of the
parallel implementations of procedures BuildHull and EHD.
