  We report on two distinct computational approaches to self-consistently
measure photospheric properties of large samples of stars. Both procedures
consist of a set of several semi-integrated tasks based on shell and Python
scripts, which efficiently run either our own codes or open source software
commonly adopted by the astronomical community. One approach aims to derive the
main stellar photospheric parameters and abundances of a few elements by
analysing high-resolution spectra from a given public library homogeneously
constructed. The other one is applied to recover the abundance of a single
element in stars with known photospheric parameters by using mid-resolution
spectra from another open homogeneous database and calibrating derived
abundances. Both semi-automated computational approaches provide homogeneity
and objectivity to every step of the process and represent a fast way to reach
partial and final results as well as to estimate measurement errors, making
possible to systematically evaluate and improve the distinct steps.
