In recent years the study of neutrino properties has raised a lot of interests in the Particle and Astroparticle physics communities. The discovery of coherent elastic neutrino nucleus scattering (CEνNS), as new highly sensitive detection channel has unleashed an impressive discovery potential. Cryogenic detectors are now proposed for the detection of astrophysical neutrino sources via CEνNS. This process ensures extremely high-cross sections, about a factor 1000 higher than IBD, and equal sensitivity to all neutrino flavours. The signals produced by these messengers in terrestrial detectors are low energy nuclear recoils in the keV range. The newly proposed RES-NOVA experiment is aiming at studying the neutrino emission of Supernovae via CEνNS, using a cryogenic detector array made from archaeological Pb. The unique properties of this material ensure simultaneously the highest neutrino cross-section and ultra-low background level. In this contribution we will present the study and development of an online trigger system to detect short-lived features in event time series. We study the online monitoring of the collected data using both Poisson statistics and others tests that are sensitive to non-uniformities in random samples, showing results on their possible combination. Based on this we present results regarding the promptness and successful recognition of signals based on models of neutrino bursts preceding and following a Supernova explosion.