To ensure reproducible experiments and prevent overburdening the server, we construct an SQL-based database dubbed RiPAMI (Reviews in Pattern Analysis and Machine Intelligence, pronounced as \textipa{/ri:p\ae mi/}). This database stores information related to the paper such as title, abstract, date of publication, venue, citation counts, and reference details, etc. From initial keyword selection to the final SQL-based RiPAMI snapshot, three key steps are implemented to ensure the data in RiPAMI is clean, accurate, and reliable.