{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T17:00:58Z","timestamp":1771347658264,"version":"3.50.1"},"posted":{"date-parts":[[2026,2,17]]},"reference-count":0,"publisher":"Open Engineering Inc","license":[{"start":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:00:00Z","timestamp":1771286400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>As modern computers became increasingly more popular and larger amounts of digital data were available, different methodologies were proposed to extract information from data. CRISP-DM methodology quickly spread and is currently one of the most popular approaches used for data analysis. However, it has some shortcomings, such as beeing too general or business-centered. Different authors have proposed variations, more suitable to specific fields, in order to overcome those limitations. The present paper reviews CRISP-DM, some variations and similar methodologies, and proposes a Methodology for Industrial Data Analysis (MIDA) \u2014 amethodology conceived and improved over time, based on previous experience in industrial engineering processes. MIDA consists of eight steps and partially overlaps with CRISP-DM. It has been successfully applied in several previous projects.<\/jats:p>","DOI":"10.31224\/6465","type":"posted-content","created":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T16:01:42Z","timestamp":1771344102000},"source":"Crossref","is-referenced-by-count":0,"title":["MIDA - Method for Industrial Data Analysis based on CRISP-DM"],"prefix":"10.31224","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4313-7966","authenticated-orcid":false,"given":"Mateus","family":"Mendes","sequence":"first","affiliation":[]},{"given":"Torres","family":"Farinha","sequence":"additional","affiliation":[]}],"member":"33966","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T16:01:42Z","timestamp":1771344102000},"score":1,"resource":{"primary":{"URL":"https:\/\/engrxiv.org\/preprint\/view\/6465\/version\/8420"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,17]]},"references-count":0,"URL":"https:\/\/doi.org\/10.31224\/6465","relation":{},"subject":[],"published":{"date-parts":[[2026,2,17]]},"subtype":"preprint"}}