{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T08:05:59Z","timestamp":1761897959300,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031488573"},{"type":"electronic","value":"9783031488580"}],"license":[{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-48858-0_42","type":"book-chapter","created":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T06:02:43Z","timestamp":1702965763000},"page":"531-546","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Data Science Methodologies \u2013 A Benchmarking Study"],"prefix":"10.1007","author":[{"given":"Luciana","family":"Machado","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2181-6837","authenticated-orcid":false,"given":"Filipe","family":"Portela","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,20]]},"reference":[{"key":"42_CR1","unstructured":"Azevedo, A., Santos, M.F.: KDD, semma and CRISP-DM: a parallel overview. IADIS European Conference on Data Mining, pp. 182\u2013185 (2008). http:\/\/recipp.ipp.pt\/bitstream\/10400.22\/136\/3\/KDD-CRISP-SEMMA.pdf"},{"key":"42_CR2","unstructured":"Shafique, U., Qaiser, H.: A comparative study of data mining process models (KDD, CRISP-DM and SEMMA). Int. J. Innov. Sci. Res. 12(1), 217\u2013222 (2014). http:\/\/www.ijisr.issr-journals.org\/"},{"key":"42_CR3","doi-asserted-by":"publisher","unstructured":"Yessad, L., Labiod, A.: Comparative study of data warehouses modeling approaches: Inmon, Kimball and data vault. In: 2016 International Conference on System Reliability and Science ICSRS 2016 - Proceedings, pp. 95\u201399 (2017). https:\/\/doi.org\/10.1109\/ICSRS.2016.7815845","DOI":"10.1109\/ICSRS.2016.7815845"},{"key":"42_CR4","unstructured":"AgileData.io Limited. AGILEDATA.IO. agiledata.io (2023). https:\/\/agiledata.io\/"},{"key":"42_CR5","doi-asserted-by":"publisher","unstructured":"Di Tria, F., Lefons, E., Tangorra, F.: A proposal of methodology for designing big data warehouses. Preprints, no. June, p. 2018. https:\/\/doi.org\/10.20944\/preprints201806.0219.v1","DOI":"10.20944\/preprints201806.0219.v1"},{"key":"42_CR6","doi-asserted-by":"publisher","unstructured":"Paneque, M., del M. Rold\u00e1n-Garc\u00eda, M., Garc\u00eda-Nieto, J.: e-LION: data integration semantic model to enhance predictive analytics in e-learning. Expert Syst. Appl. 213, 118892 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2022.118892","DOI":"10.1016\/j.eswa.2022.118892"},{"issue":"1","key":"42_CR7","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/s10844-020-00608-7","volume":"56","author":"P Sawadogo","year":"2021","unstructured":"Sawadogo, P., Darmont, J.: On data lake architectures and metadata management. J. Intell. Inf. Syst. 56(1), 97\u2013120 (2021). https:\/\/doi.org\/10.1007\/s10844-020-00608-7","journal-title":"J. Intell. Inf. Syst."},{"key":"42_CR8","doi-asserted-by":"publisher","unstructured":"Haertel, C., Pohl, M., Staegemann, D., Turowski, K.: Project artifacts for the data science lifecycle: a comprehensive overview. In: Proceedings of - 2022 IEEE International Conference on Big Data (Big Data) 2022, pp. 2645\u20132654 (2022). https:\/\/doi.org\/10.1109\/BigData55660.2022.10020291","DOI":"10.1109\/BigData55660.2022.10020291"},{"key":"42_CR9","unstructured":"geeksforgeeks. Data science process. geeksforgeeks (2023). https:\/\/www.geeksforgeeks.org\/data-science-process\/"},{"key":"42_CR10","unstructured":"Campos, L.: A complete guide to data mining and how to use it. HubSpot (2023). https:\/\/blog.hubspot.com\/website\/data-mining"},{"key":"42_CR11","unstructured":"IBM. IBM analytics solution unified method. IBM (2015). http:\/\/i2t.icesi.edu.co\/ASUM-DM_External\/index.htm#cognos.external.asum-DM_Teaser\/deliveryprocesses\/ASUM-DM_8A5C87D5.html"},{"key":"42_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/3-540-63107-0_1","volume-title":"Advanced Information Systems Engineering","author":"S Ceri","year":"1997","unstructured":"Ceri, S., Fraternali, P.: The story of the idea methodology. In: Oliv\u00e9, A., Pastor, J.A. (eds.) CAiSE 1997. LNCS, vol. 1250, pp. 1\u201317. Springer, Heidelberg (1997). https:\/\/doi.org\/10.1007\/3-540-63107-0_1"},{"key":"42_CR13","doi-asserted-by":"publisher","unstructured":"Grady, N.W., Payne, J.A., Parker, H.: Agile big data analytics: analyticsops for data science. In: Proceedings of - 2017 IEEE International Conference on Big Data (Big Data) 2017, vol. 2018-Janua, pp. 2331\u20132339 (2017). https:\/\/doi.org\/10.1109\/BigData.2017.8258187","DOI":"10.1109\/BigData.2017.8258187"},{"key":"42_CR14","unstructured":"Rollins, J.B.: Metodologia de base para ci\u00eancia de dados. IBM Anal. Route 100 Somers, NY 10589 (2015). https:\/\/www.ibm.com\/downloads\/cas\/B1WQ0GM2"},{"key":"42_CR15","unstructured":"Lean. Agile framework for managing data science product and projects. leands.ai (2023). https:\/\/leands.ai\/"},{"key":"42_CR16","doi-asserted-by":"publisher","unstructured":"Kumari, K., Bhardwaj, M., Sharma, S.: OSEMN approach for real time data analysis. Int. J. Eng. Manag. Res. 10(02), 107\u2013110 (2020). https:\/\/doi.org\/10.31033\/ijemr.10.2.11","DOI":"10.31033\/ijemr.10.2.11"},{"key":"42_CR17","unstructured":"Microsoft. What is the Team Data Science Process?. Microsoft (2023). https:\/\/learn.microsoft.com\/en-us\/azure\/architecture\/data-science-process\/overview"},{"key":"42_CR18","unstructured":"Astera Software. Automa\u00e7\u00e3o de data warehouse. Astera.com (2023). https:\/\/www.astera.com\/pt\/knowledge-center\/data-warehouse-automation-a-complete-guide\/"},{"key":"42_CR19","unstructured":"IBM. Dimensional modeling life cycle and work flow. ibm.com (2021). https:\/\/www.ibm.com\/docs\/en\/ida\/9.1.2?topic=modeling-dimensional-life-cycle-work-flow"}],"container-title":["Communications in Computer and Information Science","Advanced Research in Technologies, Information, Innovation and Sustainability"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-48858-0_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T06:15:14Z","timestamp":1702966514000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-48858-0_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,20]]},"ISBN":["9783031488573","9783031488580"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-48858-0_42","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,12,20]]},"assertion":[{"value":"20 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ARTIIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"artiis2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/artiis.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}