{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T07:50:05Z","timestamp":1762069805452,"version":"build-2065373602"},"reference-count":19,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T00:00:00Z","timestamp":1664841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Data analysis of weather phenomena to either predict or control human imprint on the environment requires the collection of various forms of observational data ranging from historical and longitudinal to forecast. The objective of this research paper is the development of a data warehouse (DW) based on a new hybrid logical schema, concerning the assimilation and maintenance of historical meteorological data from all operating airports in Greece, along with data in the Greek Flight Information Region related to flight delays and cancellations. SQL is used for querying these data and makes them easily accessible and manageable. The data from the DW are collected and used as training data for the induction of predictive models. In this study, the prediction problem is cast as a classification task, and different decision tree induction techniques are applied to build accurate models that allow flexible scheduling and planning for the minimization of waiting time and inconvenience of passengers.<\/jats:p>","DOI":"10.3390\/informatics9040078","type":"journal-article","created":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T23:39:31Z","timestamp":1665272371000},"page":"78","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Meteorological Data Warehousing and Analysis for Supporting Air Navigation"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1892-4183","authenticated-orcid":false,"given":"Georgia","family":"Garani","sequence":"first","affiliation":[{"name":"Department of Digital Systems, University of Thessaly, 41500 Larisa, Greece"}]},{"given":"Dionysios","family":"Papadatos","sequence":"additional","affiliation":[{"name":"School of Science and Technology, Hellenic Open University, 26335 Patras, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2247-3082","authenticated-orcid":false,"given":"Sotiris","family":"Kotsiantis","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Patras, 26504 Patras, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9758-0819","authenticated-orcid":false,"given":"Vassilios S.","family":"Verykios","sequence":"additional","affiliation":[{"name":"School of Science and Technology, Hellenic Open University, 26335 Patras, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,4]]},"reference":[{"key":"ref_1","unstructured":"Tsoni, R., Garani, G., and Verykios, V.S. (2022, January 18\u201320). Incorporating Data Warehouses into Data Pipelines for Deploying Learning Analytics Dashboards. Proceedings of the 13th International Conference on Information, Intelligence, Systems and Applications (IISA 2022), Corfu, Greece."},{"key":"ref_2","unstructured":"Inmon, W.H. (2005). Building the Data Warehouse, Wiley Publishing. [4th ed.]."},{"key":"ref_3","unstructured":"Petricioli, L., Humski, L., and Vrdoljak, B. (2021, January 21\u201323). The Challenges of NoSQL Data Warehousing. Proceedings of the International Conference on E-Business Technologies, Belgrade, Serbia."},{"key":"ref_4","first-page":"11668","article-title":"Flight Delay Prediction System Using Weighted Multiple Linear Regression","volume":"4","author":"Oza","year":"2015","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Choi, S., Kim, Y.J., Briceno, S., and Mavris, D. (2016, January 25\u201329). Prediction of weather-induced airline delays based on machine learning algorithms. Proceedings of the IEEE\/AIAA 35th Digital Avionics Systems Conference, Sacramento, CA, USA.","DOI":"10.1109\/DASC.2016.7777956"},{"key":"ref_6","first-page":"88","article-title":"Flight Delay Prediction System","volume":"9","author":"Borse","year":"2020","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/TVT.2019.2954094","article-title":"Flight Delay Prediction Based on Aviation Big Data and Machine Learning","volume":"69","author":"Gui","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.47059\/revistageintec.v11i3.2006","article-title":"An Approach of Applying Machine Learning Model in Flight Delay Prediction-A Comparative Analysis","volume":"11","author":"Somani","year":"2021","journal-title":"Geintec"},{"key":"ref_9","first-page":"1","article-title":"Flight Delay Prediction Using Gradient Boosting Machine Learning Classifiers","volume":"3","author":"Lu","year":"2021","journal-title":"Int. J. Quantum Inf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1080\/01441647.2020.1861123","article-title":"On the relevance of data science for flight delay research: A systematic review","volume":"41","author":"Carvalho","year":"2021","journal-title":"Transp. Rev."},{"key":"ref_11","unstructured":"H\u00e4berli, C., and Tombros, D. (2001, January 4\u20138). A Data Warehouse Architecture for MeteoSwiss: An Experience Report. 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Netw."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"22","DOI":"10.4018\/jdwm.2012100102","article-title":"Integrating Star and Snowflake Schemas in Data Warehouses","volume":"8","author":"Garani","year":"2012","journal-title":"Int. J. Data Warehous. Min."},{"key":"ref_19","first-page":"36","article-title":"An Application of an Intelligent Data Warehouse for Modelling Spatiotemporal Objects","volume":"1","author":"Garani","year":"2020","journal-title":"Int. J. Big Data Intell. Appl."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/9\/4\/78\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:46:24Z","timestamp":1760143584000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/9\/4\/78"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,4]]},"references-count":19,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["informatics9040078"],"URL":"https:\/\/doi.org\/10.3390\/informatics9040078","relation":{},"ISSN":["2227-9709"],"issn-type":[{"type":"electronic","value":"2227-9709"}],"subject":[],"published":{"date-parts":[[2022,10,4]]}}}