A data mining process for building recommendation systems for agricultural machines based on big data Recommendation system for agricultural machinery application

Autor(en): Altaleb, M.
Deeken, H.
Hertzberg, J. 
Herausgeber: Gandorfer, M.
Hoffmann, C.
El Benni, N.
Cockburn, M.
Anken, T.
Floto, H.
Stichwörter: agricultural machinery; Agriculture; Big data; Common factors; Cross industry; data mining; Data mining process; Decision making, Agricultural machine; Decision-making process; Domain agnostics; E- commerces; Generic process; Machinery industry; process model; Process-models, Recommender systems; recommendation system
Erscheinungsdatum: 2022
Herausgeber: Gesellschaft fur Informatik (GI)
Journal: Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)
Volumen: P-317
Startseite: 27
Seitenende: 32
There is a potential expansion in the agricultural machinery industry by using the collected data from different years. Big data is already being used in other industries like e-commerce to improve decision-making processes. There are several existing process models to lead through the generic processes of data mining. The common factor between the process models that have attained dominant public position is that they are domain-agnostic frameworks. This paper proposes a method to extend the CRoss-Industry Standard Process for Data Mining (CRISP-DM) to focus on the agricultural domain and give guidelines on how to handle and structure the agricultural data and processes to reach defined data mining goals. The paper provides a walk-through for a use case to build a recommendation system. © 2022 Gesellschaft fur Informatik (GI). All rights reserved.
Conference of 42. Jahrestagung 2022 der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft: Was bedeutet Kunstliche Intelligenz fur die Agrar- und Ernahrungswirtschaft, GIL 2022 - 42nd Annual Conference 2022 of the Society for Information Technology in Agriculture, Forestry and Food Industry: What does Artificial Intelligence mean for the Agricultural and Food industry, GIL 2022 ; Conference Date: 21 February 2022 Through 22 February 2022; Conference Code:178642
ISBN: 9783885797111
ISSN: 1617-5468
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128199069&partnerID=40&md5=80e878c8f8b75423b9cc0165a402b8d7

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