Logo des Repositoriums
 

A data mining process for building recommendation systems for agricultural machines based on big data

dc.contributor.authorAltaleb, Mohamed
dc.contributor.authorDeeken, Henning
dc.contributor.authorHertzberg, Joachim
dc.contributor.editorGandorfer, Markus
dc.contributor.editorHoffmann, Christa
dc.contributor.editorEl Benni, Nadja
dc.contributor.editorCockburn, Marianne
dc.contributor.editorAnken, Thomas
dc.contributor.editorFloto, Helga
dc.date.accessioned2022-02-24T13:34:39Z
dc.date.available2022-02-24T13:34:39Z
dc.date.issued2022
dc.description.abstractThere 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.en
dc.identifier.isbn978-3-88579-711-1
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/38384
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-317
dc.subjectagricultural machinery
dc.subjectdata mining
dc.subjectprocess model
dc.subjectrecommendation system
dc.titleA data mining process for building recommendation systems for agricultural machines based on big dataen
dc.typeText/Conference Paper
gi.citation.endPage32
gi.citation.publisherPlaceBonn
gi.citation.startPage27
gi.conference.date21.-22. Februar 2022
gi.conference.locationTänikon, Online

Dateien

Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
Name:
GIL2022_Altaleb_27-32.pdf
Größe:
701.66 KB
Format:
Adobe Portable Document Format