Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.1464
Publisher URL: https://www.epubli.de/shop/buch/2000000069144
Title: Data mining and fault tolerance in warehousing
Language: English
Authors: Reining, Christopher 
Bousbiba, Omar 
Jungen, Svenja 
Ten Hompel, Michael 
Keywords: materials flow system;data mining;fault tolerance;survey
Issue Date: Oct-2017
Publisher: epubli
Source: Digitalization in supply chain management and logistics
Volume number: 23
Journal or Series Name: Proceedings of the Hamburg International Conference of Logistics (HICL) 
Abstract (english): This paper surveys the significance of data mining techniques and fault tolerance in future materials flow systems with a focus on planning and decision-making. The fundamental connection between data mining, fault tolerance, and materials flow is illustrated. Contemporary developments in warehousing are assessed to formulate upcoming challenges. In particular, the transition towards distributed systems and the increasing data volume is examined. The significance of taking fault tolerance into account is emphasized. Ultimately, research issues are derived by conflating the previous findings. They comprise a holistic approach towards the integration of data science and fault tolerance techniques into future materials flow systems. Tackling these research issues will help to proactively harmonize the data representation to specific data mining techniques and increase the reliability of such systems.
Conference: Hamburg International Conference of Logistics (HICL) 2017 
URI: http://tubdok.tub.tuhh.de/handle/11420/1467
DOI: 10.15480/882.1464
ISBN: 978-3-7450-4328-0
ISSN: 2365-5070
Type: InProceedings (Aufsatz / Paper einer Konferenz etc.)
License: CC BY-SA 4.0 (Attribution-ShareAlike 4.0) CC BY-SA 4.0 (Attribution-ShareAlike 4.0)
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