Integration of the Autonomous Open Data Prediction Framework in ERP Systems
ICEIS 2022: Proceedings of the 24th International Conference on Enterprise Information Systems. Vol.1 2022
Jānis Pekša, Jānis Grabis

Enterprise resource planning (ERP) systems are large modular enterprise applications that are designed to execute the majority of enterprise business processes with a focus on transaction processing. Business processes, on the other hand, frequently necessitate complex decision-making. If data processing logic requires complex analytical calculations and domain specific knowledge, is is considered as complex. To externalize the analytical calculations and decouple them from the core ERP system, this paper elaborates an integration framework fererred as to Autonomous Open Data Prediction Framework (AODPF). The AODPF provides advanced prediction capabilities to ERP systems. It uses data integration and processing as well as best model selection functions to generate predictions passed to the ERP system for decision-making purposes. The framework is experimentally evaluated by prediction road conditions for the case of winter road maintenance. The utility of the framework is evaluated i n the expert survey.


Keywords
ERP Systems, Prediction Framework, AODPF
DOI
10.5220/0011081300003179
Hyperlink
https://www.scitepress.org/Link.aspx?doi=10.5220/0011081300003179

Pekša, J., Grabis, J. Integration of the Autonomous Open Data Prediction Framework in ERP Systems. In: ICEIS 2022: Proceedings of the 24th International Conference on Enterprise Information Systems. Vol.1, Czech Republic, Prague, 25-27 April, 2022. [Setúbal]: SciTePress, 2022, pp.251-258. ISBN 978-989-758-569-2. ISSN 2184-4992. Available from: doi:10.5220/0011081300003179

Publication language
English (en)
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