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Authors: Rafael Marin Machado de Souza 1 ; Fabrício Gomes Vilasbôas 2 ; Pollyana Notargiacomo 1 and Leandro Nunes de Castro 2

Affiliations: 1 Games, Learning, Simulation, Systems and Signals Laboratory (JAS3), Graduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, Rua da Consolação 930, São Paulo and Brazil ; 2 Natural Computing and Machine Learning Laboratory (LCoN), Graduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, Rua da Consolação 930, São Paulo and Brazil

Keyword(s): Software Agents, Data Mining, Association Rule Mining, Apriori, Erp Systems, Computational Performance.

Related Ontology Subjects/Areas/Topics: Agent Models and Architectures ; Agents ; Artificial Intelligence ; Computational Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

Abstract: Deployment flexibility, low development cost, and value-adding tools are some of the features that developers are looking for in ERP systems. Modularization through software agents is one way of achieving these objectives. In this sense, the present paper proposes the planning, implementation and integration of a software agent for association rule mining into an ERP system. The development and use of tools for all Knowledge Discovery in Databases (KDD) phases (pre-processing, data mining and post-processing), will be presented. This includes input data, file loading for the agent processing, use of the Apriori association rule mining algorithm, generation of output files with association rules, use of agent outputs for database storage and use of the stored data by the item recommendation tool. Experiments were carried out focusing the assessment of the running profile for databases of different sizes and using different computational architectures.

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Paper citation in several formats:
Marin Machado de Souza, R. ; Vilasbôas, F. ; Notargiacomo, P. and Nunes de Castro, L. (2019). Integrating an Association Rule Mining Agent in an ERP System: A Proposal and a Computational Scalability Analysis. In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-350-6; ISSN 2184-433X, SciTePress, pages 778-786. DOI: 10.5220/0007483307780786

@conference{icaart19,
author={Rafael {Marin Machado de Souza} and Fabrício Gomes Vilasbôas and Pollyana Notargiacomo and Leandro {Nunes de Castro}},
title={Integrating an Association Rule Mining Agent in an ERP System: A Proposal and a Computational Scalability Analysis},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2019},
pages={778-786},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007483307780786},
isbn={978-989-758-350-6},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Integrating an Association Rule Mining Agent in an ERP System: A Proposal and a Computational Scalability Analysis
SN - 978-989-758-350-6
IS - 2184-433X
AU - Marin Machado de Souza, R.
AU - Vilasbôas, F.
AU - Notargiacomo, P.
AU - Nunes de Castro, L.
PY - 2019
SP - 778
EP - 786
DO - 10.5220/0007483307780786
PB - SciTePress

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