Open Access

ARTICLE

Implementation of Artificial Intelligence Based Analyzer Using Multi-Agent System Approach

by Norah S. Farooqi¹, Mohamed O. Khozium^2,*

1 College of Computer and Information Systems, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
2 Department of HITM, Faculty of Public Health and Health Informatics, ICRS Consultant, Umm Al-Qura University, Makkah, 21955, Saudi Arabia

* Corresponding Author: Mohamed O. Khozium. Email:

Intelligent Automation & Soft Computing 2022, 31(1), 297-309. https://doi.org/10.32604/iasc.2022.019060

Received 31 March 2021; Accepted 23 May 2021; Issue published 03 September 2021

Abstract

Using Business Intelligence (BI) applications is a critical factor for modern enterprises’ success. BI is one of the key components that persistently required for the modern high-tech companies and industries were used to handle huge amounts of data in every minute of the operations. The existing literature suggested that the lack of dynamic decision making, accuracy, and the degree of flexibility are the key limitations for handling the operational data. Many industries and companies adopted the software-based solution; however, the intelligence is there due to the dependence of the operational engagement for each of the sectors. Therefore, artificial intelligence business framework is urge to implement in the industrial and company’s larger data handling and dynamic decision making that should have the multi-agent system adopting business analyzer model. Towards developing a more universal and cooperative platform, this paper proposes a business Analyzer model. It implements it in an Analyzer agent that can functionally participate in a BI Multi-Agent System (MAS). Unlike previous analyzers which are usually included in other services, this Analyzer is a stand-alone agent that adds an abstraction level to the BI model processing. We have started by defining a BI model using MAS. Building on that, we have created the Analyzer model. Then, we have suggested the Artificial Intelligence (AI) techniques that can satisfy the Analyzer model requirements and implemented it in an Analyzer agent. The functionality and results are promising. This paper opens the road for the researchers to proceed toward universal BI model and the opportunity to implement new BI services.

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Keywords

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