@article {793,
	title = {An Agent-Based Approach for Extracting Business Association Rules from Centralized Databases Systems-},
	journal = {Journal of Digital Information Management},
	volume = {17},
	year = {2019},
	month = {10/2019},
	pages = {270-288},
	type = {Research},
	chapter = {270},
	abstract = {<p>Today, enterprises use a variety of applications to manage day-by-day business activities using a large centralized database. Since a huge amount of data stored in this centralized database produced by the daily use of several systems, it is important to integrate decision-making tools to analyse and interpret these business data. For this purpose, Data Mining is a powerful technology that promote information and knowledge extraction from large databases. In this paper, we present an agent-based approach for extracting business association rules from centralized database systems. This approach combine paradigm of multi-agent system and the association rules as a data mining technique to build anefficient model. It is relying on the intelligent partitioning of data to make the execution of business association rules in a parallel and distributed way from a large centralized database. To validate our approach, we applied it during the realization of a real case study on ERP database at the National company of Well Services (ENSP)using JADE platform with machine learning WEKA toolbox for association rules mining. The developed system has been compared with the classic association rules algorithms and has proved it is more efficient and more scalable. The main objective of our work is to improve and accelerate the process of extracting association rules by business through centralized database systems. As a result, the decision process of these systems becomes more improved.</p>
},
	doi = {https://doi.org/10.6025/jdim/2019/17/5/270-288},
	url = {http://dline.info/fpaper/jdim/v17i5/jdimv17i5_2.pdf},
	author = {Nadjib Mesbahi and Merouane Zoubeidi and Abdelhak Merizig and Okba Kazar}
}
@article {820,
	title = {New approach for intrusion detection in Big Data as a service in the Cloud-},
	journal = {Journal of Digital Information Management},
	volume = {16},
	year = {2018},
	month = {10/12},
	pages = {258-270},
	type = {Research},
	chapter = {258},
	abstract = {<p>\&nbsp;Nowadays, Big Data has reached every area of our lives because it covers many tasks in different operation. This new technique forces the cloud computing to use it as a layer, for this reason cloud technology embraces it as Big Data as a service (BDAAS). After solving the problem of storing huge volumes of information circulating on the Internet, remains to us how we can protect and ensure that this information are stored\&nbsp; without loss or distortion. The aim of this paper is to study the problem of safety in BDAAS, inparticularly we will cover the problem of Intrusion detection system (IDS). In order to solve the problems tackled in this paper, we have proposed a Self-Learning Autonomous Intrusion Detection system (SLA-IDS) which is based on the architecture ofautonomic system to detect the anomaly data. In this approach, to add the autonomy aspect to the proposed system we have used mobile and situated agents. The implementation of this model has been provided to evaluate our system. The obtained findings show the effectiveness of our proposed model. We validate our proposition using Hadoop as Big Data Platform and CloudSim, machine learning Weka with java to create Model of detection.</p>
},
	doi = {https://doi.org/10.6025/jdim/2018/16/5/258-270},
	url = {http://dline.info/fpaper/jdim/v16i5/jdimv16i5_5.pdf},
	author = {Dounya Kassimi and Okba Kazar and Omar Boussaid and Abdelhak Merizig}
}
@article {700,
	title = {New approach for intrusion detection in Big Data as a service in the Cloud},
	journal = {Journal of Digital Information Management},
	volume = {16},
	year = {2018},
	month = {10/2018},
	pages = {258-271},
	type = {Research},
	chapter = {258},
	doi = {https://doi.org/10.6025/jdim/2018/5/258-270},
	author = {Dounya Kassimi and Okba Kazar and Omar Boussaid and Abdelhak Merizig}
}
