02063nas a2200169   4500008004100000245010600041210006900147260001200216300001200228490000700240520150900247100002001756700002301776700002201799700001601821856005601837       2019                        eng d00aAn Agent-Based Approach for Extracting Business Association Rules from Centralized Databases Systems-0 aAgentBased Approach for Extracting Business Association Rules fr  c10/2019  a270-2880 v173 a<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>
1 aMesbahi, Nadjib1 aZoubeidi, Merouane1 aMerizig, Abdelhak1 aKazar, Okba  uhttp://dline.info/fpaper/jdim/v17i5/jdimv17i5_2.pdf01752nas a2200169   4500008004100000245008000041210006900121260001000190300001200200490000700212520123000219100002001449700001601469700001901485700002201504856005601526       2018                        eng d00aNew approach for intrusion detection in Big Data as a service in the Cloud-0 aNew approach for intrusion detection in Big Data as a service in  c10/12  a258-2700 v163 a<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>
1 aKassimi, Dounya1 aKazar, Okba1 aBoussaid, Omar1 aMerizig, Abdelhak  uhttp://dline.info/fpaper/jdim/v16i5/jdimv16i5_5.pdf00492nas a2200157   4500008004100000245007900041210006900120260001200189300001200201490000700213100002000220700001600240700001900256700002200275856003700297       2018                        eng d00aNew approach for intrusion detection in Big Data as a service in the Cloud0 aNew approach for intrusion detection in Big Data as a service in  c10/2018  a258-2710 v161 aKassimi, Dounya1 aKazar, Okba1 aBoussaid, Omar1 aMerizig, Abdelhak  uhttps://dline.info/newl/node/700