A Scalable Business Intelligence Decision-Making System in the Era of Big Data

Kalna, Fatima; Erraissi, Allae; Banane, Mouad; Belangour, Abdessamad

doi:10.35940/ijitee.l3251.1081219

Cited by 12 publications

(3 citation statements)

References 18 publications

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“…The meta-model that we proposed for this layer and the other layers is platform-independent according to Model-Driven Architecture pattern. By using the ATL transformation language, for example, we can move to the other platform-specific models (PSM) [59,60]. The PSM we have chosen in this work are LogRhythm's Security Intelligence Platform, IBM QRadar, RSA Security, and Hawkeye Analytics Platform.…”

Section: Discussion and Perspectivesmentioning

confidence: 99%

A Big Data Security Layer Meta-Model Proposition

Erraissi¹,

Belangour²

2019

Adv. sci. technol. eng. syst. j.

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In Big Data, several solution providers offer distributions to handle this large amount of data. Given the variety of these solutions, we are working to provide universal metamodeling for all layers of a Big Data system in order to address the issue of interoperability and portability between these solutions. As part of our continuous efforts to standardize concepts in Big Data world, we apply in this paper techniques related to Model-Driven Engineering "MDE" to propose a meta-model for the security layer in Big Data. This metamodel with the others that we have already proposed for the other layers of the Big Data system, will be used as a platform-independent according to Model-Driven Architecture pattern, which describes the structures of Big Data layers independently of any specific platform.

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Section: Discussion and Perspectivesmentioning

confidence: 99%

A Big Data Security Layer Meta-Model Proposition

Erraissi¹,

Belangour²

2019

Adv. sci. technol. eng. syst. j.

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“…This enormous volume of data, known by the term "Big Data" [34,35], allows, on the one hand, to have an incredible wealth in terms of information allowing the offer of advanced services. On the other hand, this volume of data creates new challenges to be considered such as securing, processing, and real-time accessibility of this data [36,37].…”

Section: A Iotmentioning

confidence: 99%

A Comparative Study of Microservices-based IoT Platforms

Khalyly¹,

Belangour²,

Banane³

et al. 2020

IJACSA

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Internet of Things (IoT) is a set of technologies that aim at fitting together smart devices and applications to build an IoT ecosystem. The target of this kind of ecosystem is to enhance interaction between machines and humans through hardware to software binding while reducing cost and resource consumption. On the application level, IoT ecosystems were implemented by various technologies that all seek better interconnection, monitoring, and controlling of IoT smart devices. Among recent technologies, Microservices, which are a variant of the serviceoriented architecture, are subject to great excitement. In fact, Microservices are an emerging technology built around Microservice paradigm which goal is to offer services with a small granularity, which exactly meets the distributed nature of IoT devices while maintaining a loosely coupled architecture between IoT components among other advantages. Efforts to build Microservice-based IoT platforms sooner emerged to take advantage of the numerous benefits of the Microservice paradigm to build scalable, interoperable, and dynamic ecosystems. The goal of this paper is to list these approaches, classify them and compare them using a Weighted Scoring Model (WSM) method. This involves, studying these platforms, establishing relevant criteria for comparison, assigning weights for each criterion, and finally calculating scores. The obtained results reveal the weaknesses and strengths of each of the studied platforms.

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“…This enormous volume of data, known by the term "Big Data" [38,39,57,58], allows, on the one hand, to have an incredible wealth in terms of information allowing the offer of advanced services. On the other hand, this volume of data creates new challenges to be considered such as securing, processing, and real-time accessibility of this data [40,41]. To end this overview, we must mention the metalisms and meta-modeling languages offered tools and meta-CASE type environment.…”

Section: Related Workmentioning

confidence: 99%

Devops and Microservices Based Internet of Things Meta-Model

2020

IJETER

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The ecosystem of the Internet of Things is a set of physical devices such as sensors and actuators. It includes a set of servers and gateways that provide connectivity. These devices can be installed at three levels: the edge level, the fog level, and the cloud level. Applications that monitor things and collect data from sensors are deployed at the edge, fog, and cloud levels. These applications can be containerized and deployed in different devices by Docker, which takes advantage of its advantage to create executable containers that are isolated from each other. This paper presents a generic Meta-model of the Internet of Things ecosystem based on microservices and supported by Docker as a containerization tool, Ansible as a monitoring tool and Kubernetes as scaling tool. This Meta-model allows us to generate a system of connected objects that can be deployed on three levels: Fog, Edge, Cloud. This Meta-model can be used in different types of domains such as Smart Home -Smart City -Smart Vehicle -Smart Health -Smart Farm -Smart Factory. The Meta-model proposed in this article is a fusion between 5 Meta-models: Internet of Things -Microservices -Ansible -Docker -Kubernetes.

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A Scalable Business Intelligence Decision-Making System in the Era of Big Data

Cited by 12 publications

References 18 publications

A Big Data Security Layer Meta-Model Proposition

A Big Data Security Layer Meta-Model Proposition

A Comparative Study of Microservices-based IoT Platforms

Devops and Microservices Based Internet of Things Meta-Model

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