A review of big data environment and its related technologies

Mohammed, Ahmed Fuad; Humbe, Vikas T.; Chowhan, S. S.

doi:10.1109/icices.2016.7518904

Cited by 16 publications

(5 citation statements)

References 10 publications

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“…Therefore, it is necessary to integrate more information to the analysis architecture, as universities generally store the socioeconomic information of students and in some cases include relevant information on the academic performance of basic training institutions. This information allows the discovery of possible trends in the students and the way in which they learn [38]. All the aforementioned refers to structured data; however, this work aims to obtain information from students through all available sources, such as social networks.…”

Section: Analysis Of Datamentioning

confidence: 99%

Improvement of an Online Education Model with the Integration of Machine Learning and Data Analysis in an LMS

2020

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The events that took place in the year 2020 have shown us that society is still fragile and that it is exposed to events that rapidly change the paradigms that govern it. This has been shown by a pandemic like Coronavirus disease 2019; this global emergency has changed the way people interact, communicate, study, or work. In short, the way in which society carries out all activities has changed. This includes education, which has bet on the use of information and communication technologies to reach students. An example of the aforementioned is the use of learning management systems, which have become ideal environments for resource management and the development of activities. This work proposes the integration of technologies, such as artificial intelligence and data analysis, with learning management systems in order to improve learning. This objective is outlined in a new normality that seeks robust educational models, where certain activities are carried out in an online mode, surrounded by technologies that allow students to have virtual assistants to guide them in their learning.

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Section: Analysis Of Datamentioning

confidence: 99%

Improvement of an Online Education Model with the Integration of Machine Learning and Data Analysis in an LMS

2020

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“…Indeed, many researchers have worked on big data, particularly on its value chain and on its processing tools. To start with, Mohammed, Humbe, and Chowhan (2016) [10] aimed to provide an overview of the big data context. They defined the big data value chain in 5V.…”

Section: Related Workmentioning

confidence: 99%

Meta-modeling of Big Data management layer

Erraissi¹

2019

IJETER

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Nowadays, the magnitude of data generated daily through the technological environment has increased enormously. This massive amount of heterogeneous data led to the emergence of a large number of big data systems and technologies that share similar architectures but with different implementations. In essence, the common architecture is composed of many components: Data sources, Ingestion, Hadoop Storage, Hadoop Platform management, Visualization, Monitoring, and Security Layers. In our way for a unified abstract implementation, we proposed, in previous works, meta-models for data sources, ingestion, storage, and visualization layers. We also relied on our previous comparatives studies to define key concepts of management layer in Big Data. Thus, we shall present in this paper our meta-model for management layer in Big Data by applying techniques related to Model Driven Engineering. The main goal of this universal meta-modeling is to enable Big Data distribution providers to offer standard and unified solutions for a Big Data system.

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“…Smartphones are not the only means of collecting data. IoT includes numerous forms of data collection such as appliances, watches, smart vehicles, and sensor networks [16]. The volume of data being collected and subsequently analyzed is growing exponentially [17].…”

Section: Big Data Processingmentioning

confidence: 99%

Supporting Big Data at the vehicular edge

Decker

Olariu

2019

Big Data-Enabled Internet of Things

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these resources as long as they are mobile. The question to ask is whether these resources could be put to use when the vehicle is not mobile. If the vehicle is parked, the resources are simply dormant and waiting for use. If the vehicle has a connection to a larger computing infrastructure, then it can put its resources towards that infrastructure. With enough vehicles interconnected, there exists a computing environment that could handle many cloud-based application services. If these vehicles were electric, then they could in return receive electrical charging services. This Thesis will develop a simple vehicle datacenter solution based upon Smart Vehicles in a parking lot. While previous work has developed similar models based upon the idea of migration of jobs due to residency of the vehicles, this model will assume that residency times cannot be predicted and therefore no migration is utilized. In order to offset the migration of jobs, a divide-and-conquer approach is created. This uses a MapReduce process to divide the job into numerous sub-jobs and process the subtask in parallel. Finally, a checkpoint will be used between the Map and Reduce phase to avoid loss of intermediate data. This will serve as a means to test the practicality of the model and create a baseline for comparison with future research.

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A review of big data environment and its related technologies

Cited by 16 publications

References 10 publications

Improvement of an Online Education Model with the Integration of Machine Learning and Data Analysis in an LMS

Improvement of an Online Education Model with the Integration of Machine Learning and Data Analysis in an LMS

Meta-modeling of Big Data management layer

Supporting Big Data at the vehicular edge

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