A Machine Hearing Framework for Real-Time Streaming Analytics Using Lambda Architecture

Demertzis, Konstantinos; Iliadis, Lazaros; Anezakis, Vardis-Dimitris

doi:10.1007/978-3-030-20257-6_21

Cited by 3 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This design of data ingestion architecture for learning analytics was done based on the types of devices available for data collection and the different sources of data generated inside academic institutions. For the purposes of this paper, we analyzed studies as a result of which is a big data ingestion architecture tailored for learning analytics has been proposed based on the lambda architecture [20]. The proposed data ingestion architecture was based on a review of recent literature and adapts best practices, guidelines, and techniques to meet the demand of current big-data ingestion issues.…”

Section: Methodsmentioning

confidence: 99%

Learning analytics for higher education: proposal of big data ingestion architecture

Amare

Šimonová

2021

SHS Web of Conf.

View full text Add to dashboard Cite

Research background: Higher education institutions are generating multiple formats of data from diverse sources across the globe. The data ingestion layer is responsible for collecting data and transform for analysis. Learning analytics plays a vital role in providing decision-making support and selection of suitable timely intervention. The lack of tailored big-data ingestion architectures for academics led to several implementation challenges. Purpose of the article: The purpose of this article is to propose data ingestion architecture enabled for big data learning analytics. Methods: The study reviews existing literature to examine big-data ingestion tools and frameworks; and identify big-data ingestion challenges. An optimized framework for the real world learning analytics application was not yet in place at global higher educations. Consequently, the big-data ingestion pipeline is experiencing challenges of inefficient and complex data access, slow processing time, and security issues associated with transferring data to the system. The proposed data ingestion architecture is based on review of recent literature and adapts best international practices, guidelines, and techniques to meet the demand of current big-data ingestion issues. Findings & value added: This study identifies the current global challenges in implementing learning analytics projects. Review of recent big data ingestion techniques has been done based on defined metrics tuned for learning analytics purposes. The proposed data ingestion framework would increase the effectiveness of collecting, importing, processing and storing of learning data. Besides, the proposed architecture contributes to the construction of full-fledged big-data learning analytics ecosystem of higher educations.

show abstract

Section: Methodsmentioning

confidence: 99%

Learning analytics for higher education: proposal of big data ingestion architecture

Amare

Šimonová

2021

SHS Web of Conf.

View full text Add to dashboard Cite

show abstract

A Novel Distributed Software Architecture for Managing Customer Behavior Data: A Case Study in Banking Sector

Kargili¹,

Arik

Bekler³

et al. 2021

2021 21st International Conference on Computational Science and Its Applications (ICCSA)

View full text Add to dashboard Cite

Data Analytics for Climate and Atmospheric Science

Demertzis

Demertzi

Demertzis

2021

Int. J. Big Data Mini. Glob. Warm.

View full text Add to dashboard Cite

Global climate change has already had observable effects on the environment. Glaciers have shrunk, ice on rivers and lakes is breaking up earlier, plant and animal ranges have shifted and trees are flowering sooner. Under these conditions, air pollution is likely to reach levels that create undesirable living conditions. Anthropogenic activities, such as industry, release large amounts of greenhouse gases into the atmosphere, increasing the atmospheric concentrations of these gases, thus significantly enhancing the greenhouse effect, which has the effect of increasing air heat and thus the speedup of climate change. The use of sophisticated data analysis methods to identify the causes of extreme pollutant values, the correlation of these values with the general climatic conditions and the general malfunctions that can be caused by prolonged air pollution can give a clear picture of current and future climate change. This paper presents a thorough study of preprocessing steps of data analytics and the appropriate big data architectures that are appropriate for the research study of Climate Change and Atmospheric Science.

show abstract

A Machine Hearing Framework for Real-Time Streaming Analytics Using Lambda Architecture

Cited by 3 publications

References 30 publications

Learning analytics for higher education: proposal of big data ingestion architecture

Learning analytics for higher education: proposal of big data ingestion architecture

A Novel Distributed Software Architecture for Managing Customer Behavior Data: A Case Study in Banking Sector

Data Analytics for Climate and Atmospheric Science

Contact Info

Product

Resources

About