<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the development and expansion of the Internet of Things, computing at the edge is becoming increasingly important, especially for applications where real-time response is important. In this paper, we made a systematic review of the literature on analytics at the edge. We extracted data from 40 selected primary relevant studies from the complete set of 419 papers retrieved from scientific databases. In our analysis of the full text of every primary study we investigated: temporal distribution of primary studies, publication types, domain and application areas of the primary papers, used machine learning and deep learning methods. We also elaborated on the main themes of the primary studies and recommended some possible interesting future research possibilities.</p>
</abstract>

With the development and expansion of the Internet of Things, computing at the edge is becoming increasingly important, especially for applications where real-time response is important. In this paper, we made a systematic review of the literature on analytics at the edge. We extracted data from 40 selected primary relevant studies from the complete set of 419 papers retrieved from scientific databases. In our analysis of the full text of every primary study we investigated: temporal distribution of primary studies, publication types, domain and application areas of the primary papers, used machine learning and deep learning methods. We also elaborated on the main themes of the primary studies and recommended some possible interesting future research possibilities.

Applications of edge analytics: a systematic review

Acta Universitatis Sapientiae, Informatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Applications of edge analytics: a systematic review"}

With the development and expansion of the Internet of Things, computing at the edge is becoming increasingly important, especially for applications where...

Applications of edge analytics: a systematic review

Online veröffentlicht: 12. Dez. 2023

Seitenbereich: 345 - 358

Eingereicht: 22. Sept. 2023

DOI: https://doi.org/10.2478/ausi-2023-0021

Schlüsselwörter
machine learning, toxic comment, deep learning, systematic review

© 2023 Darko Andročec, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Applications of edge analytics: a systematic review

Online veröffentlicht: 12. Dez. 2023

Seitenbereich: 345 - 358

Eingereicht: 22. Sept. 2023

DOI: https://doi.org/10.2478/ausi-2023-0021

Schlüsselwörtermachine learning, toxic comment, deep learning, systematic review

© 2023 Darko Andročec, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
machine learning, toxic comment, deep learning, systematic review