HAIVAN: a Holistic  ML Analytics Infrastructure for a Variety of Radio Access Networks

Linh Truong; Nguyen Ngoc Nhu Trang

doi:10.1109/BigData55660.2022.10020515

HAIVAN: a Holistic ML Analytics Infrastructure for a Variety of Radio Access Networks

Linh Truong, Nguyen Ngoc Nhu Trang

MobiFone Corporation

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

228 Lataukset (Pure)

Abstrakti

This paper presents our approach for supporting machine learning (ML)-based analytics of quality of experience (QoE) related issues in a variety of Radio Access Networks (V-RAN). We focus on key problems in a holistic analytics infrastructure for engineers without strong ML skills and powerful computing infrastructures. We characterize types of relevant data and existing data systems to follow a specific data mesh approach suitable for engineers. The paper presents key steps in establishing the participation of engineers and the acquisition of domain knowledge. We introduce models for representing analytics subjects and their dependencies, and for managing relevant ML techniques and methods for analytics subjects. We explain our work through examples from a large-scale mobile network of approximately 4 million subscribers.

Alkuperäiskieli	Englanti
Otsikko	2022 IEEE International Conference on Big Data (IEEE BigData 2022)
Toimittajat	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Kustantaja	IEEE
Sivut	2389-2393
Sivumäärä	5
ISBN (elektroninen)	978-1-6654-8045-1
ISBN (painettu)	978-1-6654-8046-8
DOI - pysyväislinkit	https://doi.org/10.1109/BigData55660.2022.10020515
Tila	Julkaistu - 2023
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Conference on Big Data - Osaka, Japani Kesto: 17 jouluk. 2022 → 20 jouluk. 2022

Conference

Conference	IEEE International Conference on Big Data
Lyhennettä	BigData
Maa/Alue	Japani
Kaupunki	Osaka
Ajanjakso	17/12/2022 → 20/12/2022

Pääsy asiakirjaan

10.1109/BigData55660.2022.10020515

HAIVAN_accepted_versionAccepted author manuscript, 1,9 MB

Muut tiedostot ja linkit

Linkki julkaisuun Scopuksessa

Siteeraa tätä

Truong, L., & Nhu Trang, N. N. (2023). HAIVAN: a Holistic ML Analytics Infrastructure for a Variety of Radio Access Networks. teoksessa S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Toimittajat), 2022 IEEE International Conference on Big Data (IEEE BigData 2022) (Sivut 2389-2393). IEEE. https://doi.org/10.1109/BigData55660.2022.10020515

Truong, Linh ; Nhu Trang, Nguyen Ngoc. / HAIVAN: a Holistic ML Analytics Infrastructure for a Variety of Radio Access Networks. 2022 IEEE International Conference on Big Data (IEEE BigData 2022). Toimittaja / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. IEEE, 2023. Sivut 2389-2393

@inproceedings{eeafed70d4c6446b9605b17a685101fe,

title = "HAIVAN: a Holistic ML Analytics Infrastructure for a Variety of Radio Access Networks",

abstract = "This paper presents our approach for supporting machine learning (ML)-based analytics of quality of experience (QoE) related issues in a variety of Radio Access Networks (V-RAN). We focus on key problems in a holistic analytics infrastructure for engineers without strong ML skills and powerful computing infrastructures. We characterize types of relevant data and existing data systems to follow a specific data mesh approach suitable for engineers. The paper presents key steps in establishing the participation of engineers and the acquisition of domain knowledge. We introduce models for representing analytics subjects and their dependencies, and for managing relevant ML techniques and methods for analytics subjects. We explain our work through examples from a large-scale mobile network of approximately 4 million subscribers.",

author = "Linh Truong and {Nhu Trang}, {Nguyen Ngoc}",

year = "2023",

doi = "10.1109/BigData55660.2022.10020515",

language = "English",

isbn = "978-1-6654-8046-8",

pages = "2389--2393",

editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",

booktitle = "2022 IEEE International Conference on Big Data (IEEE BigData 2022)",

publisher = "IEEE",

address = "United States",

note = "IEEE International Conference on Big Data, BigData ; Conference date: 17-12-2022 Through 20-12-2022",

}

Truong, L & Nhu Trang, NN 2023, HAIVAN: a Holistic ML Analytics Infrastructure for a Variety of Radio Access Networks. julkaisussa S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (toim), 2022 IEEE International Conference on Big Data (IEEE BigData 2022). IEEE, Sivut 2389-2393, IEEE International Conference on Big Data, Osaka, Japani, 17/12/2022. https://doi.org/10.1109/BigData55660.2022.10020515

HAIVAN: a Holistic ML Analytics Infrastructure for a Variety of Radio Access Networks. / Truong, Linh; Nhu Trang, Nguyen Ngoc.
2022 IEEE International Conference on Big Data (IEEE BigData 2022). toim. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. IEEE, 2023. s. 2389-2393.

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - HAIVAN: a Holistic ML Analytics Infrastructure for a Variety of Radio Access Networks

AU - Truong, Linh

AU - Nhu Trang, Nguyen Ngoc

PY - 2023

Y1 - 2023

N2 - This paper presents our approach for supporting machine learning (ML)-based analytics of quality of experience (QoE) related issues in a variety of Radio Access Networks (V-RAN). We focus on key problems in a holistic analytics infrastructure for engineers without strong ML skills and powerful computing infrastructures. We characterize types of relevant data and existing data systems to follow a specific data mesh approach suitable for engineers. The paper presents key steps in establishing the participation of engineers and the acquisition of domain knowledge. We introduce models for representing analytics subjects and their dependencies, and for managing relevant ML techniques and methods for analytics subjects. We explain our work through examples from a large-scale mobile network of approximately 4 million subscribers.

AB - This paper presents our approach for supporting machine learning (ML)-based analytics of quality of experience (QoE) related issues in a variety of Radio Access Networks (V-RAN). We focus on key problems in a holistic analytics infrastructure for engineers without strong ML skills and powerful computing infrastructures. We characterize types of relevant data and existing data systems to follow a specific data mesh approach suitable for engineers. The paper presents key steps in establishing the participation of engineers and the acquisition of domain knowledge. We introduce models for representing analytics subjects and their dependencies, and for managing relevant ML techniques and methods for analytics subjects. We explain our work through examples from a large-scale mobile network of approximately 4 million subscribers.

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U2 - 10.1109/BigData55660.2022.10020515

DO - 10.1109/BigData55660.2022.10020515

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SP - 2389

EP - 2393

BT - 2022 IEEE International Conference on Big Data (IEEE BigData 2022)

A2 - Tsumoto, Shusaku

A2 - Ohsawa, Yukio

A2 - Chen, Lei

A2 - Van den Poel, Dirk

A2 - Hu, Xiaohua

A2 - Motomura, Yoichi

A2 - Takagi, Takuya

A2 - Wu, Lingfei

A2 - Xie, Ying

A2 - Abe, Akihiro

A2 - Raghavan, Vijay

PB - IEEE

T2 - IEEE International Conference on Big Data

Y2 - 17 December 2022 through 20 December 2022

ER -

Truong L, Nhu Trang NN. HAIVAN: a Holistic ML Analytics Infrastructure for a Variety of Radio Access Networks. julkaisussa Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, toimittajat, 2022 IEEE International Conference on Big Data (IEEE BigData 2022). IEEE. 2023. s. 2389-2393 doi: 10.1109/BigData55660.2022.10020515