Clustering Quality of a High-dimensional Service Monitoring Time-series Dataset

Farzana Anowar; Farzana Anowar; Samira Sadaoui; Hardik Dalal

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Paper

Clustering Quality of a High-dimensional Service Monitoring Time-series Dataset

Topics: Big Data; Data Mining; Data Science; Deep Learning; Industrial Applications of AI; Machine Learning

In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, 183-192, 2022

Authors: Farzana Anowar ^{1

;

2} ; Samira Sadaoui ² and Hardik Dalal ¹

Affiliations: ¹ Ericsson Canada Inc., Montreal, Canada ; ² University of Regina, Regina, Canada

Keyword(s): High-dimensional Time-series Dataset, Clustering Quality, Data Clustering, Data Imputation, Deep Learning.

Abstract: Our study evaluates the quality of a high-dimensional time-series dataset gathered from service observability and monitoring application. We construct the target dataset by extracting heterogeneous sub-datasets from many servers, tackling data incompleteness in each sub-dataset using several imputation techniques, and fusing all the optimally imputed sub-datasets. Based on robust data clustering approaches and metrics, we thoroughly assess the quality of the initial dataset and the reconstructed datasets produced with Deep and Convolutional AutoEncoders. The experiments reveal that the Deep AutoEncoder dataset’s performances outperform the initial dataset’s performances.

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Paper citation in several formats:

Anowar, F.; Sadaoui, S. and Dalal, H. (2022). Clustering Quality of a High-dimensional Service Monitoring Time-series Dataset. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 183-192. DOI: 10.5220/0010801400003116

@conference{icaart22,
author={Farzana Anowar. and Samira Sadaoui. and Hardik Dalal.},
title={Clustering Quality of a High-dimensional Service Monitoring Time-series Dataset},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2022},
pages={183-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010801400003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Clustering Quality of a High-dimensional Service Monitoring Time-series Dataset
SN - 978-989-758-547-0
IS - 2184-433X
AU - Anowar, F.
AU - Sadaoui, S.
AU - Dalal, H.
PY - 2022
SP - 183
EP - 192
DO - 10.5220/0010801400003116
PB - SciTePress