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

Farzana Anowar, Farzana Anowar, Samira Sadaoui, Hardik Dalal

2022

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.

Paper Citation

in Harvard Style

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, pages 183-192. DOI: 10.5220/0010801400003116

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Anowar F.
AU - Sadaoui S.
AU - Dalal H.
PY - 2022
SP - 183
EP - 192
DO - 10.5220/0010801400003116