loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

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): Service Monitoring, High-dimensional Time-series, Dimensionality Reduction, Incremental Dimensionality Reduction, Clustering Quality, Data Reconstruction.

Abstract: Our work introduces an ensemble-based dimensionality reduction approach to efficiently address the high dimensionality of an industrial unlabeled time-series dataset, intending to produce robust data labels. The ensemble comprises a self-supervised learning method to improve data quality, an unsupervised dimensionality reduction to lower the ample feature space, and a chunk-based incremental dimensionality reduction to further increase confidence in data labels. Since the time-series dataset is massive, we divide it into several chunks and evaluate each chunk’s quality using time-series clustering method and metrics. The experiments reveal that clustering performances increased significantly for all the chunks after performing the ensemble approach.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.147.61.180

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Anowar, F.; Sadaoui, S. and Dalal, H. (2022). An Ensemble-based Dimensionality Reduction for Service Monitoring Time-series. In Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - DeLTA; ISBN 978-989-758-584-5; ISSN 2184-9277, SciTePress, pages 117-124. DOI: 10.5220/0011273700003277

@conference{delta22,
author={Farzana Anowar. and Samira Sadaoui. and Hardik Dalal.},
title={An Ensemble-based Dimensionality Reduction for Service Monitoring Time-series},
booktitle={Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - DeLTA},
year={2022},
pages={117-124},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011273700003277},
isbn={978-989-758-584-5},
issn={2184-9277},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - DeLTA
TI - An Ensemble-based Dimensionality Reduction for Service Monitoring Time-series
SN - 978-989-758-584-5
IS - 2184-9277
AU - Anowar, F.
AU - Sadaoui, S.
AU - Dalal, H.
PY - 2022
SP - 117
EP - 124
DO - 10.5220/0011273700003277
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic