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Authors: Eduardo Duarte 1 ; Diogo Gomes 2 ; David Campos 3 and Rui Aguiar 4

Affiliations: 1 Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro and Portugal ; 2 Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal, Institute of Telecommunications, University of Aveiro, Aveiro and Portugal ; 3 Bosch Thermotechnology, Aveiro 3800-533 and Portugal ; 4 Institute of Telecommunications, University of Aveiro, Aveiro and Portugal

ISBN: 978-989-758-377-3

Keyword(s): Time Series, Annotations, Annotation Systems, Collaborative Software, Data Analysis, Information Science, Data Modeling, Knowledge Management, Database Management Systems, Distributed Systems, Information Visualization.

Abstract: The recent expansion of metrification on a daily basis has led to the production of massive quantities of data, which in many cases correspond to time series. To streamline the discovery and sharing of meaningful information within time series, a multitude of analysis software tools were developed. However, these tools lack appropriate mechanisms to handle massive time series data sets and large quantities of simultaneous requests, as well as suitable visual representations for annotated data. We propose a distributed, scalable, secure and high-performant architecture that allows a group of researchers to curate a mutual knowledge base deployed over a network and to annotate patterns while preventing data loss from overlapping contributions or unsanctioned changes. Analysts can share annotation projects with peers over a reactive web interface with a customizable workspace. Annotations can express meaning not only over a segment of time but also over a subset of the series that coexis t in the same segment. In order to reduce visual clutter and improve readability, we propose a novel visual encoding where annotations are rendered as arcs traced only over the affected curves. The performance of the prototype under different architectural approaches was benchmarked. (More)

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Paper citation in several formats:
Duarte, E.; Gomes, D.; Campos, D. and Aguiar, R. (2019). Distributed and Scalable Platform for Collaborative Analysis of Massive Time Series Data Sets.In Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-377-3, pages 41-52. DOI: 10.5220/0007834700410052

@conference{data19,
author={Eduardo Duarte. and Diogo Gomes. and David Campos. and Rui L. Aguiar.},
title={Distributed and Scalable Platform for Collaborative Analysis of Massive Time Series Data Sets},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2019},
pages={41-52},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007834700410052},
isbn={978-989-758-377-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Distributed and Scalable Platform for Collaborative Analysis of Massive Time Series Data Sets
SN - 978-989-758-377-3
AU - Duarte, E.
AU - Gomes, D.
AU - Campos, D.
AU - Aguiar, R.
PY - 2019
SP - 41
EP - 52
DO - 10.5220/0007834700410052

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