Distributed and Scalable Platform for Collaborative Analysis of Massive Time Series Data Sets

Eduardo Duarte, Diogo Gomes, David Campos, Rui L. Aguiar

2019

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 coexist 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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@conference{data19,
author={Eduardo Duarte and Diogo Gomes and David Campos and Rui 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},
}

in EndNote Style

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