Domain Ontology for Time Series Provenance

Lucélia de Souza, Maria Salete Marcon Gomes Vaz, Marcos Sfair Sunye

Abstract

Time series data are generated all the time with a volume without precedent, constituting themselves of a points sequence spread out over time, usually at time regular intervals. Time series analysis is different from data analysis, given its intrinsic nature, where observations are dependent and the observations order is important for analysis. The knowledge about the data which will be analyzed is relevant in an analysis process, but this knowledge is not always explicit and easy to interpret in many information resources. Time series can be semantically enriched where provenance information using ontologies allows to representing and inferring knowledge. The main contribution of this paper is to present a domain ontology developed by modular design for time series provenance, which adds semantic knowledge and contributes to the choice of appropriate statistical methods for an important step of time series analysis that is the trend extraction (detrending). Trend is a time series component that needs be extracted because it can hide other phenomena, as well as the most statistical methods are developed for stationary time series. With this work, is intended to contribute for semantically improving the decision making about trend extraction step, facilitating the preprocessing phase of time series analysis.

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Paper Citation


in Harvard Style

de Souza L., Marcon Gomes Vaz M. and Sfair Sunye M. (2014). Domain Ontology for Time Series Provenance . In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-028-4, pages 217-224. DOI: 10.5220/0004886502170224


in Bibtex Style

@conference{iceis14,
author={Lucélia de Souza and Maria Salete Marcon Gomes Vaz and Marcos Sfair Sunye},
title={Domain Ontology for Time Series Provenance},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2014},
pages={217-224},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004886502170224},
isbn={978-989-758-028-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Domain Ontology for Time Series Provenance
SN - 978-989-758-028-4
AU - de Souza L.
AU - Marcon Gomes Vaz M.
AU - Sfair Sunye M.
PY - 2014
SP - 217
EP - 224
DO - 10.5220/0004886502170224