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A Data Quality Assessment Approach in the SmartWork Project’s Time-series Data Imputation Paradigm

Topics: Decision support systems and personalized interventions for workability sustainability ; ICT tools for modelling and monitoring worker state (functional abilities, cognitive capacity, mental fatigue, workload, etc.), activity, behaviour and emotional status ; Occupational health risks assessment and assistive ICT intervention tools; Unobtrusive and pervasive health monitoring at the workplace; Virtual worker and workplace models and simulation tools

Authors: Georgios Papoulias ; Otilia Kocsis and Konstantinos Moustakas

Affiliation: Department of Electrical and Computer Engineering, University of Patras, Greece

Keyword(s): Data Quality, Missing Data, Time-series Imputation.

Abstract: The plethora of collected data streams of the SmartWork project’s sensing system is often accompanied by missing values, yielding the need for estimating these missing values through imputation, which may prove unnecessary or computationally expensive in relation to the outcome. This work introduces a data quality assessment approach that allows for decision making regarding the need/efficiency of data completion in order to save system computational resources and ensure quality of imputed data. Preliminary validation of the proposed approach is performed by assessing the correlation between the proposed data quality assessment scores and the normalized mean square error of the imputation on various simulated missing patterns. The results reinforce our initial hypothesis that the suggested score is a suitable data quality indicator, correlating well with the potential errors introduced by imputation in the case of a given batch of input data.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Papoulias, G.; Kocsis, O. and Moustakas, K. (2021). A Data Quality Assessment Approach in the SmartWork Project’s Time-series Data Imputation Paradigm. In Proceedings of the 13th International Joint Conference on Computational Intelligence - SmartWork; ISBN 978-989-758-534-0; ISSN 2184-3236, SciTePress, pages 453-459. DOI: 10.5220/0010719000003063

@conference{smartwork21,
author={Georgios Papoulias. and Otilia Kocsis. and Konstantinos Moustakas.},
title={A Data Quality Assessment Approach in the SmartWork Project’s Time-series Data Imputation Paradigm},
booktitle={Proceedings of the 13th International Joint Conference on Computational Intelligence - SmartWork},
year={2021},
pages={453-459},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010719000003063},
isbn={978-989-758-534-0},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computational Intelligence - SmartWork
TI - A Data Quality Assessment Approach in the SmartWork Project’s Time-series Data Imputation Paradigm
SN - 978-989-758-534-0
IS - 2184-3236
AU - Papoulias, G.
AU - Kocsis, O.
AU - Moustakas, K.
PY - 2021
SP - 453
EP - 459
DO - 10.5220/0010719000003063
PB - SciTePress

- Science and Technology Publications, Lda.
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