Home // GEOProcessing 2019, The Eleventh International Conference on Advanced Geographic Information Systems, Applications, and Services // View article
Authors:
Douglas Galarus
Rafal Angryk
Keywords: Data Quality; Spatial-Temporal Data; Quality Control; Outlier; Inlier; Bad Data; Ground Truth
Abstract:
There is a need for robust solutions to the challenges of near real-time spatio-temporal outlier and anomaly detection. Yet, there are many challenges in developing and evaluating meth-ods including: real-world cost and infeasibility of verifying ground truth, non-isotropic covariance, near-real-time opera-tion, challenges with time, bad data, bad metadata, and other quality factors. In this paper, we demonstrate the challenges of evaluating spatio-temporal data quality methods for weath-er sensor data via a method we developed and other popular, interpolation-based methods to conduct model-based outlier detection. We demonstrate that a multi-faceted approach is necessary to counteract the impact of outliers. We demonstrate the challenges of evaluation in the presence of incorrect labels of good and bad data.
Pages: 1 to 10
Copyright: Copyright (c) IARIA, 2019
Publication date: February 24, 2019
Published in: conference
ISSN: 2308-393X
ISBN: 978-1-61208-687-3
Location: Athens, Greece
Dates: from February 24, 2019 to February 28, 2019