The increasing availability of enriched geospatial data has opened up a new domain and enables the development of more sophisticated location-based services and applications. However, this development has also given rise to various data quality problems as it is very hard to verify the data for all real-world entities contained in a dataset. In this paper, we propose ARCI, a relative quality indicator which exploits the vast availability of spatio-textual datasets, to indicate how confident a user can be in the correctness of a given dataset. ARCI operates in the absence of ground truth and aims at computing the relative quality of an input dataset by cross-referencing its entries among various similar datasets. We also present an algorithm for computing ARCI and we evaluate its performance in a preliminary experimental evaluation using real-world datasets.
CITATION STYLE
Ge, M., & Chondrogiannis, T. (2017). Assessing the quality of spatio-textual datasets in the absence of ground truth. In Communications in Computer and Information Science (Vol. 767, pp. 12–20). Springer Verlag. https://doi.org/10.1007/978-3-319-67162-8_2
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