GE, Mouzhi and Theodoros CHONDROGIANNIS. Assessing the Quality of Spatio-textual Datasets in the Absence of Ground Truth. In Proceedings of the 21st European Conference on Advances in Databases and Information Systems. Cham: Springer, 2017, p. 12-20. ISBN 978-3-319-67161-1. Available from: https://dx.doi.org/10.1007/978-3-319-67162-8_2.
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Basic information
Original name Assessing the Quality of Spatio-textual Datasets in the Absence of Ground Truth
Authors GE, Mouzhi (156 China, guarantor, belonging to the institution) and Theodoros CHONDROGIANNIS (300 Greece).
Edition Cham, Proceedings of the 21st European Conference on Advances in Databases and Information Systems, p. 12-20, 9 pp. 2017.
Publisher Springer
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Germany
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW Springer, CORE B conference, SCOPUS, WoS, DBLP
RIV identification code RIV/00216224:14330/17:00096805
Organization unit Faculty of Informatics
ISBN 978-3-319-67161-1
ISSN 1865-0929
Doi http://dx.doi.org/10.1007/978-3-319-67162-8_2
UT WoS 000775606800002
Keywords in English spatio-textual data; data quality; relative quality
Tags core_B, firank_B
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 31/5/2022 12:06.
Abstract
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.
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