D 2017

Assessing the Quality of Spatio-textual Datasets in the Absence of Ground Truth

GE, Mouzhi and Theodoros CHONDROGIANNIS

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

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"

RIV identification code

RIV/00216224:14330/17:00096805

Organization unit

Faculty of Informatics

ISBN

978-3-319-67161-1

ISSN

UT WoS

000775606800002

EID Scopus

2-s2.0-85029820501

Keywords in English

spatio-textual data; data quality; relative quality

Tags

International impact, Reviewed
Changed: 31/5/2022 12:06, RNDr. Pavel Šmerk, Ph.D.

Abstract

In the original language

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.