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

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

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Germany

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

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

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

Abstract

V originále

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.

Citovat

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.

@inproceedings{1381529,
   author = {Ge, Mouzhi and Chondrogiannis, Theodoros},
   address = {Cham},
   booktitle = {Proceedings of the 21st European Conference on Advances in Databases and Information Systems},
   doi = {http://dx.doi.org/10.1007/978-3-319-67162-8_2},
   keywords = {spatio-textual data; data quality; relative quality},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-319-67161-1},
   pages = {12-20},
   publisher = {Springer},
   title = {Assessing the Quality of Spatio-textual Datasets in the Absence of Ground Truth},
   url = {http://cyprusconferences.org/adbis2017/},
   year = {2017}
}

TY  - JOUR
ID  - 1381529
AU  - Ge, Mouzhi - Chondrogiannis, Theodoros
PY  - 2017
TI  - Assessing the Quality of Spatio-textual Datasets in the Absence of Ground Truth
PB  - Springer
CY  - Cham
SN  - 9783319671611
KW  - spatio-textual data
KW  - data quality
KW  - relative quality
UR  - http://cyprusconferences.org/adbis2017/
N2  - 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.
ER  -

GE, Mouzhi and Theodoros CHONDROGIANNIS. Assessing the Quality of Spatio-textual Datasets in the Absence of Ground Truth. In \textit{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.

Detailed Information on Publication Record