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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00096805" target="_blank" >RIV/00216224:14330/17:00096805 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-67162-8_2" target="_blank" >http://dx.doi.org/10.1007/978-3-319-67162-8_2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-67162-8_2" target="_blank" >10.1007/978-3-319-67162-8_2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 21st European Conference on Advances in Databases and Information Systems

ISBN

9783319671611

ISSN

1865-0929

e-ISSN

—

Počet stran výsledku

9

Strana od-do

12-20

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Nicosia, Cyprus

Datum konání akce

1. 1. 2017

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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