A quality-aware spatial data warehouse for querying hydroecological data

被引：11

作者：

Berrahou, L. ^{[1
]}

Lalande, N. ^{[1
]}

Serrano, E. ^{[3
]}

Molla, G. ^{[1
]}

Berti-Equille, L. ^{[3
,4
]}

Bimonte, S. ^{[2
]}

Bringay, S. ^{[5
]}

Cernesson, F. ^{[1
]}

Grac, C. ^{[6
]}

Ienco, D. ^{[1
]}

Le Ber, F. ^{[7
]}

Teisseire, M. ^{[1
]}

机构：

[1] AgroParisTech, IRSTEA, TETIS, F-34000 Montpellier, France

[2] IRSTEA, TSCF, F-63170 Aubiere, France

[3] ESPACE DEV, IRD, F-34000 Montpellier, France

[4] QCRI, Doha, Qatar

[5] Univ Montpellier 3, LIRMM, F-34000 Montpellier, France

[6] Univ Strasbourg, LIVE, CNRS, ENGEES, F-67000 Strasbourg, France

[7] Univ Strasbourg, ICUBE, ENGEES, CNRS, F-67400 Illkirch Graffenstaden, France

来源：

COMPUTERS & GEOSCIENCES | 2015年 / 85卷

关键词：

Information system; Data warehouse modeling and design; Data quality; Hydroecological data;

D O I：

10.1016/j.cageo.2015.09.012

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Addressing data quality issues in information systems remains a challenging task. Many approaches only tackle this issue at the extract, transform and load steps. Here we define a comprehensive method to gain greater insight into data quality characteristics within data warehouse. Our novel architecture was implemented for an hydroecological case study where massive French watercourse sampling data are collected. The method models and makes effective use of spatial, thematic and temporal accuracy, consistency and completeness for multidimensional data in order to offer analysts a "data quality" oriented framework. The results obtained in experiments carried out on the Saone River dataset demonstrated the relevance of our approach. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：126 / 135

页数：10

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