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Towards GeoSpatial semantic data management: strengths, weaknesses, and challenges ahead

Authors:

Kostas Patroumpas,

Giorgos Giannopoulos,

Spiros AthanasiouAuthors Info & Claims

SIGSPATIAL '14: Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 301 - 310

https://doi.org/10.1145/2666310.2666410

Published: 04 November 2014 Publication History

Abstract

An immense wealth of data is already accessible through the Semantic Web and an increasing part of it also has geospatial context or relevance. Although existing technology is mature enough to integrate a variety of information from heterogeneous sources into interlinked features, it still falls behind when it comes to representation and reasoning on spatial characteristics. It is only lately that several RDF stores have begun to accommodate geospatial entities and to enable some kind of processing on them. To address interoperability, the OGC has recently adopted the GeoSPARQL standard, which defines a vocabulary for representing geometric types in RDF and an extension to the SPARQL language for formulating queries. In this paper, we provide a comprehensive review of the current state-of-the-art in geospatially-enabled semantic data management. Apart from an insightful analysis of the available architectures in industry and academia, we conduct an evaluation study on prominent RDF stores with geospatial support. We also compare their performance and attested capabilities to renowned DBMSs widely used in geospatial applications. We introduce a methodology suitable to assess RDF stores for robustness against large geospatial datasets, and also for expressiveness on a variety of queries involving both spatial and thematic criteria. As our findings demonstrate, the potential for query optimization, advanced indexing schemes, and spatio-semantic extensions is significant. Towards this goal, we point out several challenging issues for joint research by the GIS and Semantic Web communities.

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Cited By

Yue ZZhu WMei XZhong S(2025)An integrated graph-spatial method for high-performance geospatial-temporal semantic queryInternational Journal of Applied Earth Observation and Geoinformation10.1016/j.jag.2025.104437137(104437)Online publication date: Mar-2025
https://doi.org/10.1016/j.jag.2025.104437
Sunitha GSuma KGalety MDavanam GVarna C(2024)Python for Geospatial Data AnalysisEthics, Machine Learning, and Python in Geospatial Analysis10.4018/979-8-3693-6381-2.ch005(94-119)Online publication date: 10-May-2024
https://doi.org/10.4018/979-8-3693-6381-2.ch005
Tran BAussenac-Gilles NComparot CTrojahn C(2022)Semantic Integration of Raster Data for Earth Observation on Territorial UnitsISPRS International Journal of Geo-Information10.3390/ijgi1102014911:2(149)Online publication date: 19-Feb-2022
https://doi.org/10.3390/ijgi11020149
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Index Terms

Towards GeoSpatial semantic data management: strengths, weaknesses, and challenges ahead
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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Information

Published In

cover image ACM Conferences

SIGSPATIAL '14: Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2014

651 pages

ISBN:9781450331319

DOI:10.1145/2666310

General Chairs:
Yan Huang
University of North Texas
,
Markus Schneider
University of Florida
,
Program Chairs:
Michael Gertz
Heidelberg University, Germany
,
John Krumm
Microsoft Research
,
Jagan Sankaranarayanan
NEC Labs

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

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Published: 04 November 2014

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SIGSPATIAL

SIGSPATIAL '14: 22nd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 4 - 7, 2014

Texas, Dallas

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SIGSPATIAL '14 Paper Acceptance Rate 39 of 184 submissions, 21%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Yue ZZhu WMei XZhong S(2025)An integrated graph-spatial method for high-performance geospatial-temporal semantic queryInternational Journal of Applied Earth Observation and Geoinformation10.1016/j.jag.2025.104437137(104437)Online publication date: Mar-2025
https://doi.org/10.1016/j.jag.2025.104437
Sunitha GSuma KGalety MDavanam GVarna C(2024)Python for Geospatial Data AnalysisEthics, Machine Learning, and Python in Geospatial Analysis10.4018/979-8-3693-6381-2.ch005(94-119)Online publication date: 10-May-2024
https://doi.org/10.4018/979-8-3693-6381-2.ch005
Tran BAussenac-Gilles NComparot CTrojahn C(2022)Semantic Integration of Raster Data for Earth Observation on Territorial UnitsISPRS International Journal of Geo-Information10.3390/ijgi1102014911:2(149)Online publication date: 19-Feb-2022
https://doi.org/10.3390/ijgi11020149
Popa ARamallo González AJaglan GFensel A(2022)A Semantically Data-Driven Classification Framework for Energy Consumption in BuildingsEnergies10.3390/en1509315515:9(3155)Online publication date: 26-Apr-2022
https://doi.org/10.3390/en15093155
Almobydeen SViqueira JLama M(2022)GeoSPARQL query support for scientific raster array dataComputers & Geosciences10.1016/j.cageo.2021.105023159:COnline publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1016/j.cageo.2021.105023
Zhang FLu QDu ZChen XCao C(2021)A comprehensive overview of RDF for spatial and spatiotemporal data managementThe Knowledge Engineering Review10.1017/S026988892100008436Online publication date: 22-Jun-2021
https://doi.org/10.1017/S0269888921000084
Ioannidis TGarbis GKyzirakos KBereta KKoubarakis M(2021)Evaluating Geospatial RDF Stores Using the Benchmark Geographica 2Journal on Data Semantics10.1007/s13740-021-00118-x10:3-4(189-228)Online publication date: 23-Apr-2021
https://doi.org/10.1007/s13740-021-00118-x
Jin XEom SShin SLee KHong C(2021)DORIC: discovering topological relations based on spatial link compositionKnowledge and Information Systems10.1007/s10115-021-01603-263:10(2645-2669)Online publication date: 16-Aug-2021
https://doi.org/10.1007/s10115-021-01603-2
Tran BAussenac-Gilles NComparot CTrojahn C(2020)Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land CoverISPRS International Journal of Geo-Information10.3390/ijgi90905039:9(503)Online publication date: 21-Aug-2020
https://doi.org/10.3390/ijgi9090503
Tran BAussenac-Gilles NComparot CTrojahn C(2020)An Approach for Integrating Earth Observation, Change Detection and Contextual Data for Semantic SearchIGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS39084.2020.9324064(3115-3118)Online publication date: 26-Sep-2020
https://doi.org/10.1109/IGARSS39084.2020.9324064
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