Skip to main content
SpringerLink
Log in

Scalable Transformation of Big Geospatial Data into Linked Data

  • Conference paper
  • First Online:
The Semantic Web – ISWC 2021 (ISWC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12922))

Included in the following conference series:

Abstract

In the era of big data, a vast amount of geospatial data has become available originating from a large diversity of sources. In most cases, this data does not follow the linked data paradigm and the existing transformation tools have been proved ineffective due to the large volume and velocity of geospatial data. This is because none of the existing tools can utilize effectively the processing power of clusters of computers. We present the system GeoTriples-Spark which is able to massively transform big geospatial data into RDF graphs using Apache Spark. We evaluate GeoTriple-Spark’s performance and scalability in standalone and distributed environments and show that it exhibits superior performance and scalability when compared to all of its competitors.

The present work was funded by the European Union’s Horizon 2020 research and innovation project under grant agreement No 825258.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.openstreetmap.org/.

  2. 2.

    https://hub.arcgis.com/search.

  3. 3.

    https://www.copernicus.eu/.

  4. 4.

    https://lod-cloud.net/.

  5. 5.

    http://kr.di.uoa.gr/#datasets.

  6. 6.

    https://scihub.copernicus.eu/.

  7. 7.

    https://scihub.copernicus.eu/twiki/do/view/SciHubWebPortal/AnnualReport2019.

  8. 8.

    https://graphdb.ontotext.com/.

  9. 9.

    http://earthanalytics.eu/.

  10. 10.

    https://github.com/LinkedEOData/GeoTriples.

  11. 11.

    https://zenodo.org/record/4899793.

  12. 12.

    https://github.com/LinkedEOData/GeoTriples/tree/master/data.

  13. 13.

    https://www.ogc.org/standards/wkt-crs.

  14. 14.

    https://www.w3.org/TR/rdb-direct-mapping/.

  15. 15.

    https://www.w3.org/TR/r2rml/.

  16. 16.

    https://rml.io/.

  17. 17.

    https://geotools.org/.

  18. 18.

    https://jena.apache.org/documentation/io/streaming-io.html.

  19. 19.

    https://github.com/SLIPO-EU/TripleGeo.

  20. 20.

    http://geoknow.eu/Welcome.html.

  21. 21.

    http://slipo.eu/.

  22. 22.

    http://ontop-spatial.di.uoa.gr/.

  23. 23.

    http://ai.di.uoa.gr/.

  24. 24.

    https://www.ogc.org/standards/sfs.

  25. 25.

    https://spark.apache.org/.

  26. 26.

    http://geospark.datasyslab.org/.

  27. 27.

    https://tools.ietf.org/html/rfc7946.

  28. 28.

    https://github.com/DataSystemsLab/GeoSpark/issues/356.

  29. 29.

    https://desktop.arcgis.com/en/arcmap/latest/manage-data/shapefiles/geoprocessing-considerations-for-shapefile-output.htm.

  30. 30.

    https://github.com/dimitrianos/GeoTriples-Hadoop.

  31. 31.

    https://github.com/SLIPO-EU/TripleGeo/tree/master/src/eu/slipo/athenarc/triplegeo/partitioning.

  32. 32.

    https://github.com/hopshadoop/hops.

  33. 33.

    The system uses hyper-threading hence it has 16 physical cores.

  34. 34.

    https://www.logicalclocks.com/.

  35. 35.

    https://gadm.org/.

  36. 36.

    http://download.geofabrik.de/.

  37. 37.

    https://www.kth.se/blogs/pdc/2018/11/scalability-strong-and-weak-scaling/.

  38. 38.

    https://spark.apache.org/docs/latest/tuning.html.

  39. 39.

    https://hive.apache.org/.

  40. 40.

    https://accumulo.apache.org/.

References

  1. Abdul, J., Alkathiri, M., Potdar, M.B.: Geospatial Hadoop (GS-Hadoop) an efficient mapreduce based engine for distributed processing of shapefiles. In: ICACCA (2016)

    Google Scholar 

  2. Ali, W., Saleem, M., Yao, B., Hogan, A., Ngomo, A.N.: A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs. CoRR (2021)

    Google Scholar 

  3. Auer, S., Lehmann, J., Hellmann, S.: LinkedGeoData: adding a spatial dimension to the web of data. In: ISWC (2009)

    Google Scholar 

  4. Bereta, K., et al.: The copernicus app lab project: easy access to copernicus data. In: EDBT (2019)

    Google Scholar 

  5. Bereta, K., Koubarakis, M.: Ontop of geospatial databases. In: ISWC (2016)

    Google Scholar 

  6. Bereta, K., Koubarakis, M.: Creating virtual semantic graphs on top of big data from space. In: BiDS (2017)

    Google Scholar 

  7. Bilidas, D., Ioannidis, T., Mamoulis, N., Koubarakis, M.: Efficient storage and querying for big linked geospatial data: the system Strabo 2. Manuscript in preparation (2021)

    Google Scholar 

  8. Blower, J., Clifford, D., Goncalves, P., Koubarakis, M.: The MELODIES project: integrating diverse data using linked data and cloud computing. In: BiDS (2014)

    Google Scholar 

  9. Burgstaller, S., et al.: LEOpatra: a mobile application for smart fertilization based on Linked Data. In: HAICTA (2017)

    Google Scholar 

  10. Chentout, K., Vaisman, A.A.: Adding spatial support to R2RML mappings. In: OTM Workshops (2013)

    Google Scholar 

  11. Dimou, A., Sande, M.V., Colpaert, P., Verborgh, R., Mannens, E., de Walle, R.V.: RML: a generic language for integrated RDF mappings of heterogeneous data. In: LDOW (2014)

    Google Scholar 

  12. Herring, J.R.: OpenGIS Implementation Standard for Geographic information - Simple feature access - Part 2: SQL option. Open Geospatial Consortium standard (2010). http://portal.opengeospatial.org/files/?artifact_id=25354

  13. Hoffart, J., Suchanek, F.M., Berberich, K., Weikum, G.: YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia (2013)

    Google Scholar 

  14. Homburg, T., Staab, S., Janke, D.: GeoSPARQL+: syntax, semantics and system for integrated querying of graph, raster and vector data. In: Pan, J.Z., et al. (eds.) ISWC 2020. LNCS, vol. 12506, pp. 258–275. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62419-4_15

    Chapter  Google Scholar 

  15. Ismail, M., Gebremeskel, E., Kakantousis, T., Berthou, G., Dowling, J.: Hopsworks: improving user experience and development on hadoop with scalable. ICDCS, strongly consistent metadata. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) (2017)

    Google Scholar 

  16. Kaoudi, Z., Manolescu, I., Zampetakis, S.: Cloud-Based RDF Data Management (2020)

    Google Scholar 

  17. Karalis, N., Mandilaras, G.M., Koubarakis, M.: Extending the YAGO2 knowledge graph with precise geospatial knowledge. In: ISWC (2019)

    Google Scholar 

  18. Koubarakis, M., et al.: From copernicus big data to extreme earth analytics. In: EDBT (2019)

    Google Scholar 

  19. Kyzirakos, K., et al.: The spatiotemporal RDF store strabon. In: SSTD (2013)

    Google Scholar 

  20. Kyzirakos, K., et al.: Wildfire monitoring using satellite images, ontologies and Linked Geospatial Data. J. Web Semant. 24, 18–26 (2014)

    Google Scholar 

  21. Kyzirakos, K., et al.: GeoTriples: transforming geospatial data into RDF graphs using R2RML and RML mappings. J. Web Semant. 52, 16–32 (2018)

    Google Scholar 

  22. Kyzirakos, K., Vlachopoulos, I., Savva, D., Manegold, S., Koubarakis, M.: GeoTriples: a tool for publishing geospatial data as RDF graphs using R2RML mappings. In: ISWC Posters (2014)

    Google Scholar 

  23. de León, A., Saquicela, V., Vilches, L.M., Villazón-Terrazas, B., Priyatna, F., Corcho, O.: Geographical linked data: a spanish use case. In: I-SEMANTICS (2010)

    Google Scholar 

  24. Mandilaras, G., Pantazi, D.A., Koubarakis, M., Hughes, N., Everett, A., Kiærbech, A.: Ice monitoring with extremeearth. In: LASCAR (2020)

    Google Scholar 

  25. Niazi, S., Ismail, M., Haridi, S., Dowling, J., Grohsschmiedt, S., Ronström, M.: HopsFS: scaling hierarchical file system metadata using NewSQL databases. In: FAST (2017)

    Google Scholar 

  26. Nikolaou, C., et al.: Sextant: visualizing time-evolving linked geospatial data. J. Web Semant. 35, 35–52 (2015)

    Google Scholar 

  27. Papadakis, G.A., Mandilaras, G., Nikos, M., Koubarakis, M.: Progressive, holistic geospatial interlinking. In: Web Conference (2021)

    Google Scholar 

  28. Patroumpas, K., Alexakis, M., Giannopoulos, G., Athanasiou, S.: TripleGeo: an ETL Tool for transforming geospatial data into RDF triples. In: EDBT/ICDT (2014)

    Google Scholar 

  29. Patroumpas, K., Skoutas, D., Mandilaras, G.M., Giannopoulos, G., Athanasiou, S.: Exposing points of interest as linked geospatial data. In: SSTD (2019)

    Google Scholar 

  30. Sherif, M.A., Dreßler, K., Smeros, P., Ngomo, A.N.: Radon - rapid discovery of topological relations. In: AAAI (2017)

    Google Scholar 

  31. Smeros, P., Koubarakis, M.: Discovering spatial and temporal links among RDF data. In: LDOW (2016)

    Google Scholar 

  32. Yu, J., Wu, J., Sarwat, M.: GeoSpark: a cluster computing framework for processing large-scale spatial data. In: SIGSPATIAL (2015)

    Google Scholar 

  33. Zaharia, M., et al.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: USENIX (2012)

    Google Scholar 

  34. Zaharia, M., et al.: Apache spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece

    George Mandilaras & Manolis Koubarakis

Authors
  1. George Mandilaras
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manolis Koubarakis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to George Mandilaras or Manolis Koubarakis .

Editor information

Editors and Affiliations

  1. University of Würzburg, Würzburg, Germany

    Andreas Hotho

  2. Linköping University, Linköping, Sweden

    Eva Blomqvist

  3. University of Düsseldorf, Düsseldorf, Germany

    Stefan Dietze

  4. IBM Research - Thomas J. Watson Research, Hawthorne, CA, USA

    Achille Fokoue

  5. University of Texas, Austin, TX, USA

    Ying Ding

  6. Imperial College, London, UK

    Payam Barnaghi

  7. Australian National University, Canberra, ACT, Australia

    Armin Haller

  8. Fondazione Bruno Kessler, Povo, Trento, Italy

    Mauro Dragoni

  9. The Open University Walton Hall, Milton Keynes, UK

    Harith Alani

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mandilaras, G., Koubarakis, M. (2021). Scalable Transformation of Big Geospatial Data into Linked Data. In: Hotho, A., et al. The Semantic Web – ISWC 2021. ISWC 2021. Lecture Notes in Computer Science(), vol 12922. Springer, Cham. https://doi.org/10.1007/978-3-030-88361-4_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-88361-4_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88360-7

  • Online ISBN: 978-3-030-88361-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation