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SRX: efficient management of spatial RDF data

  • Konstantinos Theocharidis
  • John LiagourisEmail author
  • Nikos Mamoulis
  • Panagiotis Bouros
  • Manolis Terrovitis
Regular Paper
  • 46 Downloads

Abstract

We present a general encoding scheme for the efficient management of spatial RDF data. The scheme approximates the geometries of the RDF entities inside their (integer) IDs and can be used, along with several operators and optimizations we introduce, to accelerate queries with spatial predicates and to re-encode entities dynamically in case of updates. We implement our ideas in SRX, a system built on top of the popular RDF-3X system. SRX extends RDF-3X with support for three types of spatial queries: range selections (e.g., find entities within a given polygon), spatial joins (e.g., find pairs of entities whose locations are close to each other), and spatial k-nearest neighbors (e.g., find the three closest entities from a given location). We evaluate SRX on spatial queries and updates with real RDF data, and we also compare its performance with the latest versions of three popular RDF stores. The results show SRX ’s superior performance over the competitors; compared to RDF-3X, SRX improves its performance for queries with spatial predicates while incurring little overhead during updates.

Keywords

Spatial RDF data GeoSPARQL Bit encoding Hilbert curve RDF-3X Query evaluation Updates 

Notes

Acknowledgements

We acknowledge support of this work by the project “Moving from Big Data Management to Data Science” (MIS 5002437/3) which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure,” funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020) and co-financed by Greece and the European Union (European Regional Development Fund). This work is also partially supported by Grant 17253616 from Hong Kong RGC.

Supplementary material

778_2019_554_MOESM1_ESM.pdf (144 kb)
Supplementary material 1 (pdf 143 KB)

References

  1. 1.
    Abadi, D. J., Marcus, A., Madden, S., Hollenbach, K.J.: Scalable semantic web data management using vertical partitioning. In: VLDB (2007)Google Scholar
  2. 2.
    Aberger, C.R., Tu, S., Olukotun, K., Ré, C.: Emptyheaded: a relational engine for graph processing. In SIGMOD (2016)Google Scholar
  3. 3.
    Aberger, C.R., Tu, S., Olukotun, K., Ré, C.: Old techniques for new join algorithms: a case study in RDF processing. In: ICDE Workshops (2016)Google Scholar
  4. 4.
    Atre, M., Chaoji, V., Zaki, M.J., Hendler, J.A.: Matrix “Bit” loaded: a scalable lightweight join query processor for RDF data. In: WWW (2010)Google Scholar
  5. 5.
    Battle, R., Kolas, D.: Enabling the geospatial semantic web with parliament and geosparql. Semant. Web 3(4), 355–370 (2012)Google Scholar
  6. 6.
    Bornea, M.A., Dolby, J., Kementsietsidis, A., Srinivas, K., Dantressangle, P., Udrea, O., Bhattacharjee, B.: Building an efficient RDF store over a relational database. In: SIGMOD (2013)Google Scholar
  7. 7.
    Brinkhoff, T., Kriegel, H.-P., Seeger, B.: Efficient processing of spatial joins using R-trees. In: SIGMOD (1993)Google Scholar
  8. 8.
    Brodt, A., Nicklas, D., Mitschang, B.: Deep integration of spatial query processing into native RDF triple stores. In: GIS (2010)Google Scholar
  9. 9.
    Broekstra, J., Kampman, A., van Harmelen, F.: Sesame: An architecture for storing and querying RDF data and schema information. In: Semantics for the WWW. MIT Press (2001)Google Scholar
  10. 10.
    Chong, E.I., Das, S., Eadon, G., Srinivasan, J.: An efficient SQL-based RDF querying scheme. In: VLDB (2005)Google Scholar
  11. 11.
    Eldawy, A., Mokbel, M.F.: The era of big spatial data: a survey. Found. Trends Databases 6(3–4), 163–273 (2016)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: SIGMOD (1984)Google Scholar
  14. 14.
    Hadjieleftheriou, M., Hoel, E.G., Tsotras, V.J.: Sail: a spatial index library for efficient application integration. GeoInformatica 9(4), 367–389 (2005)CrossRefGoogle Scholar
  15. 15.
    Koubarakis, M., Kyzirakos, K.: Modeling and querying metadata in the semantic sensor web: the model stRDF and the query language stSPARQL. In: ESWC (2010)Google Scholar
  16. 16.
    Kyzirakos, K., Karpathiotakis, M., Koubarakis, M.: Strabon: A semantic geospatial DBMS. In: ISWC (2012)Google Scholar
  17. 17.
    Liagouris, J., Mamoulis, N., Bouros, P., Terrovitis, M.: An effective encoding scheme for spatial RDF data. Proc. VLDB Endow. 7(12), 1271–1282 (2014)CrossRefGoogle Scholar
  18. 18.
  19. 19.
    Lo, M.-L., Ravishankar, C.V.: Spatial hash-joins. In: SIGMOD (1996)Google Scholar
  20. 20.
    Mamoulis, N.: Spatial Data Management. Morgan & Claypool Publishers, San Rafael (2011)CrossRefzbMATHGoogle Scholar
  21. 21.
    Mamoulis, N., Papadias, D.: Slot index spatial join. TKDE 15(1), 211–231 (2003)Google Scholar
  22. 22.
    Mouratidis, K., Hadjieleftheriou, M., Papadias, D.: Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring. In: SIGMOD (2005)Google Scholar
  23. 23.
    Neumann, T., Moerkotte, G.: Characteristic sets: accurate cardinality estimation for RDF queries with multiple joins. In: ICDE (2011)Google Scholar
  24. 24.
    Neumann, T., Weikum, G.: Scalable join processing on very large RDF graphs. In: SIGMOD (2009)Google Scholar
  25. 25.
    Neumann, T., Weikum, G.: RDF-3X: a RISC-style engine for RDF. Proc. VLDB Endow. 1(1), 647–659 (2008)CrossRefGoogle Scholar
  26. 26.
    Neumann, T., Weikum, G.: The RDF-3X engine for scalable management of RDF data. VLDB J. 19(1), 91–113 (2010)CrossRefGoogle Scholar
  27. 27.
    Neumann, T., Weikum, G.: x-RDF-3X: fast querying, high update rates, and consistency for RDF databases. Proc. VLDB Endow. 3(1–2), 256–263 (2010)CrossRefGoogle Scholar
  28. 28.
    Nikitopoulos, P., Vlachou, A., Doulkeridis, C., Vouros, G.A.: DiStRDF: distributed spatio-temporal RDF queries on Spark. In: EDBT/ICDT (2018)Google Scholar
  29. 29.
    Pandey, V., Kipf, A., Neumann, T., Kemper, A.: How good are modern spatial analytics systems? Proc. VLDB Endow. 11(11), 1661–1673 (2018)CrossRefGoogle Scholar
  30. 30.
  31. 31.
    Patroumpas, K., Giannopoulos, G., Athanasiou, S.: Towards geospatial semantic data management: strengths, weaknesses, and challenges ahead. In: GIS (2014)Google Scholar
  32. 32.
  33. 33.
    Wang, C.-J., Ku, W.-S., Chen, H.: Geo-store: a spatially-augmented sparql query evaluation system. In: GIS (2012)Google Scholar
  34. 34.
    Wang, D., Zou, L., Feng, Y., Shen, X., Tian, J., Zhao, D.: S-store: an engine for large RDF graph integrating spatial information. In: DASFAA (2013)Google Scholar
  35. 35.
    Weiss, C., Karras, P., Bernstein, A.: Hexastore: sextuple indexing for semantic web data management. Proc. VLDB Endow. 1(1), 1008–1019 (2008)CrossRefGoogle Scholar
  36. 36.
    Wilkinson, K., Sayers, C., Kuno, H.A., Reynolds, D.: Efficient RDF storage and retrieval in Jena2. In: SWDB (2003)Google Scholar
  37. 37.
  38. 38.
    Yan, Y., Wang, C., Zhou, A., Qian, W., Ma, L., Pan, Y.: Efficient indices using graph partitioning in RDF triple stores. In: ICDE (2009)Google Scholar
  39. 39.
    Yuan, P., Liu, P., Wu, B., Jin, H., Zhang, W., Liu, L.: TripleBit: a fast and compact system for large scale RDF data. Proc. VLDB Endow. 6(7), 517–528 (2013)CrossRefGoogle Scholar
  40. 40.
    Zeng, K., Yang, J., Wang, H., Shao, B., Wang, Z.: A distributed graph engine for web scale RDF data. Proc. VLDB Endow. 6(4), 265–276 (2013)CrossRefGoogle Scholar
  41. 41.
    Zou, L., Mo, J., Chen, L., Özsu, M.T., Zhao, D.: gStore: answering SPARQL queries via subgraph matching. Proc. VLDB Endow. 4(8), 482–493 (2011)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of PeloponneseTripoliGreece
  2. 2.IMSI ‘Athena’AthensGreece
  3. 3.ETH ZürichZurichSwitzerland
  4. 4.University of IoanninaIoanninaGreece
  5. 5.Johannes Gutenberg University MainzMainzGermany

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