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SRX: efficient management of spatial RDF data

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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.

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Notes

  1. For entities that have point geometries, the spatial selection can be evaluated using only the R-tree. If the entities have non-point geometries, the R-tree search may result in false positives; thus, the final results of the spatial filter are confirmed by retrieving the exact geometries from the dictionary.

  2. If the spatial join inputs are very small, we simply fetch the geometries of the input entity sets and do a nested-loop spatial join.

  3. Most spatial predicates, when translated to the grid-based approximations of the encoding, involve distance computations and/or cheap geometry intersection tests.

  4. Recall that the inputs are sorted by ID and that entities may be encoded at different granularities due to data skew or geometry extents. Therefore, using the cell ID of e\(_r\) alone is not sufficient and we have to use the minChildID of e\(_r\).

  5. The fact that the entities arrive from the inputs sorted by their IDs guarantees that they are also sorted based on their minChildIDs.

  6. Recall that the actual geometries of the entities have not been retrieved yet; otherwise, SHJ [19] would be used (see Sect. 4).

  7. In case there are no spatial entities in the database falling in \(c_p\) or one of its parent cells, then as limit we use the first free (i.e., the minimum) spatial ID for an entity in \(c_p\).

  8. https://tinyurl.com/yc4lxqdv.

  9. https://tinyurl.com/y7ukhge3.

  10. We only included a small separate cache of 40 KB for the R-tree. Since the OS caches R-tree pages, we used a small cache size in order to reduce the effect of double caching by the SaIL library.

  11. https://tinyurl.com/ydbscsxf.

  12. https://tinyurl.com/y7ukhge3.

  13. This check was not included in the version of RDF-3X we had but we added it for consistency.

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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.

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Authors and Affiliations

  1. University of Peloponnese, Tripoli, Greece

    Konstantinos Theocharidis

  2. IMSI ‘Athena’, Athens, Greece

    Konstantinos Theocharidis & Manolis Terrovitis

  3. ETH Zürich, Zurich, Switzerland

    John Liagouris

  4. University of Ioannina, Ioannina, Greece

    Nikos Mamoulis

  5. Johannes Gutenberg University Mainz, Mainz, Germany

    Panagiotis Bouros

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  1. Konstantinos Theocharidis
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  2. John Liagouris
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  3. Nikos Mamoulis
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  4. Panagiotis Bouros
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  5. Manolis Terrovitis
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Correspondence to John Liagouris.

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Theocharidis, K., Liagouris, J., Mamoulis, N. et al. SRX: efficient management of spatial RDF data. The VLDB Journal 28, 703–733 (2019). https://doi.org/10.1007/s00778-019-00554-z

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