Abstract
Compact data structures can represent data with usually a much smaller memory footprint than its plain representation. In addition to maintaining the data in a form that uses less space, they allow us to efficiently access and query the data in its compact form. The \(k^2\)-tree is a self-indexed, compact data structure used to represent binary matrices, that can also be used to represent points in a spatial dataset. Efficient processing of the Distance-based Join Queries (DJQs) is of great importance in spatial databases due to its wide area of application. Two of the most representative and known DJQs are the K Closest Pairs Query (KCPQ) and the \(\varepsilon \) Distance Join Query (\(\varepsilon \)DJQ). These types of join queries are executed over two spatial datasets and can be solved by plane-sweep algorithms, which are efficient but with great requirements of RAM, to be able to fit the whole datasets into main memory. In this work, we present new and efficient algorithms to implement DJQs over the \(k^2\)-tree representation of the spatial datasets, experimentally showing that these algorithms are competitive in query times, with much lower memory requirements.
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Notes
- 1.
Available at http://spatialhadoop.cs.umn.edu/datasets.html.
- 2.
Available at https://gitlab.lbd.org.es/public-sources/djq/k2tree-djq.
- 3.
Available at https://github.com/simongog/sdsl-lite.
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Acknowledgments
Guillermo de Bernardo, Miguel R. Penabad and Nieves R. Brisaboa are partially funded by: MCIN/AEI [PDC2021-121239-C31 (FLATCITY-POC), PDC2021-120917-C21 (SIGTRANS, NextGenerationEU/PRTR), PID2020-114635RB-I00 (EXTRACompact), PID2019-105221RB-C41 (MAGIST)]; ED431C 2021/53 (GRC), GAIN/Xunta de Galicia; and as CITIC members are also partially funded by ED431G 2019/01 (CSI), Xunta de Galicia, FEDER Galicia 2014–2020. The work by Antonio Corral was partially funded by the EU ERDF and the Andalusian Government (Spain) under the project UrbanITA (ref. PY20_00809) and the Spanish Ministry of Science and Innovation under the R &D project HERMES (ref. PID2021-124124OB-I00).
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Bernardo, G.d., Penabad, M.R., Corral, A., Brisaboa, N.R. (2023). Compact Data Structures for Efficient Processing of Distance-Based Join Queries. In: Fournier-Viger, P., Hassan, A., Bellatreche, L. (eds) Model and Data Engineering. MEDI 2022. Lecture Notes in Computer Science, vol 13761. Springer, Cham. https://doi.org/10.1007/978-3-031-21595-7_15
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