A Systematic Review of Spatial Approximations in Spatial Database Systems
DOI:
https://doi.org/10.5753/jidm.2022.2519Keywords:
GIS, spatial approximation, spatial database system, spatial information retrieval, spatial query processingAbstract
Many applications rely on spatial information retrieval, which involves costly computational geometric algorithms to process spatial queries. Spatial approximations simplify the geometric shape of complex spatial objects, allowing faster spatial queries at the expense of result accuracy. In this sense, spatial approximations have been employed to efficiently reduce the number of objects under consideration, followed by a refinement step to restore accuracy. For instance, spatial index structures employ spatial approximations to organize spatial objects in hierarchical structures (e.g., the R-tree). It leads to the interest in studying how spatial approximations can be efficiently employed to improve spatial query processing. This article presents a systematic review on this topic. We gather relevant studies by performing a search string on several digital libraries. We further expand the studies under consideration by employing a single iteration of the snowballing approach, where we track the reference list of selected papers. As a result, we provide an overview and comparison of existing approaches that propose, evaluate, or make use of spatial approximations to optimize the performance of spatial queries. The spatial approximations mentioned by the approaches are also summarized. Further, we characterize the approaches and discuss some future trends.
Downloads
References
Aggarwal, A., Chang, J. S., and Yap, C. K. Minimum area circumscribing polygons. The Visual Computer vol. 1, pp. 112–117, 1985.
Alam, M. M., Torgo, L., and Bifet, A. A survey on spatio-temporal data analytics systems. ACM Computing Surveys, 2021.
Anselin, L. Spatial data science. In International Encyclopedia of Geography. John Wiley & Sons, Ltd, pp. 1–6, 2020.
Bandi, N., Sun, C., Agrawal, D., and El Abbadi, A. Fast computation of spatial selections and joins using graphics hardware. Information Systems 32 (8): 1073–1100, 2007.
Beckmann, N., Kriegel, H.-P., Schneider, R., and Seeger, B. The R*-tree: An efficient and robust access method for points and rectangles. In ACM SIGMOD International Conference on Management of Data. pp. 322–331, 1990.
Beckmann, N. and Seeger, B. A revised R*-tree in comparison with related index structures. In ACM SIGMOD International Conference on Management of Data. pp. 799–812, 2009.
Bertella, P. K., Lopes, Y. K., Oliveira, R. A. P., and Carniel, A. C. The application of spatial approximations to spatial query processing: A systematic review of literature. In Brazilian Symposium on Databases. pp. 229–240, 2021.
Bouros, P. and Mamoulis, N. Spatial joins: What’s next? SIGSPATIAL Special 11 (1): 13–21, 2019.
Brinkhoff, T., Horn, H., Kriegel, H.-P., and Schneider, R. A storage and access architecture for efficient query processing in spatial database systems. In International Symposium on Spatial Databases. pp. 357–376, 1993b.
Brinkhoff, T., Kriegel, H.-P., and Schneider, R. Comparison of approximations of complex objects used for approximation-based query processing in spatial database systems. In IEEE International Conference on Data Engineering. pp. 40–49, 1993a.
Brinkhoff, T., Kriegel, H.-P., Schneider, R., and Seeger, B. Multi-step processing of spatial joins. In ACM SIGMOD International Conference on Management of Data. pp. 197–208, 1994.
Brodsky, A., Lassez, C., Lassez, J.-L., and Maher, M. J. Separability of polyhedra for optimal filtering of spatial and constraint data. In ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. pp. 54–65, 1995.
Carniel, A. C. Spatial information retrieval in digital ecosystems: A comprehensive survey. In International Conference on Management of Digital Ecosystems. pp. 10–17, 2020.
Carniel, A. C., Ciferri, R. R., and Ciferri, C. D. A. Spatial datasets for conducting experimental evaluations of spatial indices. In Satellite Events of the Brazilian Symposium on Databases - Dataset Showcase Workshop. pp. 286–295, 2017.
Carniel, A. C., Ciferri, R. R., and Ciferri, C. D. A. FESTIval: A versatile framework for conducting experimental evaluations of spatial indices. MethodsX vol. 7, pp. 1–19, 2020.
Carniel, A. C., Roumelis, G., Ciferri, R. R., Vassilakopoulos, M., Corral, A., and Aguiar, C. D. Porting disk-based spatial index structures to flash-based solid state drives. GeoInformatica vol. 26, pp. 253–298, 2022.
Carniel, A. C. and Schneider, M. A survey of fuzzy approaches in spatial data science. In IEEE International Conference on Fuzzy Systems. pp. 1–6, 2021.
Castro, J. P. C., Carniel, A. C., and Ciferri, C. D. A. Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective. Software: Practice and Experience 50 (12): 2121–2144, 2020.
Egenhofer, M. J. and Herring, J. R. Categorizing binary topological relations between regions, lines and points in geographic databases. In The 9-Intersection: Formalism and Its Use for Natural-Language Spatial Predicates, 1994.
Esperança, C. and Samet, H. Orthogonal polygons as bounding structures in filter-refine query processing strategies. In International Symposium on Spatial Databases. pp. 197–220, 1997.
Esquerdo, J. C. D. M., Antunes, J. F. G., Coutinho, A. C., Speranza, E. A., Kondo, A. A., and dos Santos, J. L. SATVeg: A web-based tool for visualization of MODIS vegetation indices in South America. Computers and Electronics in Agriculture vol. 175, pp. 105516, 2020.
Fevgas, A., Akritidis, L., Bozanis, P., and Manolopoulos, Y. Indexing in flash storage devices: a survey on challenges, current approaches, and future trends. The VLDB Journal vol. 29, pp. 273–311, 2020.
Gaede, V. and Günther, O. Multidimensional access methods. ACM Computing Surveys 30 (2): 170–231, 1998.
Graham, R. L. An efficient algorithm for determining the convex hull of a finite planar set. Information Processing Letters vol. 1, pp. 132–133, 1972.
Güting, R. H. An introduction to spatial database systems. The VLDB Journal vol. 3, pp. 357–399, 1994.
Guttman, A. R-trees: A dynamic index structure for spatial searching. In ACM SIGMOD International Conference on Management of Data. pp. 47–57, 1984.
Huang, H., Gartner, G., Krisp, J. M., Raubal, M., and de Weghe, N. V. Location based services: ongoing evolution and research agenda. Journal of Location Based Services 12 (2): 63–93, 2018.
Jacox, E. H. and Samet, H. Spatial join techniques. ACM Transactions on Database Systems 32 (1): 1–44, 2007.
Jagadish, H. Spatial search with polyhedra. In IEEE International Conference on Data Engineering. pp. 311–319, 1990.
Jensen, C. S., Kligys, A., Pedersen, T. B., and Timko, I. Multidimensional data modeling for location-based services. The VLDB Journal vol. 13, pp. 1–21, 2004.
Kamel, I. and Faloutsos, C. Hilbert R-tree: An improved R-tree using fractals. In International Conference on Very Large Data Bases. pp. 500–509, 1994.
Kothuri, R. K. and Ravada, S. Efficient processing of large spatial queries using interior approximations. In International Symposium on Spatial and Temporal Databases. pp. 404–421, 2001.
Mourao, E., Kalinowski, M., Murta, L., Mendes, E., and Wohlin, C. Investigating the use of a hybrid search strategy for systematic reviews. In ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. pp. 193–198, 2017.
Oosterom, P. V. a. N. Spatial access methods. In Geographical Information Systems: Principles, Techniques, Management and Applications, 2nd Edition ed., P. A. Longley, M. F. Goodchild, D. J. Maguire, and D. W. Rhind (Eds.). pp. 385–400, 2005.
Pandey, V., Kipf, A., Neumann, T., and Kemper, A. How good are modern spatial analytics systems? VLDB Endowment 11 (11): 1661–1673, 2018.
Pandey, V., van Rene, A., Kipf, A., and Kemper, A. How good are modern spatial libraries? Data Science and Engineering vol. 6, pp. 192–208, 2021.
Papadias, D., Sellis, T., Theodoridis, Y., and Egenhofer, M. J. Topological relations in the world of minimum bounding rectangles: A study with R-trees. In ACM SIGMOD International Conference on Management of Data. pp. 92–103, 1995.
Samet, H. The quadtree and related hierarchical data structures. ACM Computing Surveys 16 (2): 187–260, 1984.
Schneider, M. and Behr, T. Topological relationships between complex spatial objects. ACM Transactions on Database Systems 31 (1): 39–81, 2006.
Shen, J., Chen, M., and Liu, X. Classification of topological relations between spatial objects in two-dimensional space within the dimensionally extended 9-intersection model. Transactions in GIS 22 (2): 514–541, 2018.
Sidlauskas, D., Chester, S., Zacharatou, E. T., and Ailamaki, A. Improving spatial data processing by clipping minimum bounding boxes. In IEEE International Conference on Data Engineering. pp. 425–436, 2018.
Su, W.-T., Wei, H.-Y., Yeh, J.-H., and Chen, W.-C. An efficient approach based on polygon approximation to query spatial data on digital archiving system. In International Conference on Applied System Innovation. pp. 389–392, 2017.
Welzl, E. Smallest enclosing disks (balls and ellipsoids). In New Results and New Trends in Computer Science. pp. 359–370, 1991.
Wohlin, C. Guidelines for snowballing in systematic literature studies and a replication in software engineering. In International Conference on Evaluation and Assessment in Software Engineering. pp. 1–10, 2014.
Zimbrao, G. and Souza, J. M. A raster approximation for processing of spatial joins. In International Conference on Very Large Data Bases. pp. 558–569, 1998.
