Abstract
JSON (JavaScript Object Notation) has become popular for exchanging data sets over the Internet. Many data sets are “geo-tagged”, since they represent spatial entities. As an effect, spatial analysts have to perform spatial queries on JSON data sets. While working with large data sets, crisp (on/off) spatial relations could be marginally effective; instead, soft relations and “soft spatial querying” could be the right tools, because they reveal the extent of a given spatial relation. In this paper, we present the recent evolution of J-CO-QL \(^+\), the query language of the J-CO Framework (under development at University of Bergamo, Italy) towards soft spatial querying on geo-tagged JSON data sets.
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Github repository - https://github.com/JcoProjectTeam/JcoProjectPage.
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Fosci, P., Psaila, G. (2022). Soft Spatial Querying on JSON Data Sets. In: Chiusano, S., Cerquitelli, T., Wrembel, R. (eds) Advances in Databases and Information Systems. ADBIS 2022. Lecture Notes in Computer Science, vol 13389. Springer, Cham. https://doi.org/10.1007/978-3-031-15740-0_17
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