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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CITATION STYLE
Fosci, P., & Psaila, G. (2022). Soft Spatial Querying on JSON Data Sets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13389 LNCS, pp. 223–237). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-15740-0_17
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