Evolving J-CO-QL+ with fuzzy evaluators for flexible querying of JSON data sets
Paolo Fosci, Giuseppe Psaila · Neurocomputing · 2025
How to introduce soft querying based on fuzzy sets in the novel J-CO-QL + query language (specifically designed to query collections of JSON documents from NoSQL databases) has been investigated by the authors in their past work. Specifically, capabilities for defining fuzzy operators and fuzzy aggregators were introduced through two distinct concepts on which two different language constructs were based. This paper proposes the unified concept of “fuzzy evaluator”, by means of which it is possible to define complex methods for evaluating the membership degrees of JSON documents to fuzzy sets, so as to capture complex semantics while analyzing data in a soft way. The paper both provides a formal meta-model for fuzzy evaluators, and proposes a novel statement for the J-CO-QL + language, so as to further foster soft-querying capabilities.