Soft Querying JSON Datasets with Personalized Preferences and Aggregations
Paolo Fosci, Giuseppe Psaila · 2024
Soft conditions are a powerful and established formal tool to select data on the basis of linguistic predicates. In previous work, the J-CO Framework (and its query language) was used to perform Soft Web Intelligence, i.e., a practical interpretation of the concept of Web Intelligence that exploits soft conditions to search for desired items in JSON datasets acquired from Web sources. However, the effectiveness of soft conditions depends on how elementary conditions are combined: in this sense, a plethora of proposals are available, such as the vector p-norm. This paper shows how a generic concept, named "user-defined fuzzy evaluator", that has been recently introduced in the query language, actually allows users to define their own operators, so as to express advanced operators such as "and possibly". The paper also shows how the AND operator defined as a vector p-norm actually behaves, depending on different configurations of parameters, so as to let the reader understand how to use it in practice.