Soft querying on databases (i.e., selecting data items that partially match selection conditions) was investigated on top of classical relational databases in past research works; however, constraints and limitations posed by relational DBMSs significantly limited the practical effects of this research. The advent of JSON as the format for representing and sharing data over the Internet, together with the birth of JSON document stores (a specific category of NoSQL databases), is now changing the panorama. In fact, the need to integrate and query large JSON data sets is now calling for novel and powerful tools for managing and integrating JSON data sets in a flexible way. At the University of Bergamo (Italy), we are devising the J-CO Framework, which is a platform-independent tool that relies on a high-level and general-purpose language named J-CO-QL⁺: among all its features, it provides capabilities towards “soft querying” of JSON documents.

However, a general-purpose language, although extremely powerful, cannot provide support for domain-specific computations that often relies on procedural algorithms. In this paper, we show how supporting user-defined functions actually empowers J-CO-QL⁺ users towards applying soft querying on JSON data sets. User-defined functions written both in JavaScript and in Java are accepted by the J-CO-QL⁺ Engine: in this paper, we present how to define them and the different execution performance.