Integrating Dynamically-Computed Data and Web APIs into “Virtual” Databases and Knowledge Graphs

Enabling transparent SQL/SPARQL access to both static and dynamically-computed data

Query languages for databases (e.g., SQL) and knowledge graphs (e.g., SPARQL) provide a concise, declarative, and highly flexible mechanism to access stored data. Yet, many use cases also involve dynamically-computed data available through web APIs or other forms of external services. In such settings, data access is comparatively less flexible (e.g., due to restrictions on available input/output methods), convenient, and sometimes prohibitively slow for users interactively querying data. In this talk, we discuss these problems and present open source solutions that enable querying dynamically-computed data as a “virtual” (since not fully materialized) relational database via SQL, or as a “virtual” knowledge graph via SPARQL, at the same time providing pre-computation and caching solutions to speed up data access. The core components presented in the talk have been developed in the context of the HIVE “Fusion Grant” project and the OntoCRM project, both involving UNIBZ and Ontopic srl. In both projects, we aim at extending virtual knowledge graphs to dynamically-computed data, with a particular focus on applications in the domains of environmental sustainability and climate risk management.