VOLT: A Provenance-Producing, Transparent SPARQL Proxy for the On-Demand Computation of Linked Data and its Application to Spatiotemporally Dependent Data

Powered by Semantic Web technologies, the Linked Data paradigm aims at weaving a globally interconnected graph of raw data that transforms the ways we publish, retrieve, share, reuse, and integrate data from a variety of distributed and heterogeneous sources. In practice, however, this vision faces substantial challenges with respect to data quality, coverage, and longevity, the amount of background knowledge required to query distant data, the reproducibility of query results and their derived (scientific) findings, and the lack of computational capabilities required for many tasks. One key issue underlying these challenges is the trade-off between storing data and computing them. Intuitively, data that is derived from already stored data, changes frequently in space and time, or is the result of some workflow or procedure, should be computed. However, this functionality is not readily available on the Linked Data cloud with its current technology stack. In this work, we introduce a proxy that can transparently run on top of arbitrary SPARQL endpoints to enable the on-demand computation of Linked Data together with the provenance information required to understand how they were derived. While our work can be generalized to multiple domains, we focus on two geographic use cases to showcase the proxy's capabilities.