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

author: Blake Regalia, University of California, Santa Barbara
published: July 28, 2016,   recorded: June 2016,   views: 1481


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The Linked Data paradigm has changed how data on the Web is published, retrieved, and interlinked, thereby enabling modern question answering systems and contributing to the spread of open data. With the increasing size, interlinkage, and complexity of the Linked Data cloud, the focus is now shifting towards strategies and technologies to ensure that Linked Data can also succeed as an infrastructure. This raises questions about the sustainability of query endpoints, the reproducibility of scientific experiments conducted using Linked Data, the lack of established quality metrics, as well as the need for improved ontology alignment and query federation techniques. One core issue that needs to be addressed is the trade-off between storing data and computing them on-demand. Data that is derived from already stored data, changes frequently in space and time, or is the output of some workflow, should be computed. However, such functionality is not readily available on the Linked Data cloud today. To address this issue, we have developed a transparent SPARQL proxy that enables the on-demand computation of Linked Data together with the provenance information required to understand how the data were derived. Here, we demonstrate how the proxy works under the hood by applying it to the computation of cardinal directions between geographic features in DBpedia.

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