Search

SPARQL query services that balance processing between clients and servers become more and more essential to handle the increasing load for open and decentralized knowledge graphs on the Web. To this end, Linked Data Fragments (LDF) have introduced a foundational framework that has sparked research exploring a spectrum of potential Web querying interfaces in between server-side query processing via SPARQL endpoints and client-side query processing of data dumps. Current proposals in between typically suffer from imbalanced load on either the client or the server. In this paper, to the best of our knowledge, we present the first work that combines both client-side and server-side query optimization techniques in a truly dynamic fashion: we introduce WiseKG, a system that employs a cost model that dynamically delegates the load between servers and clients by combining client-side processing of shipped partitions with efficient server-side processing of star-shaped sub-queries, based on current server workload and client capabilities. Our experiments show that WiseKG significantly outperforms state-of-the-art solutions in terms of average total query execution time per client, while at the same time decreasing network traffic and increasing server-side availability.

The European General Data Protection Regulation (GDPR) brings new challenges for companies, who must demonstrate that their systems and business processes comply with usage constraints specified by data subjects. However, due to the lack of standards, tools, and best practices, many organizations struggle to adapt their infrastructure and processes to ensure and demonstrate that all data processing is in compliance with users' given consent. The SPECIAL EU H2020 project has developed vocabularies that can formally describe data subjects' given consent as well as methods that use this description to automatically determine whether processing of the data according to a given policy is compliant with the given consent. Whereas this makes it possible to determine whether processing was compliant or not, integration of the approach into existing line of business applications and ex-ante compliance checking remains an open challenge. In this short paper, we demonstrate how the SPECIAL consent and compliance framework can be integrated into Linked Widgets, a mashup platform, in order to support privacy-aware ad-hoc integration of personal data. The resulting environment makes it possible to create data integration and processing workflows out of components that inherently respect usage policies of the data that is being processed and are able to demonstrate compliance. We provide an overview of the necessary meta data and orchestration towards a privacy-aware linked data mashup platform that automatically respects subjects' given consents. The evaluation results show the potential of our approach for ex-ante usage policy compliance checking within the Linked Widgets Platforms and beyond.