What's in a Query: Analyzing, Predicting, and Managing Linked Data Access
The term Linked Data refers to connected information sources comprising structured data about a wide range of topics and for a multitude of applications. In recent years, the conceptional and technical foundations of Linked Data have been formalized and reﬁned. To this end, well-known technologies have been established, such as the Resource Description Framework (Rdf) as a Linked Data model or the SPARQL Protocol and RDF Query Language (Sparql) for retrieving this information.
Whereas most research has been conducted in the area of generating and publishing Linked Data, this thesis presents novel approaches for improved management. In particular, we illustrate new methods for analyzing and processing Sparql queries.
Here, we present two algorithms suitable for identifying structural relationships between these queries. Both algorithms are applied to a large number of real-world requests to evaluate the performance of the approaches and the quality of their results. Based on this, we introduce different strategies enabling optimized access of Linked Data sources.
We demonstrate how the presented approach facilitates effective utilization of Sparql endpoints by prefetching results relevant for multiple subsequent requests.
Furthermore, we contribute a set of metrics for determining technical characteristics of such knowledge bases. To this end, we devise practical heuristics and validate them through thorough analysis of real-world data sources. We discuss the ﬁndings and evaluate their impact on utilizing the endpoints. Moreover, we detail the adoption of a scalable infrastructure for improving Linked Data discovery and consumption. As we outline in an exemplary use case, this platform is eligible both for processing and provisioning the corresponding information.