SPARQL endpoints are web services that allow for querying RDF knowledge graphs online using the SPARQL query language. In SPARQL 1.1, the federation extension allows for explicitly evaluating expressions of a query at certain SPARQL endpoints using the SERVICE keyword. However, the current foundations of federated SPARQL query processing rely on two central assumptions: (i) members of the federation are assumed to be equal in terms of quality, and (ii) a strict definition of answer completeness, where a SPARQL expression is evaluated over all relevant endpoints. These assumptions hinder the scalability of federated query processing approaches, as the number of contacted sources is potentially high. To address this challenge, in this work, we propose a solution that supports quality descriptions about federation members, and further exploit these descriptions to prune low-quality sources and to speed up query execution. To this end, we present a definition of quality-aware federations of SPARQL endpoints. Moreover, we define and investigate the semantics of recurring ALTERNATIVE SERVICE expressions which are the union of SERVICE requests around the same expression. We then devise a utility-aware semantics for them, in which quality information about the federation is leveraged to potentially reduce the number of endpoints to be contacted. We evaluate the proposed semantics in an experimental study and investigate the utility, execution time, and answer completeness of the proposed semantics for queries in over 60 quality-aware federations of varying size and quality.

Linked Data Fragments (LDFs) are Web interfaces that enable querying knowledge graphs on the Web. These interfaces, such as SPARQL endpoints or Triple Pattern Fragment servers, differ in the SPARQL expressions they can evaluate and the metadata they provide. So far, federated query processing has focused on federations with a single type of LDF interface, typically SPARQL endpoints. In this work, we address the challenges of SPARQL query processing over federations with heterogeneous LDF interfaces. To this end, we propose an interface-aware framework and illustrate its applicability with a prototypical approach. The results over the FedBench benchmark show a substantial improvement in performance by devising this interface-aware approach that exploits the capabilities of heterogeneous interfaces in federations.

Scalarization functions represent preferences in multi-objective optimization by mapping the vector of objectives to a single real value. Optimization techniques using scalarized preferences mainly focus on obtaining only a single global preference optimum. Instead, we propose considering all local and global scalarization optima on the global Pareto front. These points represent the best choice in their immediate neighborhood. Additionally, they are usually sufficiently far apart in the objective space to present themselves as true alternatives if the scalarization function cannot capture every detail of the decision maker's true preference. We propose an algorithmic framework for obtaining all scalarization optima of a multi-objective optimization problem. In said framework, an approximation of the global Pareto front is obtained, from which neighborhoods of local optima are identified. Local optimization algorithms are then applied to identify the optimum of every neighborhood. In this way, we have an optima-based approximation of the global Pareto front based on the underlying scalarization function. A computational study reveals that local optimization algorithms must be carefully configured for being able to find all optima.