Incremental SPARQL Evaluation for Query Answering on Linked Data.

SPARQL is the standard query language for RDF data. However, its application to Linked Data is challenging because the assumption that all necessary data is present at the beginning of the evaluation does not apply. Some relevant data sources may only be discovered by processing available data. Existing approaches provide implementations that compute results for basic graph patterns incrementally while retrieving the data. We contribute to this area by a formal analysis of the SPARQL algebra to provide incremental adaptions of the operations. This enables us to evaluate the costs of the incremental evaluation for the design of optimizers that choose the presumably best computation depending on the number of insertions and deletions. In addition, we propose a construction of the SPARQL dataset from Linked Data resources that enables the usage of the GRAPH-operator in query answering for Linked Data.