Answering Provenance-Aware Queries on RDF Data Cubes under Memory Budgets


The steadily-growing popularity of semantic data on the Web and the support for aggregation queries in SPARQL 1.1 have propelled the interest in Online Analytical Processing (OLAP) and multidimensional data (cubes) in RDF. Query processing in such settings is challenging because SPARQL OLAP queries tend to be complex: they usually contain many triple patterns with grouping and aggregation. Moreover, one important factor of query answering on web data is its provenance, i.e., metadata that tells us about the origin and quality of the data. Some applications, e.g., in data analytics, access control, etc., require to augment the data with provenance metadata and run queries that impose constraints on this provenance. This task is called provenance-aware query answering. In this paper, we investigate the benefit of caching some parts of an RDF cube augmented with provenance information when answering provenance-aware SPARQL queries. We propose provenance-aware caching (PAC), a caching approach based on a provenance-aware partitioning for RDF graphs, and a benefit model optimized for RDF cubes and SPARQL queries with aggregation. Our results on real and synthetic data show that PAC outperforms the LRU (least recently used) caching strategy and the Jena TDB native caching in terms of hit-rate and performance.