EMA attaches Markers as compact summaries to graph edges for predicate-aware guidance in filtering ANN search, delivering 1.68x-12.25x speedups over prior general filtering methods while supporting dynamic updates.
An Analytical Study of Large SPARQL Query Logs
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abstract
With the adoption of RDF as the data model for Linked Data and the Semantic Web, query specification from end- users has become more and more common in SPARQL end- points. In this paper, we conduct an in-depth analytical study of the queries formulated by end-users and harvested from large and up-to-date query logs from a wide variety of RDF data sources. As opposed to previous studies, ours is the first assessment on a voluminous query corpus, span- ning over several years and covering many representative SPARQL endpoints. Apart from the syntactical structure of the queries, that exhibits already interesting results on this generalized corpus, we drill deeper in the structural char- acteristics related to the graph- and hypergraph represen- tation of queries. We outline the most common shapes of queries when visually displayed as pseudographs, and char- acterize their (hyper-)tree width. Moreover, we analyze the evolution of queries over time, by introducing the novel con- cept of a streak, i.e., a sequence of queries that appear as subsequent modifications of a seed query. Our study offers several fresh insights on the already rich query features of real SPARQL queries formulated by real users, and brings us to draw a number of conclusions and pinpoint future di- rections for SPARQL query evaluation, query optimization, tuning, and benchmarking.
fields
cs.DB 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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EMA: Approximate Nearest Neighbor Search with General Attribute Filtering and Dynamic Updates
EMA attaches Markers as compact summaries to graph edges for predicate-aware guidance in filtering ANN search, delivering 1.68x-12.25x speedups over prior general filtering methods while supporting dynamic updates.