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Analytical queries over RDF data are becoming prominent as a result of the proliferation of knowledge graphs. Yet, RDF databases are not optimized to perform such queries efficiently, leading to long processing times. A well known technique to improve the performance of analytical queries is to exploit materialized views.Although popular in relational databases, view materialization for RDF and SPARQL has not yet transitioned into practice, due to the non-trivial application to the RDF graph model. Motivated by a lack of understanding of the impact of view materialization alternatives for RDF data, we demonstrate Sofos, a system that implements and compares several cost models for view materialization. Sofos is, to the best of our knowledge, the first attempt to adapt cost models, initially studied in relational data, to the generic RDF setting, and to propose new ones, analyzing their pitfalls and merits. Sofos takes an RDF dataset and an analytical query for some facet in the data, and compares and evaluates alternative cost models, displaying statistics and insights about time, memory consumption, and query characteristics.
|Title of host publication||SIGMOD 2021 - Proceedings of the 2021 International Conference on Management of Data|
|Number of pages||5|
|Place of Publication||New York|
|Publisher||Association for Computing Machinery|
|Publication date||9 Jun 2021|
|Publication status||Published - 9 Jun 2021|
|Event||2021 International Conference on Management of Data, SIGMOD 2021 - Virtual, Online, China|
Duration: 20 Jun 2021 → 25 Jun 2021
|Conference||2021 International Conference on Management of Data, SIGMOD 2021|
|Period||20/06/2021 → 25/06/2021|
|Series||Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems|
Bibliographical noteFunding Information:
Matteo Lissanrini is supported by the EU’s H2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 838216. This research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (iQARuS Project No 1147).
© 2021 ACM.
- knowledge graphs
- view selection
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- 1 Finished
EDAO: EDAO: Example Driven Analytics for Open Knowledge Graphs
Lissandrini, M., Pedersen, T. B. & Hose, K.
15/09/2019 → 14/09/2021