Streaming saturation for large RDF graphs with dynamic schema information
Farvardin, Mohammad Amin; Colazzo, Dario; Belhajjame, Khalid; Sartiani, Carlo (2019), Streaming saturation for large RDF graphs with dynamic schema information, in Alvin Cheung, Kim Nguyễn, Proceedings of the 17th ACM SIGPLAN International Symposium on Database Programming Languages (DBPL 2019 ), ACM - Association for Computing Machinery : New York, NY, p. 42-52. 10.1145/3315507.3330201
TypeCommunication / Conférence
Conference titleProceedings of the 17th ACM SIGPLAN International Symposium on Database Programming Languages (DBPL 2019 )
Conference cityNew York, NY
Book titleProceedings of the 17th ACM SIGPLAN International Symposium on Database Programming Languages (DBPL 2019 )
Book authorAlvin Cheung, Kim Nguyễn
MetadataShow full item record
Abstract (EN)In the Big Data era, RDF data are produced in high volumes. While there exist proposals for reasoning over large RDF graphs using big data platforms, there is a dearth of solutions that do so in environments where RDF data are dynamic, and where new instance and schema triples can arrive at any time. In this work, we present the first solution for reasoning over large streams of RDF data using big data platforms. In doing so, we focus on the saturation operation, which seeks to infer implicit RDF triples given RDF schema constraints. Indeed, unlike existing solutions which saturate RDF data in bulk, our solution carefully identifies the fragment of the existing (and already saturated) RDF dataset that needs to be considered given the fresh RDF statements delivered by the stream. Thereby, it performs the saturation in an incremental manner. Experimental analysis shows that our solution outperforms existing bulk-based saturation solutions.
Subjects / KeywordsRDF saturation; RDF streams; Big Data; Spark
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