A data distribution model for RDF


The ever-increasing amount of RDF data made available requires data to be partitioned across multiple servers. We have witnessed some research progress made towards scaling RDF query processing based on suitable data distribution methods. In general, they work well for queries matching simple triple patterns, but they are not efficient for queries involving more complex patterns. In this paper, we present an RDF data distribution method which overcomes the shortcomings of the current approaches in order to scale RDF storage both on the volume of data and query processing. We apply a method that identifies frequent patterns accessed by queries in order to keep related data in the same partition. We deploy our reasoning on a summarized view of data in order to avoid exhaustive analysis on large datasets. As result, partitioning templates are obtained from data items in an RDF structure. In addition, we provide an approach for dynamic data insertions even if new data do not conform to the original RDF structure. Apart from the repartitioning approaches, we use an overflow repository to store data which may not follow the original schema. Our study shows that our method scales well and is effective to improve the overall performance by decreasing the amount of message passing among servers, compared to alternative data distribution approaches for RDF.

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Schroeder, R., Penteado, R.R.M. & Hara, C.S. A data distribution model for RDF. Distrib Parallel Databases 39, 129–167 (2021). https://doi.org/10.1007/s10619-020-07296-w

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  • RDF
  • Data fragmentation
  • Data allocation
  • Distributed databases
  • Dynamic datasets