Space-Adaptive and Workload-Aware Replication and Partitioning for Distributed RDF Triple Stores
The efficient distributed processing of big RDF graphs requires typically decreasing the communication cost over the network. This requires on the storage level both a careful partitioning (in order to keep the queried data in the same machine), and a careful data replication strategy (in order to enhance the probability of a query finding the required data locally). Analyzing the collected workload trend can provide a base to highlight the more important parts of the data set that are expected to be targeted by future queries. However, the outcome of such analysis is highly affected by the type and diversity of the collected workload and its correlation with the used application. In addition, the replication type and size are limited by the amount of available storage space. Both of the two main factors, workload quality and storage space, are very dynamic on practical system. In this work we present our adaptable partitioning and replication approach for a distributed RDF triples store. The approach enables the storage layer to adapt with the available size of storage space and with the available quality of workload aiming to give the most optimized performance under these variables.
The authors would like to thank Deutscher Akademischer Austauschdienst (DAAD) for providing funds for research on this project.
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