High Throughput Indexing for Large-scale Semantic Web Data

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High Throughput Indexing for Large-scale Semantic Web Data

Long ChengLong Cheng,  Spyros KotoulasSpyros Kotoulas,  Tomas E WardTomas E Ward,  Georgios TheodoropoulosGeorgios Theodoropoulos
Long Cheng, Spyros Kotoulas, Tomas E Ward, Georgios Theodoropoulos
High Throughput Indexing for Large-scale Semantic Web Data
Proc. 30th ACM/SIGAPP Symposium On Applied Computing (SAC'15), 416-422, April 2015. ACM
  • KurzfassungAbstract
    Distributed RDF data management systems become increasingly important with the growth of the Semantic Web. Currently, several such systems have been proposed, however, their indexing methods meet performance bottlenecks either on data loading or querying when processing large amounts of data. In this work, we propose a high throughout index to enable rapid analysis of large datasets. We adopt a hybrid structure to combine the loading speed of similar-size based methods with the execution speed of graph-based approaches, using dynamic data repartitioning over query workloads. We introduce the design and detailed implementation of our method. Experimental results show that the proposed index can indeed vastly improve loading speeds while remaining competitive in terms of performance. Therefore, the method could be considered as a good choice for RDF analysis in large-scale distributed scenarios.
  • Weitere Informationen unter:Other info: Link
  • Projekt:Project: DIAMOND
  • Forschungsgruppe:Research Group: Wissensbasierte Systeme
@inproceedings{CKWT2015,
  author    = {Long Cheng and Spyros Kotoulas and Tomas E Ward and Georgios
               Theodoropoulos},
  title     = {High Throughput Indexing for Large-scale Semantic Web Data},
  booktitle = {Proc. 30th {ACM/SIGAPP} Symposium On Applied Computing (SAC'15)},
  publisher = {ACM},
  year      = {2015},
  month     = {April},
  pages     = {416-422},
  doi       = {10.1145/2695664.2695920}
}