Abstract
The Resource Description Framework (RDF) has been widely used in various applications or services as a model for displaying, sharing and connecting data. With the increase of RDF data scale, distributed RDF data management system becomes popular, but there are still many problems to be solved. To solve these problems, we proposed a distributed RDF data management system K2RDF based on the Porperty Chain model on the Kudu and Impala platforms. Kudu is a data storage engine that combines OLAP and OLTP scenario and Impala can process SQL queries in real time. The combination of these two platforms provides new options for processing RDF data, making storage more efficient and queries faster. The Property Chain model is derived from the RDF data content. The RDF data is divided into different parts stored in the corresponding attribute table by the class information extracted from RDF schema. In the attribute table, In the attribute table, each column corresponds to a property in the RDF class. This model can increase the data storage density and improve the query processing speed by reducing the number of the join operation. By comparing with the current popular distributed RDF data management systems in some experiments, our system has lower query latency and faster query speed.
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Manola, F., Miller, E., McBride, B.: RDF primer. W3C Recomm. 10(1–107), 6 (2004)
Dan, B., Guha, R.V.: RDF vocabulary description language 1.0: RDF Schema. W3C Recommendation (2004)
Eric, P., Andy, S.: SPARQL query language for RDF. W3C Recommendation (2008)
Beleau, F., Nolin, M.A., Tourigny, N., et al.: Bio2RDF: towards a mashup to build bioinformatics knowledge systems. J. Biomed. Inform. 41(5), 706–716 (2008)
Du, J.-H., Wang, H.-F., Ni, Y., Yu, Y.: HadoopRDF: a scalable semantic data analytical engine. In: Huang, D.-S., Ma, J., Jo, K.-H., Gromiha, M.M. (eds.) ICIC 2012. LNCS (LNAI), vol. 7390, pp. 633–641. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31576-3_80
Rohloff, K., Schantz, R.E.: High-performance, massively scalable distributed systems using the MapReduce software framework: the SHARD triple-store. In: Proceedings of the Programming Support Innovations for Emerging Distributed Applications, p. 4. ACM (2010)
Gurajada, S., Seufert, S., Miliaraki, I., et al.: TriAD: a distributed shared-nothing RDF engine based on asynchronous message passing. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 289–300. ACM (2014)
Quilitz, B., Leser, U.: Querying distributed RDF data sources with SPARQL. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 524–538. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68234-9_39
Özsu, M.T.: A survey of RDF data management systems. Front. Comput. Sci. 10(3), 418–432 (2016). https://doi.org/10.1007/s11704-016-5554-y
Lipcon, T., Alves, D., Burkert, D., et al.: Kudu: Storage for fast analytics on fast data. Apache (2015). https://kudu.apache.org/kudu.pdf
Kornacker, M., Behm, A., Bittorf, V., et al.: Impala: a modern, open-source SQL engine for Hadoop. In: Proceedings of the 7th Conference on Innovative Data Systems Research (CIDR), vol. 1, p. 9 (2015)
Zaharia, M., Chowdhury, M., Franklin, M.J., et al.: Spark: cluster computing with working sets. HotCloud 10(10–10), 95 (2010)
Shvachko, K., Kuang, H., Radia, S., et al.: The Hadoop distributed file system. In: Proceedings of 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST). IEEE, pp. 1–10 (2010)
Papailiou, N., Konstantinou, I., Tsoumakos, D., et al.: H2RDF: adaptive query processing on RDF data in the cloud. In: Proceedings of the 21st International Conference on World Wide Web, pp. 397–400. ACM (2012)
Vora, M.N.: Hadoop-HBase for large-scale data. In: Proceedings of 2011 International Conference on Computer Science and Network Technology, vol. 1, pp. 601–605. IEEE (2011)
Wilkinson, K.: Jena property table implementation. In: The Second Workshop on Scalable Semantic Web Knowledge Base Systems, Georgia, USA (2006)
Bornea, M.A., Dolby, J., Kementsietsidis, A., et al.: Building an efficient RDF store over a relational database. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 121–132. ACM (2013)
Abadi, D.J., Marcus, A., Madden, S.R., et al.: Scalable semantic web data management using vertical partitioning. In: Proceedings of the VLDB Endowment, pp. 411–422 (2007)
Schtzle, A., Przyjaciel-Zablocki, M., Skilevic, S., et al.: S2RDF: RDF querying with SPARQL on spark. Proc. VLDB Endow. 9(10), 804–815 (2016)
Shao, B., Wang, H., Li, Y.: The trinity graph engine. Technical Report 161291, Microsoft Research (2012)
Zeng, K., Yang, J., Wang, H., et al.: A distributed graph engine for web scale RDF data. Proc. VLDB Endow. 6(4), 265–276 (2013)
Schätzle, A., Przyjaciel-Zablocki, M., Neu, A., Lausen, G.: Sempala: interactive SPARQL query processing on Hadoop. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 164–179. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11964-9_11
Xin, R.S., Gonzalez, J.E., Franklin, M.J., Stoica, I.: GraphX: a resilient distributed graph system on spark. In: Proceedings of the First International Workshop on Graph Data Management Experiences and Systems, p. 2. ACM (2013)
Schätzle, A., Przyjaciel-Zablocki, M., Berberich, T., Lausen, G.: S2X: graph-parallel querying of RDF with GraphX. In: Wang, F., Luo, G., Weng, C., Khan, A., Mitra, P., Yu, C. (eds.) Big-O(Q)/DMAH -2015. LNCS, vol. 9579, pp. 155–168. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41576-5_12
Ösu, M.T., Valduriez, P.: Principles of Distributed Database Systems. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-030-26253-2
Liu, R., Xu, J.: GCM-bench: a benchmark for RDF data management system on microorganism data. In: Ren, R., Zheng, C., Zhan, J. (eds.) SDBA 2018. CCIS, vol. 911, pp. 3–14. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-5910-1_1
Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., et al.: DBpediaa large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web 6(2), 167–195 (2015)
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a large ontology from wikipedia and wordnet. Web Seman.: Sci. Servi. Agents World Wide Web 6(3), 203–217 (2008)
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This work is supported by the National Key Research and Development Plan of China (Grant No. 2016YFB1000600 and 2016YFB1000601).
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Chen, X., Qiu, B., Xu, J., Liu, R. (2021). K2RDF: A Distributed RDF Data Management System on Kudu and Impala. In: Wolf, F., Gao, W. (eds) Benchmarking, Measuring, and Optimizing. Bench 2020. Lecture Notes in Computer Science(), vol 12614. Springer, Cham. https://doi.org/10.1007/978-3-030-71058-3_4
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