Abstract
The existing keyword-based search algorithms based on streaming data are hard to meet the needs of users for real-time data processing. To solve this problem, multi-keyword parallel search algorithm for streaming RDF data (MPSASR) proposed in this paper combines the Spark and Redis frameworks to construct query subgraphs integrated with ontology based on the query keywords in real time. Associated with scoring function, regarding the high-priority query subgraph as a guide, parallel search is performed in the instance data, and finally the Top-k query results are returned. Of course, our algorithm uses a hash compression algorithm to compress RDF data, which reduces the space required. Moreover, our algorithm makes full use of historical data and effectively speeds up search efficiency. Our algorithm is experimentally verified to have great advantages in real-time search, response time, and search effects.
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References
Dell’Aglio, D., Della Valle, E., Calbimonte, J.P., et al.: RSP-QL semantics: a unifying query model to explain heterogeneity of RDF stream processing systems. Int. J. Semant. Web Inf. Syst. (IJSWIS) 10(4), 17–44 (2014)
Barbieri, D.F., Braga, D., Ceri, S., et al.: C-SPARQL: a continuous query language for RDF data streams. Int. J. Semant. Comput. 4(01), 3–25 (2010)
Le-Phuoc, D., Dao-Tran, M., Xavier Parreira, J., Hauswirth, M.: A native and adaptive approach for unified processing of linked streams and linked data. In: Aroyo, L., et al. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 370–388. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25073-6_24
Borthakur, D., Gray, J., Sarma, J.S., et al.: Apache Hadoop goes realtime at Facebook. In: ACM SIGMOD International Conference on Management of Data, SIGMOD 2011, Athens, Greece, pp. 1071–1080, June 2011
Hou, R., Fang, J., Zhang, J.: Data query method for real-time streaming data protection. J. Comput. Appl. 31(9), 2736–2740 (2014)
Xu, W.: Research on streaming data real-time query method. Shandong University (2015)
Jiang, C., Ji, Y., Sun, Y., et al.: Storm-oriented real-time streaming query system design for big data. J. Nanjing Univ. Posts Telecommun. 36(3), 100–105 (2016)
Zhu, M., Cheng, J., Bai, W.: An RDF data storage model based on HBase. J. Comput. Res. Dev. 50(s1), 23–31 (2013)
RDF concepts and abstract syntax. http://www.w3.org/TR/rdf-concepts/
Li, H., Ran, Y.: KREAG: RDF data keyword query method based on the relationship of entity triads. Chin. J. Comput. 34(5), 825–835 (2011)
De Virgilio, R., Maccioni, A.: Distributed keyword search over RDF via MapReduce. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 208–223. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07443-6_15
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Guan, J., Wang, J., Yu, L. (2018). Multi-keyword Parallel Search Algorithm for Streaming RDF Data. In: Xu, Z., Gao, X., Miao, Q., Zhang, Y., Bu, J. (eds) Big Data. Big Data 2018. Communications in Computer and Information Science, vol 945. Springer, Singapore. https://doi.org/10.1007/978-981-13-2922-7_33
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DOI: https://doi.org/10.1007/978-981-13-2922-7_33
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