Abstract
The graph database system can express the complex relationships in the real world through the simple and intuitive description of “entities” and“relationships”. Now, the graph database system is used to analyze complex relationship between entities, especially, in the scientific research field. For example, the RDF-based graph database system has been used for biological data processing. Many previous works have proved that the graph database system is very suite for biological researchers to store and analyze biological data. In this paper, we evaluate two graph database systems (Apache Jena and gStore) by the biological RDF data set, the biological RDF data set contains 10 millions pieces of data on the types of Uniport, Enzyme, Taxonomy and Gen. And we design five query workloads, which are “1-step”,“2-steps(p1)”, “2-steps(p2)”,“union” and “filtering” and one data load workload. The metrics which we evaluated including user-observed metrics (workload execution time), system metrics (CPU utilization, I/O wait ratio and memory bandwith) and micro-architecture metrics (IPC, cache miss and branch misprediction ratio). The experiment results show that gStore performs better in complex query workloads, and Jena is more suitable for the simple ones.
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Acknowledgment
This work is supported by the National Key Research and Development Plan of China (Grant No.2016YFB1000600 and 2016YFB1000601).
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Yu, M., Zang, Y., Dai, S., Zheng, D., Li, J. (2019). Evaluating Graph Database Systems for Biological Data. In: Ren, R., Zheng, C., Zhan, J. (eds) Big Scientific Data Benchmarks, Architecture, and Systems. SDBA 2018. Communications in Computer and Information Science, vol 911. Springer, Singapore. https://doi.org/10.1007/978-981-13-5910-1_3
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DOI: https://doi.org/10.1007/978-981-13-5910-1_3
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