## 5 Conclusions  
This paper proposed a distributed SPARQL query processing scheme considering communication costs between nodes in Spark environments. In the proposed scheme, the disk I/O cost incurred during query processing while parsing a large amount of RDF data, which was a problem in existing schemes, was reduced as the disk I/O was processed in the Spark environment (an in-memory environment). In addition, the proposed scheme performed query processing through the query execution path created considering data parsing cost, data communication cost, join cost, and queuing time. The performance evaluation results showed that the proposed scheme improved the performance of query processing time, transfer time, and query processing time in each node by around 15% compared to an existing scheme, which was a distributed SPARQL query processing-related scheme in the Spark environment. In addition, no or large difference in performance was revealed between them, according to the query used. If a query was simple, thus that the number of nodes used during query processing was small, or if the number of query results was smaller, no significant difference in performance was exhibited between the existing and proposed schemes. However, when a query was complex, or the size of query results was large, a performance difference was large. In the near future, a study on load management between nodes during query processing will be conducted.  
Figure 10: Query processing time by data size: (**a**) comparison through query whose intermediate result is small; (**b**) comparison through query whose intermediate result is large.  
**Author Contributions:** Conceptualization, J.L., B.K., H.L., D.C., K.B. and J.Y.; methodology, J.L., B.K., H.L., D.C., K.B. and J.Y.; validation, J.L., B.K., H.L., D.C. and K.B.; formal analysis, J.L., B.K., H.L., D.C. and K.B.; writing--original draft preparation, J.L., B.K., H.L., D.C. and K.B.; writing--review and editing, J.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ01624701) Rural Development Administration, Republic of Korea, the MSIT(Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2021-2020-0-01462) supervised by the IITP (Institute for Information and communications Technology Planning and Evaluation), Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2014-3-00123, Development of High-Performance Visual Big Data Discovery Platform for Large-Scale Realtime Data Analysis), and Korea Institute for Advancement of Technology(KIAT) grant funded by the Korea Government(MOTIE) (P0008421, The Competency Development Program for Industry Specialist).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.