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Various distributed processing schemes were studied to efficiently utilize a large scale of RDF graph in semantic web services. This paper proposes a new distributed SPARQL query processing scheme considering communication costs in Spark environments to reduce I/O costs during SPARQL query processing. We divide a SPARQL query into several subqueries using a WHERE clause to process a query of an RDF graph stored in a distributed environment. The proposed scheme reduces data communication costs by grouping the divided subqueries in related nodes through the index and processing them, and the grouped subqueries calculate the cost of all possible query execution paths to select an efficient query execution path. The efficient query execution path is selected through the algorithm considering the data parsing cost of all possible query execution paths, amount of data communication, and queue time per node. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

Incremental graph processing has been developed to reduce unnecessary redundant calculations in dynamic graphs. In this paper, we propose an incremental dynamic graph-processing scheme using a cost model to selectively perform incremental processing or static processing. The cost model calculates the predicted values of the detection cost and processing cost of the recalculation region based on the past processing history. If there is a benefit of the cost model, incremental query processing is performed. Otherwise, static query processing is performed because the detection cost and processing cost increase due to the graph change. The proposed incremental scheme reduces the amount of computation by processing only the changed region through incremental processing. Further, it reduces the detection and disk I/O costs of the vertex, which are calculated by reusing the subgraphs from the previous results. The processing structure of the proposed scheme stores the data read from the cache and the adjacent vertices and then performs only memory mapping when processing these graph. It is demonstrated through various performance evaluations that the proposed scheme outperforms the existing schemes.

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In this study, we propose three k-nearest neighbor (k-NN) optimization techniques for a distributed, in-memory-based, high-dimensional indexing method to speed up content-based image retrieval. The proposed techniques perform distributed, in-memory, high-dimensional indexing-based k-NN query optimization: a density-based optimization technique that performs k-NN optimization using data distribution; a cost-based optimization technique using query processing cost statistics; and a learning-based optimization technique using a deep learning model, based on query logs. The proposed techniques were implemented on Spark, which supports a master/slave model for large-scale distributed processing. We showed the superiority and validity of the proposed techniques through various performance evaluations, based on high-dimensional data.