This manuscript proposed and explored a novel strategy for query pattern optimization towards parallel query planning and execution in Distributed RDF environments. The critical objective of the proposal is to optimize the query patterns from the query chains initiated to execute parallel in distributed RDF environment, which is unique regard to the earlier contributions related to parallel query planning and execution strategies found in contemporary literature. All of these existing models aimed to notify the query patterns from the given query chain, which are less significant to optimize the parallel process of the query patterns that discovered from multiple query chains submitted in parallel in distributed environment (such as cloud computing) to query the distributed triple stores. In order to this, the Discrete Rank based Query Pattern Optimization (DR-QPO) strategy is proposed. The DR-QPO optimizes the query patterns from multiple query chains initiated in parallel. A novel scale called Discrete Rank ConsistenceScore (DRDCS) defined, which uses the order of other metrics query pattern occurrence count, search space utilization, and access cost as input. The experiments conducted on the proposed model and other benchmark models found in contemporary literature. The results obtained from the experimental study evincing that the proposed model is significant and robust to optimize the query patterns in order to execute distribute query chains in parallel. The comparative analysis of the results obtained from DR-QPO and other contemporary models performed using ANOVA standards like t-test, Wilcoxon signed rank test.
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