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Recently, the number and size of RDF data collections has increased rapidly making the issue of scalable processing techniques crucial. The MapReduce model has become a de facto standard for large scale data processing using a cluster of machines in the cloud. Generally, RDF query processing creates join-intensive workloads, resulting in lengthy MapReduce workflows with expensive I/O, data transfer, and sorting costs. However, the MapReduce computation model provides limited static optimization techniques used in relational databases (e.g., indexing and cost-based optimization). Consequently, dynamic optimization techniques for such join-intensive tasks on MapReduce need to be investigated. In some previous efforts, we propose a Nested Triple Group data model and Algebra (NTGA) for efficient graph pattern query processing in the cloud. Here, we extend this work with a scan-sharing technique …