Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many emerging Semantic b applications combine and aggregate data across domains for analysis. Such analytical queries compute aggregates over multiple groupings of data, resulting in query plans with complex grouping-aggregation constraints. In the context of an RDF analytical query, each such grouping maps to a graph pattern subquery with multiple join operations, and related groups often result in overlapping graph patterns within the same query. ,  propose a holistic approach to optimize RDF analytical queries by refactoring queries to achieve shared execution of common subexpressions that enables parallel evaluation of groupings as ll as aggregations. Such a rewriting enables shorter execution workflows, particularly beneficial for scale-out processing on distributed Cloud systems with multiple I/O phases. Experiments on real-world and synthetic benchmarks confirm that such a rewriting can achieve more efficient execution plans when compared to relational-style SPARQL query plans executed on popular (Cloud systems.
['Padmashree Ravindra', 'HyeongSik Kim', 'Kemafor Anyanwu']
I am writing a research paper on a idea Rewriting complex SPARQL analytical queries for efficient cloud-based processing
[{"from": "human", "value": "I am writing a research paper on a idea Rewriting complex SPARQL analytical queries for efficient cloud-based processing"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many emerging Semantic b applications combine and aggregate data across domains for analysis. Such analytical queries compute aggregates over multiple groupings of data, resulting in query plans with complex grouping-aggregation constraints. In the context of an RDF analytical query, each such grouping maps to a graph pattern subquery with multiple join operations, and related groups often result in overlapping graph patterns within the same query. ,  propose a holistic approach to optimize RDF analytical queries by refactoring queries to achieve shared execution of common subexpressions that enables parallel evaluation of groupings as ll as aggregations. Such a rewriting enables shorter execution workflows, particularly beneficial for scale-out processing on distributed Cloud systems with multiple I/O phases. Experiments on real-world and synthetic benchmarks confirm that such a rewriting can achieve more efficient execution plans when compared to relational-style SPARQL query plans executed on popular (Cloud systems."}]