Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most Semantic b applications rely on querying graphs, typically by using SPARQL with a triple store. Increasingly, applications also analyze properties of the graph structure to compute statistical inferences. The current Semantic b infrastructure, hover, does not efficiently support such operations. This forces developers to extract the relevant data for external statistical post-processing.   propose to rethink query execution in a triple store as a highly parallelized asynchronous graph exploration on an active index data structure. This approach also allows to integrate SPARQL-querying with the sampling of graph properties.   To evaluate this architecture  implemented Random Walk TripleRush, which is built on a distributed graph processing system. Our evaluations show that this architecture enables both competitive graph querying, as ll as the ability to execute various types of random walks with restarts that sample interesting graph properties. Thanks to the asynchronous architecture, first results are sometimes returned in a fraction of the full execution time.  also evaluate the scalability and show that the architecture supports fast query-times on a dataset with more than a billion triples.
['Philip Stutz', 'Bibek Paudel', 'Mihaela Verman', 'Abraham Bernstein']
I am writing a research paper on a idea Random Walk TripleRush: Asynchronous Graph Querying and Sampling
[{"from": "human", "value": "I am writing a research paper on a idea Random Walk TripleRush: Asynchronous Graph Querying and Sampling"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most Semantic b applications rely on querying graphs, typically by using SPARQL with a triple store. Increasingly, applications also analyze properties of the graph structure to compute statistical inferences. The current Semantic b infrastructure, hover, does not efficiently support such operations. This forces developers to extract the relevant data for external statistical post-processing.   propose to rethink query execution in a triple store as a highly parallelized asynchronous graph exploration on an active index data structure. This approach also allows to integrate SPARQL-querying with the sampling of graph properties.   To evaluate this architecture  implemented Random Walk TripleRush, which is built on a distributed graph processing system. Our evaluations show that this architecture enables both competitive graph querying, as ll as the ability to execute various types of random walks with restarts that sample interesting graph properties. Thanks to the asynchronous architecture, first results are sometimes returned in a fraction of the full execution time.  also evaluate the scalability and show that the architecture supports fast query-times on a dataset with more than a billion triples."}]