A Dynamic Query Optimization on a Sparql Endpoint by Approximate Inference Processing

On a retrieval of Linked Open Data using SPARQL, it is important to construct an efficient query that considers its execution cost, especially when the query utilizes inference capability on the endpoint. A query often causes enormous consumption of endpoints' computing resources since it is sometimes difficult to understand and predict what computations will occur on the endpoints. Preventing such an execution of time-consuming queries, approximating the original query could reduce loads of endpoints. In this paper, we present a preliminary idea and its concept on building endpoints having a mechanism to automatically avoid unwanted amount of inference computation by predicting its computational costs and allowing it to transform such a query into speed optimized query. Our preliminary experiment shows a potential benefit on speed optimizations of query executions by applying query rewriting approach. We also present a preliminary prototype system that classifies whether a query execution is time-consuming or not by using machine learning techniques at the endpoint-side.