Adaptive Spatio-temporal Query Planning For Linked Sensor Data

Tracking #: 2771-3985

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Han Nguyen Mau
Hoan Nguyen Mau Quoc

Responsible editor: 
Armin Haller

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Full Paper
Researchers in the Semantic Web community have proposed a substantial number of works that use Semantic Web technologies for effectively managing and querying heterogeneous IoT data. However, our survey of research work has shown that the goal of providing an intelligent processing and analysis engine for IoT has still not been fully achieved. Central to this problem is the requirement for a semantic spatio-temporal query processing engine that is able to not only analyze spatio-temporal correlations in a massive amount of IoT data but can also generate an effective query plan for a given query to execute in a timely manner. In this paper, we propose an alternative query optimization solution that uses query similarity identification in conjunction with machine learning techniques to recommend a previously generated query plan to the optimizer for a given query. Our approach also aims to predict the query execution time for the purposes of workload management and capacity planning. Our extensive experiments indicate the efficiency of our learning approach with an impressive prediction accuracy on test queries.
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