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Enhancing Learned Index for A Higher Recall Trajectory K-Nearest Neighbor Search. 2021 IEEE International Conference on Big Data (Big Data). :6006—6007.
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2021. Learned indices can significantly shorten the query response time of k-Nearest Neighbor search of points data. However, extending the learned index for k-Nearest Neighbor search of trajectory data may return incorrect results (low recall) and require longer pruning time. Thus, we introduce an enhancement for trajectory learned index which is a pruning step for a learned index to retrieve the k-Nearest Neighbors correctly by learning the query workload. The pruning utilizes a predicted range query that covers the correct neighbors. We show that that our approach has the potential to work effectively in a large real-world trajectory dataset.