| Title | Enhancing Learned Index for A Higher Recall Trajectory K-Nearest Neighbor Search |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Ramadhan, Hani, Kwon, Joonho |
| Conference Name | 2021 IEEE International Conference on Big Data (Big Data) |
| Keywords | Big Data, big trajectory data, Conferences, Indexes, learned index, Measurement, Metrics, nearest neighbor search, pubcrawl, Time factors, Trajectory |
| Abstract | 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. |
| DOI | 10.1109/BigData52589.2021.9671654 |
| Citation Key | ramadhan_enhancing_2021 |
Enhancing Learned Index for A Higher Recall Trajectory K-Nearest Neighbor Search