Abstract
This demo presents KAMEL; a novel trajectory imputation framework that aims to impute sparse trajectories as a means of increasing their accuracy, and hence the accuracy of their applications. Unlike the large majority of current trajectory imputation techniques, KAMEL does not require the knowledge or the availability of the underlying road network, which makes it applicable to important applications like map inference that need to infer the road network itself. Audience will experience KAMEL through various scenarios that show the imputation accuracy as well as KAMEL internals.
| Original language | English (US) |
|---|---|
| Title of host publication | SIGMOD 2023 - Companion of the 2023 ACM/SIGMOD International Conference on Management of Data |
| Publisher | Association for Computing Machinery |
| Pages | 191-194 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781450395076 |
| DOIs | |
| State | Published - Jun 4 2023 |
| Event | 2023 ACM/SIGMOD International Conference on Management of Data, SIGMOD 2023 - Seattle, United States Duration: Jun 18 2023 → Jun 23 2023 |
Publication series
| Name | Proceedings of the ACM SIGMOD International Conference on Management of Data |
|---|---|
| ISSN (Print) | 0730-8078 |
Conference
| Conference | 2023 ACM/SIGMOD International Conference on Management of Data, SIGMOD 2023 |
|---|---|
| Country/Territory | United States |
| City | Seattle |
| Period | 6/18/23 → 6/23/23 |
Bibliographical note
Funding Information:This work is supported by NSF under grants IIS-1907855 and IIS-2203553.
Publisher Copyright:
© 2023 ACM.
Keywords
- trajectory BERT
- trajectory NLP
- trajectory imputation
