Trajectory-based applications have acquired significant attention over the past decade with the rising size of trajectory data generated by users. However, building trajectory-based applications is still cumbersome due to the lack of unified frameworks to tackle the underlying trajectory analysis challenges. Inspired by the tremendous success of the BERT deep learning model in solving various NLP tasks, our vision is to have a BERT-like system for a myriad of trajectory analysis operations. We envision that in a few years, we will have such system, where no one needs to worry again about each specific trajectory analysis operation. Whether it is trajectory imputation, similarity, clustering, or whatever, it would be one system that researchers, developers, and practitioners can deploy to get high accuracy for their trajectory operations.
|Original language||English (US)|
|Title of host publication||30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2022|
|Editors||Matthias Renz, Mohamed Sarwat, Mario A. Nascimento, Shashi Shekhar, Xing Xie|
|Publisher||Association for Computing Machinery|
|State||Published - Nov 1 2022|
|Event||30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2022 - Seattle, United States|
Duration: Nov 1 2022 → Nov 4 2022
|Name||GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems|
|Conference||30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2022|
|Period||11/1/22 → 11/4/22|
Bibliographical noteFunding Information:
∗The work of these authors is partially supported by the National Science Foundation (NSF), USA, under grants IIS-1907855 and IIS-2203553.
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