KAFY: An Extensible and Scalable Transformers-Based System for Trajectory Data Analysis

Trajectory data analysis has been fundamental to widely used applications. Even though several research efforts have been dedicated to develop numerous trajectory analysis algorithms, there is an apparent lack of full-fledged trajectory analysis systems. The main reason is that each algorithm employs new methods that are tailored to one specific analysis task. This paper presents KAFY ; a full-fledged system that supports a myriad of trajectory analysis tasks. KAFY leverages the recent advances in Natural Language Processing (NLP) where the transformer architecture is introduced as a system infrastructure to support various NLP tasks. The main idea of KAFY is that instead of training a transformer with a (spoken) language to produce (language) models, it trains it with the (unspoken) trajectory language to produce (trajectory) models. KAFY is extensible with more transformers and/or trajectory operations. The first release of KAFY employs three transformers and supports five trajectory operations. Experimental results from a real deployment of KAFY show that it either outperforms or gives similar performance to existing baselines in all its supported operations.