Optimizing segmented trajectory data storage with HBase for improved spatio-temporal query efficiency
Abstract
The surging accumulation of trajectory data has yielded invaluable insights into urban systems, but it has also presented challenges for data storage and management systems. In response, specialized storage systems based on non-relational databases have been developed to support large data quantities in distributed approaches. However, these systems often utilize storage by point or storage by trajectory methods, both of which have drawbacks. In this study, we evaluate the effectiveness of segmented trajectory data storage with HBase optimizations for spatio-temporal queries. We develop a prototype system that includes trajectory segmentation, serialization, and spatio-temporal indexing and apply it to taxi trajectory data in Beijing. Our findings indicate that the segmented system provides enhanced query speed and reduced memory usage compared to the Geomesa system.
Yi Bao
Postdoctoral Researcher
2023 - presentPhD Student
2018 - 2023
My research interests include Geographical Information Systems, Remote Sensing, Urban Data Mining, Deep Learning
Ganmin Yin
PhD Student
2020 - present
My research interests include Human Mobility, Transportation, Urban Data Mining, Social Sensing and GeoAI.
