Cymo: A Flexible Storage Model With Query-Aware Indexing for Spatio-Temporal Big Data

Spatio-temporal data are continuously generated and grow exponentially with the rapid proliferation of mobile devices. Efficient storage and indexing of spatio-temporal Big Data are crucial for supporting fast queries. Due to their high write throughput and scalability, distributed NoSQL data stores such as HBase are widely adopted as storage engines in spatio-temporal Big Data systems. These systems require a key-value-based storage model to convert one or a group of spatio-temporal data points into a single key-value pair. However, existing techniques cannot govern an inherent dilemma caused by adding a time dimension within spatial information, that is, we can either optimize for space-preferred or time-preferred queries but not both. In this paper, we propose Cymo, a flexible storage model with query-aware indexing that dynamically adapts to varying query patterns across different spatial regions and evolving query behaviors over time. Our key idea is to partition the spatio-temporal space into multiple subspaces, allowing each subspace to optimize its storage model based on its specific query characteristics. Cymo features a learning model that predicts query patterns for each subspace using historical workloads. Additionally, it introduces a virtual layer that abstracts the heterogeneity of different storage models, providing a unified query interface. This design ensures that storage model variations remain transparent to applications while enabling effective query optimization based on diverse query patterns. We have implemented Cymo on HBase and integrated it into GeoMesa, a representative spatio-temporal Big Data system. Experimental results on real taxi datasets demonstrate that Cymo significantly improves query performance, achieving speedups of 1.53× to 10× compared to GeoMesa. The open-source implementation of Cymo has been released for public access.