A Scalable Spatio-temporal Data Storage for Intelligent Transportation Systems Based on HBase

The recent ubiquity of sensors and GPS-enabled devices has resulted in an explosion of spatio-temporal data generated from probe cars, traffic sensors, and smart phones. To benefit from such data, intelligent transportation systems (ITS) need data storage that can handle the massive volume of data and support high-computational spatio-temporal queries. Although key-value store databases efficiently handle large-scale data, they are not equipped with effective functions for supporting spatio-temporal data. To solve this problem, we propose a lightweight, but powerful, spatio-temporal index structure based on key-value store databases. We adopted STCode, a longitude, latitude, and time-encoding algorithm, to build an index on top of HBase, a standard key-value store database. Our proposed index structure allows continuous updates of objects and provides an efficient prefix filter for supporting spatio-temporal data retrieval. Experimental results demonstrate the high performance of spatio-temporal queries with response time meeting the requirements of real-time query-processing systems.