Towards High Performance Spatio-temporal Data Management Systems

The volume of spatio-temporal data is growing at a rapid pace. This is driven by several factors, including the widespread adoption of GPS-enabled mobile devices and the proliferation of RFID-tagged objects in sensor networks. Besides the volume, such spatio-temporal data is characterized by high "velocity", with its high rate of time-stamped location updates. The rise of spatio-temporal "Big data" has led to the emergence of many novel location-oriented applications. These applications often have complex use-cases and service-level requirements. Efficient management of the spatio-temporal data is critical to meet these requirements. This poses some challenges and unique research questions, for instance: i) how to support the high rate of location updates, while at the same time supporting many concurrent historical, present and predictive queries, ii) what kind of database storage organization is suitable for such workload, iii) what are the implications for the spatio-temporal index, and iv) what kind of novel spatio-temporal queries are to be supported. Technological trends involving increasingly large main memory sizes and core counts offer opportunities to address some of these issues. We have addressed a few issues pertinent to high performance commercial Location-Based Services (LBS) by exploiting in-memory database techniques. We propose an in-memory storage organization for high insert performance and introduce a novel spatio-temporal index. With extensive evaluation, we demonstrate that our system supports high insert and query throughputs and it outperforms the leading LBS system by a significant margin. As part our future research we are building a spatio-temporal data management system in the context of a cluster of machines in the Cloud. We are also investigating the possibility of supporting trajectory-based join queries.