Spatio-temporal data services in a shared-nothing environment

Recently, there has been a proliferation of applications that produce spatiotemporal data that has to be processed, stored and queried efficiently. These applications necessitate the execution of millions of updates in order to keep the underlying database up-to-date. Consequently, there is a need for spatiotemporal data management systems that are able to support such update intensive operations. Moreover, these systems should offer users the capability to examine present as well as past (historical) data versions in an on-line fashion. We propose a system that exploits the inherent parallelism of a shared-nothing computing environment for storing and indexing the spatiotemporal data. We describe our proposed system architecture, data organization, and outline techniques for ensuring robustness and scalability under excessive query loads and high update rates.