Spatio-Temporal Data Services in a Shared-Nothing Environment

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