Emerging application areas of computing technologies exist that involve the monitoring of continuous variables. One such area concerns transportation. Such monitoring yields massive update loads that existing systems are unable to contend with. The Sensload project proposes a two-pronged approach to enabling the support of such application areas. It explores techniques for selective shedding of updates and it develops data structures and algorithms that increase the numbers of updates per time unit that database systems are able to accommodate. Specifically, the data structures and algorithms that leverage memory hierarchies and parallelism of modern computing hardware are explored.

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