Algorithms used for Updates in Streaming Data Warehouses by Scalable Scheduling

This project includes jobs correspond to processes that load new data into tables, and whose objective is to minimize data staleness over time. The proposed framework handles the complications encountered by a stream warehouse: view hierarchies and priorities, data consistency, inability to preempt updates, heterogeneity of update jobs caused by different interarrival times and data volumes among different sources, and transient overload. A novel feature of our framework is that scheduling decisions depend on the effect of update jobs on data staleness.