Updates in Streaming Data Warehouses by Scalable Scheduling

The project includes a streaming data warehouse update problem as a scheduling problem where jobs correspond to the process that load new data into tables and the objective is to minimize data staleness over time. The proposed scheduling framework that handles the complications encountered by a stream warehouse: view hierarchies and priorities, data consistency, inability to pre-empt updates, heterogeneity of update jobs caused by different inter arrival times and data volumes among different sources and transient overload. Update scheduling in streaming data warehouses which combine the features of traditional data warehouses and data stream systems. The need for on-line warehouse refreshment introduces several challenges in the implementation of data warehouse transformations, with respect to their Execution time and their overhead to the warehouse processes. The problem with this approach is that new data may arrive on multiple streams, but there is no mechanism for limiting the number of tables that can be updated simultaneously.