An Efficient Algorithm for Update Scheduling in Streaming Data Warehouses

Abstract: Traditional data warehouses are designed to hold the Historical Data. The data warehouse allows update only policy. Data Stream is a continuously growing Data from Various Sources. Update scheduling in streaming data warehouses, which combines the features of traditional data warehouses and data stream systems. External sources push append-only data streams into the warehouse with a wide range of inter arrival times. Traditional data warehouses are typically refreshed during downtimes, streaming ware houses are updated as new data arrive. We explore the model the streaming warehouse update problem as a scheduling problem, where processes that load new data into tables, and whose objective is to minimize data staleness over time.. We choose a scheduling framework that handles the complications encountered by a stream warehouse: view hierarchies and priorities, data consistency