Ascendible Quantizing of Streaming Data Warehouse

In this paper we study update scheduling problem in streaming data warehouse, streaming data Warehouse combines the benefits of traditional data warehouses and data stream systems. In this problem jobs Corresponds to the processes which a load the new data in to the table, and main is aim is to minimize data staleness. Also it handle the challenges faced by streaming warehouse such as view hierarchies, preempt updates, data consistency and heterogeneity of update jobs caused by different arrival times and size of data. The scheduling metric is considered as staleness of data, we study the paper which explains how to schedule view updates in streams and transactions in soft real time database system, where two definitions of staleness are used one is MA (Maximum Age) and another is UU (unapplied Update). Four algorithms are examined for scheduling transactions And installing updates in soft real time database systems. Deadlines are very close To each other, SJF algorithm is used to schedule task within a group. Hence total execution time is minimized.

Keywords: about Ascendible quantizing, Data Stream management system, streaming data warehouse, QoD(Quality of Data),Strategy.