Scheduling Updates in a Real-Time Stream Warehouse

This paper discusses updating a data warehouse that collects near-real-time data streams from a variety of external sources. The objective is to keep all the tables and materialized views up-to-date as new data arrive over time. We define the notion of data staleness, formalize the problem of scheduling updates in a way that minimizes average data staleness, and present scheduling algorithms designed to handle the complex environment of a real-time stream warehouse. A novel feature of our scheduling framework is that it considers the effect of an update on the staleness of the underlying tables rather than any property of the update job itself (such as deadline).