Efficient query processing framework for big data warehouse: an almost join-free approach

摘要

The rapidly increasing scale of data warehouses is challenging today’s data analytical technologies. A conventional data analytical platform processes data warehouse queries using a star schema — it normalizes the data into a fact table and a number of dimension tables, and during query processing it selectively joins the tables according to users’ demands. This model is space economical. However, it faces two problems when applied to big data. First, join is an expensive operation, which prohibits a parallel database or a MapReduce-based system from achieving efficiency and scalability simultaneously. Second, join operations have to be executed repeatedly, while numerous join results can actually be reused by different queries.