Meta Galaxy: A Flexible and Efficient Cube Model for Data Retrieval in OLAP

OLAP is widely used in data analysis. The existing design models, such as star schema and snowflake schema, are not flexible when the data model is changed. For example, the task for inserting a dimension may involve complex operations over model and application implementation. To deal with this problem, a new cube model, called Meta Galaxy, is proposed. The main contributions of this work include: (1) analyzing the shortcoming of traditional design method, (2) proposing a new cube model which is flexible for dimension changes, and (3) designing an index structure and an algorithm to accelerate the cube query. The time complexity of query algorithm is linear. The extensive experiments on the real application and synthetic dataset show that Meta Galaxy is effective and efficient for cube query. Specifically, our method decreases the storage size by 95.12%, decreases the query time by 89.89% in average compared with SQL Server 2005, and has good scalability on data size.