Issue No. 03 - March (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.253
Man Lung Yiu , Hong Kong Polytechnic University, Hong Kong
Eric Lo , Hong Kong Polytechnic University, Hong Kong
Duncan Yung , Hong Kong Polytechnic University, Hong Kong
Data cube is a key element in supporting fast OLAP. Traditionally, an aggregate function is used to compute the values in data cubes. In this paper, we extend the notion of data cubes with a new perspective. Instead of using an aggregate function, we propose to build data cubes using the skyline operation as the “aggregate function.” Data cubes built in this way are called “group-by skyline cubes” and can support a variety of analytical tasks. Nevertheless, there are several challenges in implementing group-by skyline cubes in data warehouses: 1) the skyline operation is computational intensive, 2) the skyline operation is holistic, and 3) a group-by skyline cube contains both grouping and skyline dimensions, rendering it infeasible to precompute all cuboids in advance. This paper gives details on how to store, materialize, and query such cubes.
Query processing; data warehouse and repository.
D. Yung, E. Lo and M. L. Yiu, "Measuring the Sky: On Computing Data Cubes via Skylining the Measures," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 492-505, 2010.