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2001
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Mining Multi-Dimensional Constrained Gradients in Data Cubes

9 years 2 months ago
Mining Multi-Dimensional Constrained Gradients in Data Cubes
Constrained gradient analysis (similar to the “cubegrade” problem posed by Imielinski, et al. [9]) is to extract pairs of similar cell characteristics associated with big changes in measure in a data cube. Cells are considered similar if they are related by roll-up, drill-down, or 1-dimensional mutation operation. Constrained gradient queries are expressive, capable of capturing trends in data and answering “what-if” questions. To facilitate our discussion, we call one cell in a gradient pair probe cell and the other gradient cell. An efficient algorithm is developed, which pushes constraints deep into the computation process, finding all gradient-probe cell pairs in one pass. It explores bi-directional pruning between probe cells and gradient cells, utilizing transformed measures and dimensions. Moreover, it adopts a hyper-tree structure and an H-cubing method to compress data and maximize sharing of computation. Our performance study shows that this algorithm is efficient...
Guozhu Dong, Jiawei Han, Joyce M. W. Lam, Jian Pei
Added 30 Jul 2010
Updated 30 Jul 2010
Type Conference
Year 2001
Where VLDB
Authors Guozhu Dong, Jiawei Han, Joyce M. W. Lam, Jian Pei, Ke Wang
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