Mining Multi-Dimensional Constrained Gradients in Data Cubes
Document Type
Conference Proceeding
Publication Date
9-2001
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Abstract
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 and scalable.
Repository Citation
Dong, G.,
Han, J.,
Lam, J.,
Pei, J.,
& Wang, K.
(2001). Mining Multi-Dimensional Constrained Gradients in Data Cubes. Proceedings of the Twenty-Seventh International Conference on Very Large Databases, 321-330.
https://corescholar.libraries.wright.edu/knoesis/417
Comments
Presented at the Twenty-Seventh International Conference on Very Large Databases, Rome, Italy, September 11-14, 2001.