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Mining Multi-Dimensional Constrained Gradients in Data Cubes

Summary: Mining constrained gradient pairs (gradient-probe) in data cubes under roll-up, drill-down, and 1D mutations to reveal large measure changes. A single-pass, constraint-driven algorithm uses bi-directional pruning and a hyper-tree with H-cubing compression to maximize sharing and scalability. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
8756
Venue
VLDB
Year
2001
Pagerank
5.633298e-05
Overall Rank
5,202 | 63.82%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
2,448 Multi-Dimensional Regression Analysis of Time-Series Data Streams 2002 VLDB 8.8032353e-05
6,330 Efficient Construction of Approximate Ad-Hoc ML models Through Materialization and Reuse 2018 VLDB 5.1077416e-05
6,641 Prediction Cubes 2005 VLDB 4.97969e-05
13,762 CubeExplorer: Online Exploration of Data Cubes 2002 SIGMOD -
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
247 On the Computation of Multidimensional Aggregates 1996 VLDB 0.00030927763
472 Bottom-Up Computation of Sparse and Iceberg CUBEs 1999 SIGMOD 0.00022346384
597 Computing Iceberg Queries Efficiently 1998 VLDB 0.00019475592
1,626 Exploratory Mining and Pruning Optimizations of Constrained Association Rules 1998 SIGMOD 0.00011094469
1,955 Efficient Computation of Iceberg Cubes with Complex Measures 2001 SIGMOD 9.9629452e-05
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