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: 8757
Venue: VLDB
Year: 2001
Pagerank: 5.6278547e-05
Overall Rank: 5,204 | 63.84%
DOI: -

Incoming Non-self Citations Over Time

Authors

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	8.7935134e-05
6,329	Efficient Construction of Approximate Ad-Hoc ML models Through Materialization and Reuse	2018	VLDB	5.1030159e-05
6,642	Prediction Cubes	2005	VLDB	4.9747342e-05
13,776	CubeExplorer: Online Exploration of Data Cubes	2002	SIGMOD	-

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	0.00030923842
475	Bottom-Up Computation of Sparse and Iceberg CUBEs	1999	SIGMOD	0.00022238407
598	Computing Iceberg Queries Efficiently	1998	VLDB	0.00019431661
1,609	Exploratory Mining and Pruning Optimizations of Constrained Association Rules	1998	SIGMOD	0.00011166163
1,967	Efficient Computation of Iceberg Cubes with Complex Measures	2001	SIGMOD	9.9108189e-05

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