Divide-and-approximate: A novel constraint push strategy for iceberg cube mining

Ke Wang, Yuelong Jiang, Jeffrey Xu Yu, Guozhu Dong, Jiawei Han

Research output: Contribution to journalArticlepeer-review


The iceberg cube mining computes all cells v, corresponding to GROUP BY partitions, that satisfy a given constraint on aggregated behaviors of the tuples in a GROUP BY partition. The number of cells often is so large that the result cannot be realistically searched without pushing the constraint into the search. Previous works have pushed antimonotone and monotone constraints. However, many useful constraints are neither antimonotone nor monotone. We consider a general class of aggregate constraints of the form f(v)0σ, where f is an arithmetic function of SQL-like aggregates and θ is one of <, ≤, ≥, >. We propose a novel pushing technique, called Divide-and-Approximate, to push such constraints. The idea is to recursively divide the search space and approximate the given constraint using antimonotone or monotone constraints in subspaces. This technique applies to a class called separable constraints, which properly contains all constraints built by an arithmetic function f of all SQL aggregates.

Original languageEnglish (US)
Pages (from-to)354-367
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number3
StatePublished - Mar 2005


  • Aggregate constraint
  • Constrained data mining
  • Data cube
  • Iceberg cube mining
  • Iceberg query

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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