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Wu, Xin, Mei, and Han [WXMH09] to PromoCube, which supports promotion query
analysis in multidimensional space.
The discovery-driven exploration of OLAP data cubes was proposed by Sarawagi,
Agrawal, and Megiddo [SAM98]. Further studies on integration of OLAP with data min-
ing capabilities for intelligent exploration of multidimensional OLAP data were done by
Sarawagi and Sathe [SS01]. The construction of multifeature data cubes is described by
Ross, Srivastava, and Chatziantoniou [RSC98]. Methods for answering queries quickly
by online aggregation are described by Hellerstein, Haas, and Wang [HHW97] and
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Hellerstein, Avnur, Chou, et al. [HAC 99]. A cube-gradient analysis problem, called
cubegrade, was first proposed by Imielinski, Khachiyan, and Abdulghani [IKA02]. An
efficient method for multidimensional constrained gradient analysis in data cubes was
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studied by Dong, Han, Lam, et al. [DHL 01].
Mining cube space, or integration of knowledge discovery and OLAP cubes, has
been studied by many researchers. The concept of online analytical mining (OLAM),
or OLAP mining, was introduced by Han [Han98]. Chen, Dong, Han, et al. devel-
oped a regression cube for regression-based multidimensional analysis of time-series data
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[CDH 02, CDH 06]. Fagin, Guha, Kumar, et al. [FGK 05] studied data mining in
multistructured databases. B.-C. Chen, L. Chen, Lin, and Ramakrishnan [CCLR05] pro-
posed prediction cubes, which integrate prediction models with data cubes to discover
interesting data subspaces for facilitated prediction. Chen, Ramakrishnan, Shavlik, and
Tamma [CRST06] studied the use of data mining models as building blocks in a multi-
step mining process, and the use of cube space to intuitively define the space of interest
for predicting global aggregates from local regions. Ramakrishnan and Chen [RC07]
presented an organized picture of exploratory mining in cube space.