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|Title:||Smoothing over summary information in data cubes|
|Authors:||Sung, S. |
|Citation:||Sung, S.,Huang, S.,Ramer, A. (2000). Smoothing over summary information in data cubes. Journal of Systems Integration 10 (1) : 5-22. ScholarBank@NUS Repository. https://doi.org/10.1023/A:1026503216717|
|Abstract:||Decision support usually requires drawing from a huge data warehouse some statistical information that is interesting and useful to its users. A typical data model that supports the data warehouse is the multidimensional database, also known as a data cube. A data cube contains cells, each of which is associated with some summary information, or aggregate, that the decisions are to be based on. However, in real-life databases, due to the nature of their contents, data distribution tends to be clustered and sparse. The sparsity situation gets worse, in general, as the number of cells increases. For those cells that have support levels below a certain threshold, combining with adjacent cells is necessary to acquire sufficient support. Otherwise, incomplete or biased results could be derived due to lack of sufficient support. Our main focus in this paper is to find approximations for the missing or biased aggregates of those cells that have missing or low support. We call this approximation process smoothing in this paper. We propose a smooth function that can smooth nicely on a quantitative attribute while still being preserved locally. Our method is also adaptive to sudden changes of data distribution, called discontinuities, that inevitably occur in real-life data.|
|Source Title:||Journal of Systems Integration|
|Appears in Collections:||Staff Publications|
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