(a) Fact table with COUNT aggregate; (b) Fact table with AVG aggregate
(a) (b)
Toward Integrating Data Warehousing with Data Mining Techniques 2
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clusters (groupings) and mine association rules. Finally, some related studies about
integrating DW with DM techniques will be presented.
Data.Warehousing
A data warehouse is an integration of consolidated and non volatile data from multiple
and possibly heterogeneous data sources for the purpose of decision support
making. It contains a collection of data cubes which can be exploited via OLAP
techniques such as drill-down and rollup in order to summarize, consolidate, and
view data according to different dimensions (Chaudhuri & Dayal, 1997). In a multidimensional
context with a set D of dimensions, a dimension (e.g., location of a
company, time) is a descriptive axis for data presentation under several perspectives.
A dimension hierarchy contains levels, which organize data into a logical structure
(e.g., country, state and city for the location dimension). A fact table (see Table 1)
contains numerical measures and keys relating facts to dimension tables. A cube
C=
is a visual representation of a fact table, where D is a set of dimensions
of the cube (with associated hierarchies) and M its corresponding measures.
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