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SSAS – Multidimensional Space

Working with relational databases, we are used to a 2-dimensional space – the table, with its records and fields, or rows and columns. we use cube to describe a multidimensional space, but not a cube in the geometrical sense of the term. A multidimensional data space can have any number of dimensions, and those dimensions don’t have to be the same size.

Terms we use to describe multidimensional space:

Dimension: a data element that company wants to analyze. Time dimension is common. So is plan type, status, activity code etc.

Member: one point on the dimension. Monday would be a dimension member in the case of the Time dimension. “Open” would be a dimension member in the case of the status dimension.

Value: a unique characteristic of a member. In the time dimension, it’s might be the date.

Attribute: is the full collection of members. All the days of the weeks would be the attribute of the time dimension. “Open”, “Close” would be the attribute of the status dimension.

Size: or cardinality, is the number of members a dimension contains. A time dimension made of the days of the week would have size of 7.

Fact: if we are analyzing sales of a product, then a data point representing the sales is a fact data. All the fact data points taken together constitute a fact space.

Tuple: is a set of coordinates in multidimensional space. Ex.: [2% milk], [Sales Man Joe], [March 2010]

Slice: a section of multidimensional space that can be defined by a tuple. Ex.: (*, [Sales Man Joe], [March 2010]). But the wild card is not written, ([March 2010]) represents sales of all the products, by all the sales men in March 2010.

Dimension Hierarchies: if we use month as the key attribute of the time dimension, and use the related attributes such as quarters, years. Then Year, quarter, month constitute the dimensional hierarchy.

Cell: my fact space contains only the points that represent actual sales for each month. There is no data for 2010 first quarter, 2nd quarter until I calculate them. So those data points only represent a logical space. Therefore my “full space” if the multidimensional model is made of both the “fact space” and the logical space. Each point in the cube’s space is call a cell. Each fact cell in the cube is associated with an actual or potential sale of a product to a customer. Empty cell has a value of NULL.

Measure: the value in a cell is called measure. These measures, taken together, can be seen as a measure dimension. Each measure (or a member of the measure dimension) has a set of properties, such as data type, unit of measure, and the calculation type for the data aggregation function.

Aggregation Functions: they enable you to calculate the values of cells in the logical space from the values of the cells in the fact space.

Subcubes: represent part of the full space of the cube inside the cube.

Categories: SSAS
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