Multidimensional modeling (MDM) gets its name from the idea of dimensions. Dimensions are categories of categories. If that doesn't make sense, don't worry. It's easier to show dimensions than to explain them with words, so look at the example below.

In our maximum daily temperature table, there are two dimensions represented. One is related to Geography, and one is related to Time. Each base fact (the maximum temperature recorded) is related to a CITY and a DAY. CITY and DAY are each attributes.

An attribute is a category. CITY is a category idea. There are many things in the real world called "cities." Los Angeles, Seattle, Phoenix, and San Francisco are just a few actual cities. In MDM, each of these is an attribute element (or simply "element"), an actual instance of the category idea CITY.

What are the elements of the attribute DAY? A common beginning mistake is to think of the days of the week: Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, and Saturday. In actuality, a day is a uniquely identifiable segment of time that is one day in length. An example would be Saturday, June 26, 1999. It is impossible for two different days to both be Saturday, June 26, 1999.

Using this table, it is easy to find out information about the maximum daily temperatures for any group of cities or group of days. Are cities the only way we think about geography? Are days the only way we think about time? Of course not. Both the Geography dimension and Time dimension will have other attributes in them. The exact attributes (and the elements they contain) will depend on how we choose to model the world.

For example, we might want to look at our temperature data in terms of states. Every CITY is part of a STATE. We can therefore create a new attribute, STATE, and add it to the Geography dimension. When we do, the model for the Geography dimension will look like this:

Each box represents an attribute. The line between the boxes represents the relationship between the two attributes. In this case, the relationship is one-to-many. One STATE relates to many CITIES. One-to-many relationships are very common in multidimensional data modeling.

Many-to-many relationships are also common. If we wished for it to be possible for one CITY to belong in more than one STATE¹, we would use this type of relationship. Let's assume that the US Post Office's method of splitting cities on state borders into separate cities for mail delivery purposes is good enough, and stick with a one-to-many relationship.

What if I wanted to look at my data by COUNTY? Where would a COUNTY attribute go on the model we have so far? Also, what if someone in your company wishes to look at the data by "microclimate", where a microclimate is some area of geography that can cover several neighboring cities, and generally is not bounded by state lines?

Here's a hint: You'll need two more attributes in your Geography dimension. Take a second to sketch it out, then look at the solution we would recommend.

Don't peek! When you're ready, click here to see the answer.

Note that for each element in any attribute, there are several facts that relate to it. On a given day, there will be many cities worth of data recorded. For a given city, there will be many days worth of data. For a given state, there will be many counties within it, each of which will contain many cities, each of which will have many facts associated with it. The independent nature of fact header elements is at the heart of multidimensional modeling's power, because it allows for extremely flexible multidimensional analysis.

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