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Based on the above analysis, with the error state vector de ned as x = [ , xg , xa ] , we combine eqns (109), (1011), and (1057) to obtain the dynamic model for the state vector as 0 Rn 0 b xg = 0 0 xg Fg xa xa 0 0 Fa n Rn 0 Rn 0 in b b g + 0 I 0 0 g 0 0 0 I a We combine eqns (1041) and (1043) to obtain the error state measurement models as gn m

To provide the most predictable and correct results, Cognos 8 uses clues from the model to try to determine which tables in a query are likely to have fact information and which tables are likely to have dimensional, descriptive information Fact information encompasses such things as sales quantities, forecast dollars, patient counts, and the number of days it took to close a help desk case These are all numeric values that are usually aggregated in some fashion when reported Dimensional information is the descriptive information that qualifies the fact amounts Dimensional data encompasses such things as products, products lines, customers, dates, and locations When you relate query subjects containing fact information to those containing dimensional information, the relationship will be many-to-one with the many side on the fact query subject For example, many sales transactions in the Sales query subject will refer to one product in the Product query subject because you sell the same product over and over again Knowing this, Cognos 8 will assume that the fact query subject is on the many side of the relationship Even if the model is more complex and the dimensional information is stored across multiple tables say, for instance, a Product query subject, a Product Type query subject, and a Product Line query subject Cognos 8 will try to trace the join paths to determine the fact table correctly However, with some complex models, the query engine s attempt can result in incorrect determination of the fact query subjects or query subjects that can be treated as facts or dimensions in different situations, yielding slow queries and sometimes incorrect results It is up to you, as the modeler, to understand the affect of cardinality on the resulting SQL You must strive to create the model correctly, and you must test the model thoroughly to ensure that the results are predictable and valid We cannot give you specifics for every eventuality that you might encounter in your modeling We can only give you these guidelines, and a few more in the section is in this chapter now section on Modeling Challenges.

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(1058) (1059)

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What happens if you write a report in Cognos 8 that pulls information from more than one fact query subject along with some dimensional information If modeled correctly, Cognos 8 should be able to join the facts from the different query subjects through the dimension query subjects It does this by querying the set of facts from one query subject joined to its relevant dimensions, separately joining the set of facts from the other fact query subject to

The Kalman lter measurement update computations require the variance of the discrete-time aiding measurements This is straightforward for the magnetometer measurements:

Part IV:

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