select * from [Targeted Mailing].Cases order by age

select * from [Market Basket].Cases

select * from [TM Clustering].cases

select flattened *, StructureColumn('Education') as Education from [Subcategory Associations].Cases

select distinct [Occupation] from [TM Clustering]

select * from [TM Clustering].cases where IsInNode('001') 

select flattened * from [TM Clustering].content where node_type = 5

select flattened node_caption, node_description, node_distribution, node_support from [TM Clustering].content where node_type = 5

select flattened node_caption, (select * from node_distribution where [support] > 500)
from [TM Clustering].content
where node_type = 5

select flattened node_caption,(select attribute_name, attribute_value, [support], [probability] from node_distribution)
from [TM Decision Tree].content
where node_type = 3 or node_type = 4

select Predict([Bike Buyer]) as BikeBuyer from [TM Clustering]

select t.EnglishOccupation, t.Age, t.Region, Predict([Bike Buyer]) as BikeBuyer, PredictProbability([Bike Buyer]) as [Probability]
from [TM Decision Tree]
prediction join
openquery([Adventure Works DW], 'select EnglishOccupation, Age, Region from vTargetMail') as t
on [TM Decision Tree].Occupation = t.EnglishOccupation and
[TM Decision Tree].Age = t.Age and
[TM Decision Tree].Region = t.Region