Querying a summary of database - APMD

J Intell Inf Syst (2006) 26: 59–73 69– function Explore-Select returns a list of matching summaries;– function Corr corresponds to the matching test reported in Section 4.2;– operator ‘+’ performs a list concatenation of its arguments;– function Add is the classical constructor for lists, it adds an element to a list of the suitabletype;– L res is a local variable.Example 4. The result of applying the algorithm on the portion of hierarchy in figure 2 forthe queries stated so far is listed below :Query Result list TuplesQ 1 〈z 5 , z 7 〉 t b2 , t e1Q 2 〈z 4 , z 7 〉 t d1 , t e1Q 3 〈z 3 , z 4 , z 5 , z 7 〉 t a1 , t b1 , t c1 , t d1 , t b2 , t e1Q 4 〈〉 –Q 5 〈z 3 , z 4 , z 5 , z 6 , z 7 〉 t a1 , t b1 , t c1 , t d1 , t b2 , t c2 , t e1Q 6 〈z 5 , z 7 〉 t b2 , t e14.4. ClassificationThe classification step is an aggregation of selected summaries according to their interpretationwith respect to proposition P: summaries that have the same required characteristicson all attributes of the input attributes set X form a class that is denoted by B.Example 5. In this example, query Q 5 from example 2 is employed once again. Since eachinterpretation of a logical proposition has some boolean variables valuated to true, the set ofthose variables is considered. The proposition P 5 induced by Q 5 (see example 2) admits 9such sets. But the projection of a relation R(S) on a subset X of its attributes, as done by anySpringer