Uncertainty in Data Integration and Dataspace Support Platforms - Schema Matching and Mapping - page 84

Example 5. Consider rewriting Q in the motivating example, repeated as follows:
Q: SELECT mailing-addr FROM T
To answer the query, we first rewrite Q into query Q 0 by adding the id column:
Q': SELECT id, mailing-addr FROM T
We then invoke B Y T ABLE and generate the following SQL query to compute
by-table answers for Q 0 :
Qa: SELECT id, mailing-addr, SUM(pr)
FROM (
SELECT DISTINCT id, current-addr
AS mailing-addr, 0.5 AS pr
FROM S
UNION ALL
SELECT DISTINCT id, permanent-addr
AS mailing-addr, 0.4 AS pr
FROM S
UNION ALL
SELECT DISTINCT id, email-addr
AS mailing-addr, 0.1 AS pr
FROM S)
GROUP BY id, mailing-addr
Finally, we generate the results using the following query.
Qu: SELECT mailing-addr, NOR(pr) AS pr
FROM Qa
GROUP BY mailing-addr
where for a set of probabilities pr 1 ;:::;pr n , NOR computes 1 ˘ i D 1 .1 pr i /.
t
Example 6. Consider the schema p-mapping in Example 4 .IfwereviseQ slightly
by returning the join attribute, shown as follows, we can answer the query in
polynomial time.
Q': SELECT V.hightech
FROM T, V
WHERE T.mailing-addr = V.hightech
We answer the query by dividing it into two subqueries, Q 1 and Q 2 ,asshownin
Example 4 . We can compute Q 1 with query Q u (shown in Example 5 ) and compute
Q 2 similarly with a query Q 0 u . We compute by-tuple answers of Q 0 as follows:
SELECT Qu'.hightech, Qu.pr * Qu'.pr
FROM Qu, Qu'
WHERE Qu.mailing-addr = Qu'.hightect
t