  The multi-criteria decision making, which is possible with the advent of
skyline queries, has been applied in many areas. Though most of the existing
research is concerned with only a single relation, several real world
applications require finding the skyline set of records over multiple
relations. Consequently, the join operation over skylines where the preferences
are local to each relation, has been proposed. In many of those cases, however,
the join often involves performing aggregate operations among some of the
attributes from the different relations. In this paper, we introduce such
queries as "aggregate skyline join queries". Since the naive algorithm is
impractical, we propose three algorithms to efficiently process such queries.
The algorithms utilize certain properties of skyline sets, and processes the
skylines as much as possible locally before computing the join. Experiments
with real and synthetic datasets exhibit the practicality and scalability of
the algorithms with respect to the cardinality and dimensionality of the
relations.
