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 *
@cite_7 , proposed the multi relational skyline operator. They also designed algorithms to find such skylines over multiple relations. @cite_2 , coined the term skyline join'' in the context of distributed environments. They extended SaLSa @cite_4 and also proposed an iterative algorithm that prunes the search space in each step. ASJQ queries differ in that it extends the skyline join proposed in @cite_7 with aggregate operations performed during the join. This renders the use of the existing techniques inapplicable as they work only on the local attributes.
@cite_4 @cite_7 @cite_2
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Skyline queries compute the set of Pareto-optimal tuples in a relation, ie those tuples that are not dominated by any other tuple in the same relation. Although several algorithms have been proposed for efficiently evaluating skyline queries, they either require to extend the relational server with specialized access methods (which is not always feasible) or have to perform the dominance tests on all the tuples in order to determine the result. In this paper we introduce SaLSa (Sort and Limit Skyline algorithm), which exploits the sorting machinery of a relational engine to order tuples so that only a subset of them needs to be examined for computing the skyline result. This makes SaLSa particularly attractive when skyline queries are executed on top of systems that do not understand skyline semantics or when the skyline logic runs on clients with limited power and or bandwidth. We propose to extend database systems by a Skyline operation. This operation filters out a set of interesting points from a potentially large set of data points. A point is interesting if it is not dominated by any other point. For example, a hotel might be interesting for somebody traveling to Nassau if no other hotel is both cheaper and closer to the beach. We show how SSL can be extended to pose Skyline queries, present and evaluate alternative algorithms to implement the Skyline operation, and show how this operation can be combined with other database operations, e.g., join. The database research community has recently recognized the usefulness of skyline query. As an extension of existing database operator, the skyline query is valuable for multi-criteria decision making. However, current research tends to assume that the skyline operator is applied to one table which is not true for many applications on Web databases. In Web databases, tables are distributed in different sites, and a skyline query may involve attributes of multiple tables. In this paper, we address the problem of processing skyline queries on multiple tables in a distributed environment. We call the new operator skyline-join, as it is a hybrid of skyline and join operations. We propose two efficient approaches to process skyline-join queries which can significantly reduce the communication cost and processing time. Experiments are conducted and results show that our approaches are efficient for distributed skyline-join queries.