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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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. 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. The skyline, or Pareto, operator selects those tuples that are not dominated by any others. Extending relational systems with the skyline operator would offer a basis for handling preference queries. Good algorithms are needed for skyline, however, to make this efficient in a relational setting. We propose a skyline algorithm, SFS, based on presorting that is general, for use with any skyline query, efficient, and well behaved in a relational setting. During the last decades, data management and storage have become increasingly distributed. Advanced query operators, such as skyline queries, are necessary in order to help users to handle the huge amount of available data by identifying a set of interesting data objects. Skyline query processing in highly distributed environments poses inherent challenges and demands and requires non-traditional techniques due to the distribution of content and the lack of global knowledge. This paper surveys this interesting and still evolving research area, so that readers can easily obtain an overview of the state-of-the-art. We outline the objectives and the main principles that any distributed skyline approach has to fulfill, leading to useful guidelines for developing algorithms for distributed skyline processing. We review in detail existing approaches that are applicable for highly distributed environments, clarify the assumptions of each approach, and provide a comparative performance analysis. Moreover, we study the skyline variants each approach supports. Our analysis leads to a taxonomy of existing approaches. Finally, we present interesting research topics on distributed skyline computation that have not yet been explored.