Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Efficient processing of skyline queries has been an area of growing interest. Many of the earlier skyline techniques assumed that the skyline query is applied to a single data table. Naturally, these algorithms re not suitable for many applications in which the skyline query may involve attributes belonging to multiple data sources. In other words, if the data used in the skyline query are stored in multiple tables, then  join  operations would be required before the skyline can be searched. The task of computing skylines on multiple data sources has been coined as the  skyline-join  problem and various  skyline-join  algorithms have been proposed. Hover, the current proposals suffer several drawbacks: they often need to scan the input tables exhaustively in order to obtain the set of skyline-join results; moreover, the pruning techniques employed to eliminate the tuples are largely based on expensive pairwise tuple-to-tuple comparisons. In this article,  aim to address these shortcomings by proposing two novel skyline-join algorithms, namely  skyline-sensitive join  (S 2 J) and  symmetric skyline-sensitive join  (S 3 J), to process skyline queries over two data sources. Our approaches compute the results using a novel  layer/region pruning  technique ( LR-pruning ) that prunes the join space in blocks as opposed to individual data points, thereby avoiding excessive pairwise point-to-point dominance checks. Furthermore, the S 3 J algorithm utilizes an early stopping condition in order to successfully compute the skyline results by accessing only a subset of the input tables. In addition to S 2 J and S 3 J,  also propose the S 2  J-M and S 3  J-M algorithms. These algorithms extend S 2 J's and S 3 J's two-way skyline-join ability to efficiently process skyline-join queries over more than two data sources. S 2  J-M and S 3  J-M leverage the extended concept of  LR-pruning , called  M -way  LR-pruning , to compute multi-way skyline-joins in which more than two data sources are integrated during skyline processing.  report extensive experimental results that confirm the advantages of the proposed algorithms over state-of-the-art skyline-join techniques.
['Mithila Nagendra', 'K. Selçuk Candan']
I am writing a research paper on a idea Efficient Processing of Skyline-Join Queries over Multiple Data Sources
[{"from": "human", "value": "I am writing a research paper on a idea Efficient Processing of Skyline-Join Queries over Multiple Data Sources"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Efficient processing of skyline queries has been an area of growing interest. Many of the earlier skyline techniques assumed that the skyline query is applied to a single data table. Naturally, these algorithms re not suitable for many applications in which the skyline query may involve attributes belonging to multiple data sources. In other words, if the data used in the skyline query are stored in multiple tables, then  join  operations would be required before the skyline can be searched. The task of computing skylines on multiple data sources has been coined as the  skyline-join  problem and various  skyline-join  algorithms have been proposed. Hover, the current proposals suffer several drawbacks: they often need to scan the input tables exhaustively in order to obtain the set of skyline-join results; moreover, the pruning techniques employed to eliminate the tuples are largely based on expensive pairwise tuple-to-tuple comparisons. In this article,  aim to address these shortcomings by proposing two novel skyline-join algorithms, namely  skyline-sensitive join  (S 2 J) and  symmetric skyline-sensitive join  (S 3 J), to process skyline queries over two data sources. Our approaches compute the results using a novel  layer/region pruning  technique ( LR-pruning ) that prunes the join space in blocks as opposed to individual data points, thereby avoiding excessive pairwise point-to-point dominance checks. Furthermore, the S 3 J algorithm utilizes an early stopping condition in order to successfully compute the skyline results by accessing only a subset of the input tables. In addition to S 2 J and S 3 J,  also propose the S 2  J-M and S 3  J-M algorithms. These algorithms extend S 2 J's and S 3 J's two-way skyline-join ability to efficiently process skyline-join queries over more than two data sources. S 2  J-M and S 3  J-M leverage the extended concept of  LR-pruning , called  M -way  LR-pruning , to compute multi-way skyline-joins in which more than two data sources are integrated during skyline processing.  report extensive experimental results that confirm the advantages of the proposed algorithms over state-of-the-art skyline-join techniques."}]