In recent years there has been a significant interest in peer-to-peer (P2P) environments in the community of data management. However, almost all work, so far, is focused on exact query processing in current P2P dat...In recent years there has been a significant interest in peer-to-peer (P2P) environments in the community of data management. However, almost all work, so far, is focused on exact query processing in current P2P data systems. The autonomy of peers also is not considered enough. In addition, the system cost is very high because the information publishing method of shared data is based on each document instead of document set. In this paper, abstract indices (AbIx) are presented to implement content-based approximate queries in centralized, distributed and structured P2P data systems. It can be used to search as few peers as possible but get as many returns satisfying users' queries as possible on the guarantee of high autonomy of peers. Also, abstract indices have low system cost, can improve the query processing speed, and support very frequent updates and the set information publishing method. In order to verify the effectiveness of abstract indices, a simulator of 10,000 peers, over 3 million documents is made, and several metrics are proposed. The experimental results show that abstract indices work well in various P2P data systems.展开更多
Kernel is a kind of data summary which is elaborately extracted from a large dataset.Given a problem,the solution obtained from the kernel is an approximate version of the solution obtained from the whole dataset with...Kernel is a kind of data summary which is elaborately extracted from a large dataset.Given a problem,the solution obtained from the kernel is an approximate version of the solution obtained from the whole dataset with a provable approximate ratio.It is widely used in geometric optimization,clustering,and approximate query processing,etc.,for scaling them up to massive data.In this paper,we focus on the minimumε-kernel(MK)computation that asks for a kernel of the smallest size for large-scale data processing.For the open problem presented by Wang et al.that whether the minimumε-coreset(MC)problem and the MK problem can be reduced to each other,we first formalize the MK problem and analyze its complexity.Due to the NP-hardness of the MK problem in three or higher dimensions,an approximate algorithm,namely Set Cover-Based Minimumε-Kernel algorithm(SCMK),is developed to solve it.We prove that the MC problem and the MK problem can be Turing-reduced to each other.Then,we discuss the update of MK under insertion and deletion operations,respectively.Finally,a randomized algorithm,called the Randomized Algorithm of Set Cover-Based Minimumε-Kernel algorithm(RA-SCMK),is utilized to further reduce the complexity of SCMK.The efficiency and effectiveness of SCMK and RA-SCMK are verified by experimental results on real-world and synthetic datasets.Experiments show that the kernel sizes of SCMK are 2x and 17.6x smaller than those of an ANN-based method on real-world and synthetic datasets,respectively.The speedup ratio of SCMK over the ANN-based method is 5.67 on synthetic datasets.RA-SCMK runs up to three times faster than SCMK on synthetic datasets.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No. 60473077 and the Program for New Century Excellent Talents in University.
文摘In recent years there has been a significant interest in peer-to-peer (P2P) environments in the community of data management. However, almost all work, so far, is focused on exact query processing in current P2P data systems. The autonomy of peers also is not considered enough. In addition, the system cost is very high because the information publishing method of shared data is based on each document instead of document set. In this paper, abstract indices (AbIx) are presented to implement content-based approximate queries in centralized, distributed and structured P2P data systems. It can be used to search as few peers as possible but get as many returns satisfying users' queries as possible on the guarantee of high autonomy of peers. Also, abstract indices have low system cost, can improve the query processing speed, and support very frequent updates and the set information publishing method. In order to verify the effectiveness of abstract indices, a simulator of 10,000 peers, over 3 million documents is made, and several metrics are proposed. The experimental results show that abstract indices work well in various P2P data systems.
基金the National Natural Science Foundation of China under Grant Nos.61732003,61832003,61972110 and U19A2059the National Key Research and Development Program of China under Grant No.2019YFB2101902the CCF-Baidu Open Fund CCF-BAIDU under Grant No.OF2021011.
文摘Kernel is a kind of data summary which is elaborately extracted from a large dataset.Given a problem,the solution obtained from the kernel is an approximate version of the solution obtained from the whole dataset with a provable approximate ratio.It is widely used in geometric optimization,clustering,and approximate query processing,etc.,for scaling them up to massive data.In this paper,we focus on the minimumε-kernel(MK)computation that asks for a kernel of the smallest size for large-scale data processing.For the open problem presented by Wang et al.that whether the minimumε-coreset(MC)problem and the MK problem can be reduced to each other,we first formalize the MK problem and analyze its complexity.Due to the NP-hardness of the MK problem in three or higher dimensions,an approximate algorithm,namely Set Cover-Based Minimumε-Kernel algorithm(SCMK),is developed to solve it.We prove that the MC problem and the MK problem can be Turing-reduced to each other.Then,we discuss the update of MK under insertion and deletion operations,respectively.Finally,a randomized algorithm,called the Randomized Algorithm of Set Cover-Based Minimumε-Kernel algorithm(RA-SCMK),is utilized to further reduce the complexity of SCMK.The efficiency and effectiveness of SCMK and RA-SCMK are verified by experimental results on real-world and synthetic datasets.Experiments show that the kernel sizes of SCMK are 2x and 17.6x smaller than those of an ANN-based method on real-world and synthetic datasets,respectively.The speedup ratio of SCMK over the ANN-based method is 5.67 on synthetic datasets.RA-SCMK runs up to three times faster than SCMK on synthetic datasets.