The Experts below are selected from a list of 269883 Experts worldwide ranked by ideXlab platform
Kingip Lin - One of the best experts on this subject based on the ideXlab platform.
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an Index Structure for efficient reverse nearest neighbor queries
International Conference on Data Engineering, 2001Co-Authors: Congyun Yang, Kingip LinAbstract:The Reverse Nearest Neighbor (RNN) problem is to find all points in a given data set whose nearest neighbor is a given query point. Just like the Nearest Neighbor (NN) queries, the RNN queries appear in many practical situations such as marketing and resource management. Thus, efficient methods for the RNN queries in databases are required. The paper introduces a new Index Structure, the Rdnn-tree, that answers both RNN and NN queries efficiently. A single Index Structure is employed for a dynamic database, in contrast to the use of multiple Indexes in previous work. This leads to significant savings in dynamically maintaining the Index Structure. The Rdnn-tree outperforms existing methods in various aspects. Experiments on both synthetic and real world data show that our Index Structure outperforms previous methods by a significant margin (more than 90% in terms of number of leaf nodes accessed) in RNN queries. It also shows improvement in NN queries over standard techniques. Furthermore, performance in insertion and deletion is significantly enhanced by the ability to combine multiple queries (NN and RNN) in one traversal of the tree. These facts make our Index Structure extremely preferable in both static and dynamic cases.
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An Index Structure for data mining and clustering
Knowledge and Information Systems, 2000Co-Authors: Xiong Wang, Kingip Lin, Jason T. L. Wang, Dennis Shasha, Bruce A. Shapiro, Kaizhong ZhangAbstract:In this paper we present an Index Structure, called MetricMap, that takes a set of objects and a distance metric and then maps those objects to a k-dimensional space in such a way that the distances among objects are approximately preserved. The Index Structure is a useful tool for clustering and visualization in data-intensive applications, because it replaces expensive distance calculations by sum-of-square calculations. This can make clustering in large databases with expensive distance metrics practical. We compare the Index Structure with another data mining Index Structure, FastMap, recently proposed by Faloutsos and Lin, according to two criteria: relative error and clustering accuracy. For relative error, we show that (i) FastMap gives a lower relative error than MetricMap for Euclidean distances, (ii) MetricMap gives a lower relative error than FastMap for non-Euclidean distances (i.e., general distance metrics), and (iii) combining the two reduces the error yet further. A similar result is obtained when comparing the accuracy of clustering. These results hold for different data sizes. The main qualitative conclusion is that these two Index Structures capture complementary information about distance metrics and therefore can be used together to great benefit. The net effect is that multi-day computations can be done in minutes.
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ICDE - An Index Structure for efficient reverse nearest neighbor queries
Proceedings 17th International Conference on Data Engineering, 1Co-Authors: Congyun Yang, Kingip LinAbstract:The Reverse Nearest Neighbor (RNN) problem is to find all points in a given data set whose nearest neighbor is a given query point. Just like the Nearest Neighbor (NN) queries, the RNN queries appear in many practical situations such as marketing and resource management. Thus, efficient methods for the RNN queries in databases are required. The paper introduces a new Index Structure, the Rdnn-tree, that answers both RNN and NN queries efficiently. A single Index Structure is employed for a dynamic database, in contrast to the use of multiple Indexes in previous work. This leads to significant savings in dynamically maintaining the Index Structure. The Rdnn-tree outperforms existing methods in various aspects. Experiments on both synthetic and real world data show that our Index Structure outperforms previous methods by a significant margin (more than 90% in terms of number of leaf nodes accessed) in RNN queries. It also shows improvement in NN queries over standard techniques. Furthermore, performance in insertion and deletion is significantly enhanced by the ability to combine multiple queries (NN and RNN) in one traversal of the tree. These facts make our Index Structure extremely preferable in both static and dynamic cases.
Ruizhong Rao - One of the best experts on this subject based on the ideXlab platform.
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Equivalent refractive-Index Structure constant of non-Kolmogorov turbulence.
Optics express, 2015Co-Authors: Wenyue Zhu, Ruizhong RaoAbstract:The relationship between the non-Kolmogorov refractive-Index Structure constant and the Kolmogorov refractive-Index Structure constant is derived by using the refractive-Index Structure function and the variance of refractive-Index fluctuations. It shows that the non-Kolmogorov Structure constant is proportional to the Kolmogorov Structure constant and the scaling factor depends on the outer scale and the spectral power law. For a fixed Kolmogorov Structure constant, the non-Kolmogorov Structure constant increases with a increasing outer scale for the power law less than 11/3, the trend is opposite for the power law greater than 11/3. This equivalent relation provides a way of obtaining the non-Kolmogorov Structure constant by using the Kolmogorov Structure constant.
Jian Lu - One of the best experts on this subject based on the ideXlab platform.
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An Index Structure supporting rule activation in pervasive applications
World Wide Web, 2019Co-Authors: Yi Qin, Xianping Tao, Yu Huang, Jian LuAbstract:Rule mechanism has been widely used in many areas, such as databases, artificial intelligent and pervasive computing. In a rule mechanism, rule activation decides which rules are activated, when the rules are activated, and which tuples can be generated through the activation. Rule activation determines the efficiency of rule mechanism. In this article, we define the semantic constraints, constant constraint and variable constraint, of the rule according to the semantics of Datalog rules. Based on the constraints, we propose an Index Structure, named Yield Index, to support the rule activation effectively. Yield Index consists of the data Index and semantic Index, and records the complete information of a rule, including the matching relationship among the tuples of different relations in rule body. The Index integrates tuple insertion and rule activation to directly determine whether the matching tuples of new inserted tuple exist. Due to this character, we perform effective rule activation only, avoiding ineffective rule activation that cannot generate new tuples, so that the efficiency of rule activation is improved. The article describes the Structure of Yield Index, the construction and maintenance algorithms, and the rule activation algorithm based on Yield Index. The experimental results show that Yield Index has better performance and improves activation efficiency of one order of magnitude, comparing with other Index Structures. In addition, we also discuss the possible extensions of Yield Index in other applications.
Congyun Yang - One of the best experts on this subject based on the ideXlab platform.
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an Index Structure for efficient reverse nearest neighbor queries
International Conference on Data Engineering, 2001Co-Authors: Congyun Yang, Kingip LinAbstract:The Reverse Nearest Neighbor (RNN) problem is to find all points in a given data set whose nearest neighbor is a given query point. Just like the Nearest Neighbor (NN) queries, the RNN queries appear in many practical situations such as marketing and resource management. Thus, efficient methods for the RNN queries in databases are required. The paper introduces a new Index Structure, the Rdnn-tree, that answers both RNN and NN queries efficiently. A single Index Structure is employed for a dynamic database, in contrast to the use of multiple Indexes in previous work. This leads to significant savings in dynamically maintaining the Index Structure. The Rdnn-tree outperforms existing methods in various aspects. Experiments on both synthetic and real world data show that our Index Structure outperforms previous methods by a significant margin (more than 90% in terms of number of leaf nodes accessed) in RNN queries. It also shows improvement in NN queries over standard techniques. Furthermore, performance in insertion and deletion is significantly enhanced by the ability to combine multiple queries (NN and RNN) in one traversal of the tree. These facts make our Index Structure extremely preferable in both static and dynamic cases.
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ICDE - An Index Structure for efficient reverse nearest neighbor queries
Proceedings 17th International Conference on Data Engineering, 1Co-Authors: Congyun Yang, Kingip LinAbstract:The Reverse Nearest Neighbor (RNN) problem is to find all points in a given data set whose nearest neighbor is a given query point. Just like the Nearest Neighbor (NN) queries, the RNN queries appear in many practical situations such as marketing and resource management. Thus, efficient methods for the RNN queries in databases are required. The paper introduces a new Index Structure, the Rdnn-tree, that answers both RNN and NN queries efficiently. A single Index Structure is employed for a dynamic database, in contrast to the use of multiple Indexes in previous work. This leads to significant savings in dynamically maintaining the Index Structure. The Rdnn-tree outperforms existing methods in various aspects. Experiments on both synthetic and real world data show that our Index Structure outperforms previous methods by a significant margin (more than 90% in terms of number of leaf nodes accessed) in RNN queries. It also shows improvement in NN queries over standard techniques. Furthermore, performance in insertion and deletion is significantly enhanced by the ability to combine multiple queries (NN and RNN) in one traversal of the tree. These facts make our Index Structure extremely preferable in both static and dynamic cases.
Wenyue Zhu - One of the best experts on this subject based on the ideXlab platform.
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Equivalent refractive-Index Structure constant of non-Kolmogorov turbulence.
Optics express, 2015Co-Authors: Wenyue Zhu, Ruizhong RaoAbstract:The relationship between the non-Kolmogorov refractive-Index Structure constant and the Kolmogorov refractive-Index Structure constant is derived by using the refractive-Index Structure function and the variance of refractive-Index fluctuations. It shows that the non-Kolmogorov Structure constant is proportional to the Kolmogorov Structure constant and the scaling factor depends on the outer scale and the spectral power law. For a fixed Kolmogorov Structure constant, the non-Kolmogorov Structure constant increases with a increasing outer scale for the power law less than 11/3, the trend is opposite for the power law greater than 11/3. This equivalent relation provides a way of obtaining the non-Kolmogorov Structure constant by using the Kolmogorov Structure constant.
