The Experts below are selected from a list of 71241 Experts worldwide ranked by ideXlab platform
Guoren Wang - One of the best experts on this subject based on the ideXlab platform.
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ELM Based Efficient Probabilistic Threshold Query on Uncertain Data
Proceedings of ELM-2014 Volume 1, 2015Co-Authors: Botao Wang, Guoren WangAbstract:The probabilistic Threshold query (PTQ), which returns all the objects satisfying the query with probabilities higher than a Probability Threshold, is widely used in uncertain database. Most previous work focused on the efficiency of query process, but paid no attention to the setting of Thresholds. However, setting the Thresholds too high or too low may lead to empty result or too many results. For a user, it is too difficult to choose a suitable Threshold for a query. In this paper, we propose a new framework for PTQs based on Threshold classification using ELM, where the Probability Threshold is replaced by the number range of results which is more intuitive and easier to choose. We first introduce the features selected for the probabilistic Threshold nearest neighbor query (PTNNQ), which is one of the most important PTQ types. Then a Threshold classification algorithm (TCA) using ELM is proposed to set a suitable Threshold for the PTNNQ. Further, the whole PTNNQ processing integrated with TCA are presented, and a dynamic classification strategy is proposed subsequently. Extensive experiments show that compared with the Thresholds those the users input directly, the Thresholds chosen by ELM classifiers are more suitable, which further improves the performance of PTNNQ. In addition, ELM outperforms SVM with regards to both the response time and classification accuracy.
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efficiently answering Probability Threshold based shortest path queries over uncertain graphs
Database Systems for Advanced Applications, 2010Co-Authors: Ye Yuan, Lei Chen, Guoren WangAbstract:Efficiently processing shortest path (SP) queries over stochastic networks attracted a lot of research attention as such queries are very popular in the emerging real world applications such as Intelligent Transportation Systems and communication networks whose edge weights can be modeled as a random variable. Some pervious works aim at finding the most likely SP (the path with largest Probability to be SP), and others search the least-expected-weight path. In all these works, the definitions of the shortest path query are based on simple probabilistic models which can be converted into the multi-objective optimal issues on a weighted graph. However, these simple definitions miss important information about the internal structure of the probabilistic paths and the interplay among all the uncertain paths. Thus, in this paper, we propose a new SP definition based on the possible world semantics that has been widely adopted for probabilistic data management, and develop efficient methods to find Threshold-based SP path queries over an uncertain graph. Extensive experiments based on real data sets verified the effectiveness of the proposed methods.
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DASFAA (1) - Efficiently answering Probability Threshold-based shortest path queries over uncertain graphs
Database Systems for Advanced Applications, 2010Co-Authors: Ye Yuan, Lei Chen, Guoren WangAbstract:Efficiently processing shortest path (SP) queries over stochastic networks attracted a lot of research attention as such queries are very popular in the emerging real world applications such as Intelligent Transportation Systems and communication networks whose edge weights can be modeled as a random variable. Some pervious works aim at finding the most likely SP (the path with largest Probability to be SP), and others search the least-expected-weight path. In all these works, the definitions of the shortest path query are based on simple probabilistic models which can be converted into the multi-objective optimal issues on a weighted graph. However, these simple definitions miss important information about the internal structure of the probabilistic paths and the interplay among all the uncertain paths. Thus, in this paper, we propose a new SP definition based on the possible world semantics that has been widely adopted for probabilistic data management, and develop efficient methods to find Threshold-based SP path queries over an uncertain graph. Extensive experiments based on real data sets verified the effectiveness of the proposed methods.
Lei Chen - One of the best experts on this subject based on the ideXlab platform.
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efficiently answering Probability Threshold based shortest path queries over uncertain graphs
Database Systems for Advanced Applications, 2010Co-Authors: Ye Yuan, Lei Chen, Guoren WangAbstract:Efficiently processing shortest path (SP) queries over stochastic networks attracted a lot of research attention as such queries are very popular in the emerging real world applications such as Intelligent Transportation Systems and communication networks whose edge weights can be modeled as a random variable. Some pervious works aim at finding the most likely SP (the path with largest Probability to be SP), and others search the least-expected-weight path. In all these works, the definitions of the shortest path query are based on simple probabilistic models which can be converted into the multi-objective optimal issues on a weighted graph. However, these simple definitions miss important information about the internal structure of the probabilistic paths and the interplay among all the uncertain paths. Thus, in this paper, we propose a new SP definition based on the possible world semantics that has been widely adopted for probabilistic data management, and develop efficient methods to find Threshold-based SP path queries over an uncertain graph. Extensive experiments based on real data sets verified the effectiveness of the proposed methods.
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DASFAA (1) - Efficiently answering Probability Threshold-based shortest path queries over uncertain graphs
Database Systems for Advanced Applications, 2010Co-Authors: Ye Yuan, Lei Chen, Guoren WangAbstract:Efficiently processing shortest path (SP) queries over stochastic networks attracted a lot of research attention as such queries are very popular in the emerging real world applications such as Intelligent Transportation Systems and communication networks whose edge weights can be modeled as a random variable. Some pervious works aim at finding the most likely SP (the path with largest Probability to be SP), and others search the least-expected-weight path. In all these works, the definitions of the shortest path query are based on simple probabilistic models which can be converted into the multi-objective optimal issues on a weighted graph. However, these simple definitions miss important information about the internal structure of the probabilistic paths and the interplay among all the uncertain paths. Thus, in this paper, we propose a new SP definition based on the possible world semantics that has been widely adopted for probabilistic data management, and develop efficient methods to find Threshold-based SP path queries over an uncertain graph. Extensive experiments based on real data sets verified the effectiveness of the proposed methods.
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evaluating Probability Threshold k nearest neighbor queries over uncertain data
Extending Database Technology, 2009Co-Authors: Reynold Cheng, Lei Chen, Jinchuan Chen, Xike XieAbstract:In emerging applications such as location-based services, sensor monitoring and biological management systems, the values of the database items are naturally imprecise. For these uncertain databases, an important query is the Probabilistic k-Nearest-Neighbor Query (k-PNN), which computes the probabilities of sets of k objects for being the closest to a given query point. The evaluation of this query can be both computationally- and I/O-expensive, since there is an exponentially large number of k object-sets, and numerical integration is required. Often a user may not be concerned about the exact Probability values. For example, he may only need answers that have sufficiently high confidence. We thus propose the Probabilistic Threshold k-Nearest-Neighbor Query (T-k-PNN), which returns sets of k objects that satisfy the query with probabilities higher than some Threshold T. Three steps are proposed to handle this query efficiently. In the first stage, objects that cannot constitute an answer are filtered with the aid of a spatial index. The second step, called probabilistic candidate selection, significantly prunes a number of candidate sets to be examined. The remaining sets are sent for verification, which derives the lower and upper bounds of answer probabilities, so that a candidate set can be quickly decided on whether it should be included in the answer. We also examine spatially-efficient data structures that support these methods. Our solution can be applied to uncertain data with arbitrary Probability density functions. We have also performed extensive experiments to examine the effectiveness of our methods.
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EDBT - Evaluating Probability Threshold k-nearest-neighbor queries over uncertain data
Proceedings of the 12th International Conference on Extending Database Technology Advances in Database Technology - EDBT '09, 2009Co-Authors: Reynold Cheng, Lei Chen, Jinchuan Chen, Xike XieAbstract:In emerging applications such as location-based services, sensor monitoring and biological management systems, the values of the database items are naturally imprecise. For these uncertain databases, an important query is the Probabilistic k-Nearest-Neighbor Query (k-PNN), which computes the probabilities of sets of k objects for being the closest to a given query point. The evaluation of this query can be both computationally- and I/O-expensive, since there is an exponentially large number of k object-sets, and numerical integration is required. Often a user may not be concerned about the exact Probability values. For example, he may only need answers that have sufficiently high confidence. We thus propose the Probabilistic Threshold k-Nearest-Neighbor Query (T-k-PNN), which returns sets of k objects that satisfy the query with probabilities higher than some Threshold T. Three steps are proposed to handle this query efficiently. In the first stage, objects that cannot constitute an answer are filtered with the aid of a spatial index. The second step, called probabilistic candidate selection, significantly prunes a number of candidate sets to be examined. The remaining sets are sent for verification, which derives the lower and upper bounds of answer probabilities, so that a candidate set can be quickly decided on whether it should be included in the answer. We also examine spatially-efficient data structures that support these methods. Our solution can be applied to uncertain data with arbitrary Probability density functions. We have also performed extensive experiments to examine the effectiveness of our methods.
Ye Yuan - One of the best experts on this subject based on the ideXlab platform.
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efficiently answering Probability Threshold based shortest path queries over uncertain graphs
Database Systems for Advanced Applications, 2010Co-Authors: Ye Yuan, Lei Chen, Guoren WangAbstract:Efficiently processing shortest path (SP) queries over stochastic networks attracted a lot of research attention as such queries are very popular in the emerging real world applications such as Intelligent Transportation Systems and communication networks whose edge weights can be modeled as a random variable. Some pervious works aim at finding the most likely SP (the path with largest Probability to be SP), and others search the least-expected-weight path. In all these works, the definitions of the shortest path query are based on simple probabilistic models which can be converted into the multi-objective optimal issues on a weighted graph. However, these simple definitions miss important information about the internal structure of the probabilistic paths and the interplay among all the uncertain paths. Thus, in this paper, we propose a new SP definition based on the possible world semantics that has been widely adopted for probabilistic data management, and develop efficient methods to find Threshold-based SP path queries over an uncertain graph. Extensive experiments based on real data sets verified the effectiveness of the proposed methods.
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DASFAA (1) - Efficiently answering Probability Threshold-based shortest path queries over uncertain graphs
Database Systems for Advanced Applications, 2010Co-Authors: Ye Yuan, Lei Chen, Guoren WangAbstract:Efficiently processing shortest path (SP) queries over stochastic networks attracted a lot of research attention as such queries are very popular in the emerging real world applications such as Intelligent Transportation Systems and communication networks whose edge weights can be modeled as a random variable. Some pervious works aim at finding the most likely SP (the path with largest Probability to be SP), and others search the least-expected-weight path. In all these works, the definitions of the shortest path query are based on simple probabilistic models which can be converted into the multi-objective optimal issues on a weighted graph. However, these simple definitions miss important information about the internal structure of the probabilistic paths and the interplay among all the uncertain paths. Thus, in this paper, we propose a new SP definition based on the possible world semantics that has been widely adopted for probabilistic data management, and develop efficient methods to find Threshold-based SP path queries over an uncertain graph. Extensive experiments based on real data sets verified the effectiveness of the proposed methods.
Richard Peter - One of the best experts on this subject based on the ideXlab platform.
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Who should exert more effort? Risk aversion, downside risk aversion and optimal prevention
Economic Theory, 2020Co-Authors: Richard PeterAbstract:I provide new results on how risk preferences affect optimal prevention. I identify a comparative risk aversion and a comparative downside risk aversion effect and highlight those cases where both effects are aligned. Alignment depends on a Probability Threshold, which, in turn, only depends on the preferences of the benchmark agent. This allows to define an entire class of decision-makers who all share the same comparative static prediction relative to the reference agent. I relate my findings to different intensity measures of downside risk aversion and apply them to parametric preference changes and specific classes of utility functions.
Eugene V. Mccloskey - One of the best experts on this subject based on the ideXlab platform.
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The distribution of FRAX®-based probabilities in women from Japan
Journal of bone and mineral metabolism, 2012Co-Authors: John A. Kanis, Helena Johansson, Anders Odén, Eugene V. MccloskeyAbstract:New assessment guidelines for osteoporosis in Japan include the use of the WHO risk assessment tool (FRAX) that computes the 10-year Probability of fracture. The aim of this study was to determine the distribution of fracture probabilities and to assess the impact of Probability-based intervention Thresholds in women from Japan aged 50 years and older. Age-specific simulation cohorts were constructed from the prevalences of clinical risk factors and femoral neck bone mineral density to determine the distribution of fracture probabilities as assessed by FRAX. These data were used to estimate the number and proportion of women at or above a 10-year fracture Probability of 5, 10, 15, 20, 25, and 30 %. In addition, case scenarios that applied a FRAX Probability Threshold of 15 % were compared with current guidance. In the absence of additional criteria for treatment, a 15 % fracture Probability Threshold would identify approximately 32 % of women over the age of 50 years (9.3 million women) as eligible for treatment. Because of expected changes in population demography, the 15 % fracture Probability Threshold would capture approximately 38 % of women over the age of 50 years (12.7 million women), mainly those aged 80 years or older. The introduction of a FRAX Threshold of 15 % would permit treatment in women with clinical risk factors that would otherwise fall below previously established intervention Thresholds. The incorporation of FRAX into assessment guidelines is likely to redirect treatments for osteoporosis from younger women at low risk to elderly women at high fracture risk.
