The Experts below are selected from a list of 18330 Experts worldwide ranked by ideXlab platform
Hongju Cheng - One of the best experts on this subject based on the ideXlab platform.
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research on why not questions of top k query in Orthogonal Region
2019Co-Authors: Ping Sun, Ling Yuan, Mingli Wang, Hongju ChengAbstract:Orthogonal Region query has always been an important topic in the field of database query, geographic information system, computer graphics, data mining and multimedia information retrieval. In recent years, the “Why-Not” questions has gradually become a hot topic in the SQL query, Skyline query, and spatial keyword Top-K query. However, no one has answered the “Why-Not” questions of the Orthogonal Region Top-K query. Based on the in-depth study of the Orthogonal Region Top-K query algorithm, this paper first proposes to answer the “Why-Not” questions in the Orthogonal Region Top-K query. We adjust the initial query so that the result set of the new query contains the “Why-Not” elements with the least cost. Abundant experiments have been conducted to analyze the proposed algorithm on the factors of initial k value, initial rank, and data size. The experimental results demonstrate the accuracy and efficiency of the proposed algorithm.
Ping Sun - One of the best experts on this subject based on the ideXlab platform.
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research on why not questions of top k query in Orthogonal Region
2019Co-Authors: Ping Sun, Ling Yuan, Mingli Wang, Hongju ChengAbstract:Orthogonal Region query has always been an important topic in the field of database query, geographic information system, computer graphics, data mining and multimedia information retrieval. In recent years, the “Why-Not” questions has gradually become a hot topic in the SQL query, Skyline query, and spatial keyword Top-K query. However, no one has answered the “Why-Not” questions of the Orthogonal Region Top-K query. Based on the in-depth study of the Orthogonal Region Top-K query algorithm, this paper first proposes to answer the “Why-Not” questions in the Orthogonal Region Top-K query. We adjust the initial query so that the result set of the new query contains the “Why-Not” elements with the least cost. Abundant experiments have been conducted to analyze the proposed algorithm on the factors of initial k value, initial rank, and data size. The experimental results demonstrate the accuracy and efficiency of the proposed algorithm.
Ling Yuan - One of the best experts on this subject based on the ideXlab platform.
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research on why not questions of top k query in Orthogonal Region
2019Co-Authors: Ping Sun, Ling Yuan, Mingli Wang, Hongju ChengAbstract:Orthogonal Region query has always been an important topic in the field of database query, geographic information system, computer graphics, data mining and multimedia information retrieval. In recent years, the “Why-Not” questions has gradually become a hot topic in the SQL query, Skyline query, and spatial keyword Top-K query. However, no one has answered the “Why-Not” questions of the Orthogonal Region Top-K query. Based on the in-depth study of the Orthogonal Region Top-K query algorithm, this paper first proposes to answer the “Why-Not” questions in the Orthogonal Region Top-K query. We adjust the initial query so that the result set of the new query contains the “Why-Not” elements with the least cost. Abundant experiments have been conducted to analyze the proposed algorithm on the factors of initial k value, initial rank, and data size. The experimental results demonstrate the accuracy and efficiency of the proposed algorithm.
Mingli Wang - One of the best experts on this subject based on the ideXlab platform.
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research on why not questions of top k query in Orthogonal Region
2019Co-Authors: Ping Sun, Ling Yuan, Mingli Wang, Hongju ChengAbstract:Orthogonal Region query has always been an important topic in the field of database query, geographic information system, computer graphics, data mining and multimedia information retrieval. In recent years, the “Why-Not” questions has gradually become a hot topic in the SQL query, Skyline query, and spatial keyword Top-K query. However, no one has answered the “Why-Not” questions of the Orthogonal Region Top-K query. Based on the in-depth study of the Orthogonal Region Top-K query algorithm, this paper first proposes to answer the “Why-Not” questions in the Orthogonal Region Top-K query. We adjust the initial query so that the result set of the new query contains the “Why-Not” elements with the least cost. Abundant experiments have been conducted to analyze the proposed algorithm on the factors of initial k value, initial rank, and data size. The experimental results demonstrate the accuracy and efficiency of the proposed algorithm.
Najmeh Taleb - One of the best experts on this subject based on the ideXlab platform.
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sensor deployment by a robot in an unknown Orthogonal Region achieving full coverage
2014Co-Authors: Eduard Mesabarrameda, Nicola Santoro, Wei Shi, Najmeh TalebAbstract:When deploying a wireless sensor network in an unknown environment, commonly referred to as Region of Interest (ROI), the main goal is for the entire Region to be covered by the sensing ranges of the deployed sensors. While this goal of full coverage is easily achieved in presence of human intervention, it becomes problematic if the Region is dangerous or inaccessible to human. An approach recently proposed to solve the problem is to use a robot to deploy the sensors; the main advantages respect to the alternative of employing mobile sensors are the reduced costs (due to manufacture and maintenance cost of common static sensors vs. mobile ones) and the reduced complexity of the coordination and control algorithms. Indeed several solution algorithms to achieve deployment of sensors by a robot in an unknown Region have been proposed in the literature. Unfortunately, even when restricted to Orthogonal Regions (e.g., city maps, building plans, etc), all the existing algorithms fail to achieve full coverage of the ROI. Specifically, following the existing protocols, the robot would leave uncovered areas near either the boundaries or critical areas (e.g. areas that are linked to the rest of the Region by a narrow corridor).
