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Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: Buffer analysis is a common tool of spatial analysis, which deals with the problem of proximity. Due to numerous and complex operations in the algorithm, the computational efficiency needs to be optimized. Methods: To process large scale point features, a graph-based representation model is pro⁃ posed, which establishes the spatial computational domain for data and analysis, and develops a well-balanced task-partitioning method by partitioning the graph. First, the proposed model defines processing functions of point features and their spatial relationships from the perspectives of graph nodes and graph edges, and provides a logic description for buffer zone generation around point features. Second, the computational weights of graph nodes and graph edges are obtained by fitting the time complexity of the above processing functions. Finally, graph partitioning is adopted to divide the buffer task, which contributes to multiple parallel tasks matching with the computational resources. Results: The experimental results show that graphbased buffer analysis can achieve better load balance and overall efficiency, which is superior to the main⁃ stream partitioning methods, regular-grid and quadtree. Conclusions: The proposed method can provide a reference for optimization of spatial analysis methods when processing large scale vector data. [ABSTRACT FROM AUTHOR]

In order to discover knowledge from incomplete and dynamic data, this paper proposed a technique for consistent knowledge discovery based on DL association ε L+ + rules and inductive reasoning. Firstly, by analyzing description logic sL + + rules and the dynamics of the knowledge of evolving ontology, it obtained inductive reasoning learning in evolving ontology. Basing on the support and weight of atomic set and the confidence of association sL + + rules, it achieved ε L+ + rules mining. Secondly, by getting the representative association DL sL + + rules with minimum support and minimum weight, it realized the precise identification of fundamental rules so as to complete inductive knowledge discovery. The experimental results for historical data from a certain city show that, compared with the existing mainstream methods, the proposed technique not only has better scalability, but also has higher accuracy in terms of evolving ontology and dynamic semantic data knowledge discovery. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

By studying the features of event class and relations among event elements, this paper proposed an event instance checking method based on extended description logic. This method supplemented semantic information at first, and then it verified whether an event was an instance of a specific event class by using effective reasoning capability of extended description logic, such as concept subsumption and satisfiability. Finally, it carried out an experiment to evaluate this approach. The experimental results show that this method can achieve instance checking of event classes with an accuracy of 85.12 percents. [ABSTRACT FROM AUTHOR]

To solve the problem of effectiveness analysis and verification for C4ISR system, the paper introduces a method based on ontology and softgoals analysis. It constructs a capability meta ontology, and in the semantic restrictions of the ontology, the effectiveness requirements could be captured by softgoals analysis. After that, the paper proposes an algorithm to convert the effectiveness models to effectiveness ontology, and then the consistency of the models could be verified by a symbol inference system. In the end, we present a model checking test to examine the practicality of the method. [ABSTRACT FROM AUTHOR]