In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising...In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising data based on a semantic description in coal mines is studied.First,the semantic and numerical-based hybrid description method of security supervising data in coal mines is described.Secondly,the similarity measurement method of semantic and numerical data are separately given and a weight-based hybrid similarity measurement method for the security supervising data based on a semantic description in coal mines is presented.Thirdly,taking the hybrid similarity measurement method as the distance criteria and using a grid methodology for reference,an improved CURE clustering algorithm based on the grid is presented.Finally,the simulation results of a security supervising data set in coal mines validate the efficiency of the algorithm.展开更多
To solve the shortage problem of the semantic descrip- tion scope and verification capability existed in the security policy, a semantic description method for the security policy based on ontology is presented. By de...To solve the shortage problem of the semantic descrip- tion scope and verification capability existed in the security policy, a semantic description method for the security policy based on ontology is presented. By defining the basic elements of the security policy, the relationship model between the ontology and the concept of security policy based on the Web ontology language （OWL） is established, so as to construct the semantic description framework of the security policy. Through modeling and reasoning in the Protege, the ontology model of authorization policy is proposed, and the first-order predicate description logic is introduced to the analysis and verification of the model. Results show that the ontology-based semantic description of security policy has better flexibility and practicality.展开更多
Urban traffic generates massive and diverse data,yet most systems remain fragmented.Current approaches to congestion management suffer from weak data consistency and poor scalability.This study addresses this gap by p...Urban traffic generates massive and diverse data,yet most systems remain fragmented.Current approaches to congestion management suffer from weak data consistency and poor scalability.This study addresses this gap by proposing the Urban Traffic Congestion Unified Metadata Model(UTC-UMM).The goal is to provide a standardized and extensible framework for describing,extracting,and storing multisource traffic data in smart cities.The model defines a two-tier specification that organizes nine core traffic resource classes.It employs an eXtensible Markup Language(XML)Schema that connects general elements with resource-specific elements.This design ensures both syntactic and semantic interoperability across siloed datasets.Extension principles allow new elements or constraints to be introducedwithout breaking backward compatibility.Adistributed pipeline is implemented usingHadoop Distributed File System(HDFS)and HBase.It integrates computer vision for video and natural language processing for text to automate metadata extraction.Optimized row-key designs enable low-latency queries.Performance is tested with the Yahoo!Cloud Serving Benchmark(YCSB),which shows linear scalability and high throughput.The results demonstrate that UTC-UMM can unify heterogeneous traffic data while supporting real-time analytics.The discussion highlights its potential to improve data reuse,portability,and scalability in urban congestion studies.Future research will explore integration with association rulemining and advanced knowledge representation to capture richer spatiotemporal traffic patterns.展开更多
基金The National Natural Science Foundation of China(No.50674086)Specialized Research Fund for the Doctoral Program of Higher Education(No.20060290508)the Postdoctoral Scientific Program of Jiangsu Province(No.0701045B)
文摘In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising data based on a semantic description in coal mines is studied.First,the semantic and numerical-based hybrid description method of security supervising data in coal mines is described.Secondly,the similarity measurement method of semantic and numerical data are separately given and a weight-based hybrid similarity measurement method for the security supervising data based on a semantic description in coal mines is presented.Thirdly,taking the hybrid similarity measurement method as the distance criteria and using a grid methodology for reference,an improved CURE clustering algorithm based on the grid is presented.Finally,the simulation results of a security supervising data set in coal mines validate the efficiency of the algorithm.
基金Supported by the National Natural Science Foundation of China(61462020,61363006,61163057)the Guangxi Experiment Center of Information Science Foundation(20130329)the Guangxi Natural Science Foundation(2014GXNSFAA118375)
文摘To solve the shortage problem of the semantic descrip- tion scope and verification capability existed in the security policy, a semantic description method for the security policy based on ontology is presented. By defining the basic elements of the security policy, the relationship model between the ontology and the concept of security policy based on the Web ontology language （OWL） is established, so as to construct the semantic description framework of the security policy. Through modeling and reasoning in the Protege, the ontology model of authorization policy is proposed, and the first-order predicate description logic is introduced to the analysis and verification of the model. Results show that the ontology-based semantic description of security policy has better flexibility and practicality.
基金supported by the National Natural Science Foundation of China(Grant No.62172033).
文摘Urban traffic generates massive and diverse data,yet most systems remain fragmented.Current approaches to congestion management suffer from weak data consistency and poor scalability.This study addresses this gap by proposing the Urban Traffic Congestion Unified Metadata Model(UTC-UMM).The goal is to provide a standardized and extensible framework for describing,extracting,and storing multisource traffic data in smart cities.The model defines a two-tier specification that organizes nine core traffic resource classes.It employs an eXtensible Markup Language(XML)Schema that connects general elements with resource-specific elements.This design ensures both syntactic and semantic interoperability across siloed datasets.Extension principles allow new elements or constraints to be introducedwithout breaking backward compatibility.Adistributed pipeline is implemented usingHadoop Distributed File System(HDFS)and HBase.It integrates computer vision for video and natural language processing for text to automate metadata extraction.Optimized row-key designs enable low-latency queries.Performance is tested with the Yahoo!Cloud Serving Benchmark(YCSB),which shows linear scalability and high throughput.The results demonstrate that UTC-UMM can unify heterogeneous traffic data while supporting real-time analytics.The discussion highlights its potential to improve data reuse,portability,and scalability in urban congestion studies.Future research will explore integration with association rulemining and advanced knowledge representation to capture richer spatiotemporal traffic patterns.
