Since a sensor node handles wireless communication in data transmission and reception and is installed in poor environment, it is easily exposed to certain attacks such as data transformation and sniffing. Therefore, ...Since a sensor node handles wireless communication in data transmission and reception and is installed in poor environment, it is easily exposed to certain attacks such as data transformation and sniffing. Therefore, it is necessary to verify data integrity to properly respond to an adversary's ill-intentioned data modification. In sensor network environment, the data integrity verification method verifies the final data only, requesting multiple communications. An energy-efficient private information retrieval(PIR)-based data integrity verification method is proposed. Because the proposed method verifies the integrity of data between parent and child nodes, it is more efficient than the existing method which verifies data integrity after receiving data from the entire network or in a cluster. Since the number of messages for verification is reduced, in addition, energy could be used more efficiently. Lastly, the excellence of the proposed method is verified through performance evaluation.展开更多
In e-commerce the multidimensional data analysis based on the Web data needs integrating various data sources such as XML data and relational data on the conceptual level. A conceptual data description approach to mul...In e-commerce the multidimensional data analysis based on the Web data needs integrating various data sources such as XML data and relational data on the conceptual level. A conceptual data description approach to multidimensional data model the UML galaxy diagram is presented in order to conduct multidimensional data analysis for multiple subjects. The approach is illuminated using a case of 2_roots UML galaxy diagram that takes marketing analysis of TV products involved one retailer and several suppliers into consideration.展开更多
Supposing that the overall situation is dug out from the distributed monitoring nodes, there should be two critical obstacles, heterogenous schema and instance, to integrating heterogeneous data from different monitor...Supposing that the overall situation is dug out from the distributed monitoring nodes, there should be two critical obstacles, heterogenous schema and instance, to integrating heterogeneous data from different monitoring sensors. To tackle the challenge of heterogenous schema, an instance-based approach for schema mapping, named instance-based machine-learning （IML） approach was described. And to solve the problem of heterogenous instance, a novel approach, called statistic-based clustering （SBC） approach, which utilized clustering and statistics technologies to match large scale sources holistically, was also proposed. These two algorithms utilized the machine-leaning and clustering technology to improve the accuracy. Experimental analysis shows that the IML approach is more precise than SBC approach, reaching at least precision of 81% and recall rate of 82%. Simulation studies further show that SBC can tackle large scale sources holisticalty with 85% recall rate when there are 38 data sources.展开更多
基金supported by the Sharing and Diffusion of National R&D Outcome funded by the Korea Institute of Science and Technology Information
文摘Since a sensor node handles wireless communication in data transmission and reception and is installed in poor environment, it is easily exposed to certain attacks such as data transformation and sniffing. Therefore, it is necessary to verify data integrity to properly respond to an adversary's ill-intentioned data modification. In sensor network environment, the data integrity verification method verifies the final data only, requesting multiple communications. An energy-efficient private information retrieval(PIR)-based data integrity verification method is proposed. Because the proposed method verifies the integrity of data between parent and child nodes, it is more efficient than the existing method which verifies data integrity after receiving data from the entire network or in a cluster. Since the number of messages for verification is reduced, in addition, energy could be used more efficiently. Lastly, the excellence of the proposed method is verified through performance evaluation.
基金This project was supported by China Postdoctoral Science Foundation (2005037506) and the National Natural ScienceFoundation of China (70472029)
文摘In e-commerce the multidimensional data analysis based on the Web data needs integrating various data sources such as XML data and relational data on the conceptual level. A conceptual data description approach to multidimensional data model the UML galaxy diagram is presented in order to conduct multidimensional data analysis for multiple subjects. The approach is illuminated using a case of 2_roots UML galaxy diagram that takes marketing analysis of TV products involved one retailer and several suppliers into consideration.
基金Projects(2007AA01Z126, 2007AA01Z474) supported by the National High-Tech Research and Development Program of ChinaProject(NCET-06-0928) supported by the Program for New Century Excellent Talents in University
文摘Supposing that the overall situation is dug out from the distributed monitoring nodes, there should be two critical obstacles, heterogenous schema and instance, to integrating heterogeneous data from different monitoring sensors. To tackle the challenge of heterogenous schema, an instance-based approach for schema mapping, named instance-based machine-learning （IML） approach was described. And to solve the problem of heterogenous instance, a novel approach, called statistic-based clustering （SBC） approach, which utilized clustering and statistics technologies to match large scale sources holistically, was also proposed. These two algorithms utilized the machine-leaning and clustering technology to improve the accuracy. Experimental analysis shows that the IML approach is more precise than SBC approach, reaching at least precision of 81% and recall rate of 82%. Simulation studies further show that SBC can tackle large scale sources holisticalty with 85% recall rate when there are 38 data sources.
