With the digital information and application requirement on the Internet increasing fleetly nowadays,it is urgent to work out a network storage system with a large capacity,a high availability and scalability.To solve...With the digital information and application requirement on the Internet increasing fleetly nowadays,it is urgent to work out a network storage system with a large capacity,a high availability and scalability.To solve the above-mentioned issues,a NAS-based storage network(for short NASSN)has been designed.Firstly,the NASSN integrates multi-NAS,iNAS(an iSCSI-based NAS)and enterprise SAN with the help of storage virtualization,which can provide a greater capacity and better scalability.Secondly,the NASSN can provide high availability with the help of server and storage subsystem redundancy technologies.Thirdly,the NASSN simultaneously serves for both the file I/O and the block I/O with the help of an iSCSI module,which has the advantages of NAS and SAN.Finally,the NASSN can provide higher I/O speed by a high network-attached channel which implements the direct data transfer between the storage device and client.In the experiments,the NASSN has ultra-high-throughput for both of the file I/O requests and the block I/O requests.展开更多
The clustering of trajectories over huge volumes of streaming data has been rec- ognized as critical for many modem applica- tions. In this work, we propose a continuous clustering of trajectories of moving objects ov...The clustering of trajectories over huge volumes of streaming data has been rec- ognized as critical for many modem applica- tions. In this work, we propose a continuous clustering of trajectories of moving objects over high speed data streams, which updates online trajectory clusters on basis of incremental line- segment clustering. The proposed clustering algorithm obtains trajectory clusters efficiently and stores all closed trajectory clusters in a bi- tree index with efficient search capability. Next, we present two query processing methods by utilising three proposed pruning strategies to fast handle two continuous spatio-temporal queries, threshold-based trajectory clustering queries and threshold-based trajectory outlier detections. Finally, the comprehensive experi- mental studies demonstrate that our algorithm achieves excellent effectiveness and high effi- ciency for continuous clustering on both syn- thetic and real streaming data, and the propo- sed query processing methods utilise average 90% less time than the naive query methods.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.60673191and90304011)Science Innovation Term Foundation of Guang-dong University of Foreign Studies(Grant No.GW2006-AT-005)Science Innovation Term Foundation of School of Informatics Guangdong University of Foreign Studies.
文摘With the digital information and application requirement on the Internet increasing fleetly nowadays,it is urgent to work out a network storage system with a large capacity,a high availability and scalability.To solve the above-mentioned issues,a NAS-based storage network(for short NASSN)has been designed.Firstly,the NASSN integrates multi-NAS,iNAS(an iSCSI-based NAS)and enterprise SAN with the help of storage virtualization,which can provide a greater capacity and better scalability.Secondly,the NASSN can provide high availability with the help of server and storage subsystem redundancy technologies.Thirdly,the NASSN simultaneously serves for both the file I/O and the block I/O with the help of an iSCSI module,which has the advantages of NAS and SAN.Finally,the NASSN can provide higher I/O speed by a high network-attached channel which implements the direct data transfer between the storage device and client.In the experiments,the NASSN has ultra-high-throughput for both of the file I/O requests and the block I/O requests.
基金supported by the National Natural Science Foundation of China under Grants No.61172049,No.61003251the National High Technology Research and Development Program of China(863 Program)under Grant No.2011AA040101the Doctoral Fund of Ministry of Education of Chinaunder Grant No.20100006110015
文摘The clustering of trajectories over huge volumes of streaming data has been rec- ognized as critical for many modem applica- tions. In this work, we propose a continuous clustering of trajectories of moving objects over high speed data streams, which updates online trajectory clusters on basis of incremental line- segment clustering. The proposed clustering algorithm obtains trajectory clusters efficiently and stores all closed trajectory clusters in a bi- tree index with efficient search capability. Next, we present two query processing methods by utilising three proposed pruning strategies to fast handle two continuous spatio-temporal queries, threshold-based trajectory clustering queries and threshold-based trajectory outlier detections. Finally, the comprehensive experi- mental studies demonstrate that our algorithm achieves excellent effectiveness and high effi- ciency for continuous clustering on both syn- thetic and real streaming data, and the propo- sed query processing methods utilise average 90% less time than the naive query methods.
