The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property.For sparse and small graphs,the most efficient appro...The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property.For sparse and small graphs,the most efficient approach to such visualization is node-link diagrams,whereas for dense graphs with attached data,adjacency matrices might be the better choice.Because graphs can contain both properties,being globally sparse and locally dense,a combination of several visual metaphors as well as static and dynamic visualizations is beneficial.In this paper,a visually and algorithmically scalable approach that provides views and perspectives on graphs as interactively linked node-link and adjacency matrix visualizations is described.As the novelty of this technique,insights such as clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the other views.Moreover,the importance of nodes and node groups can be detected,computed,and visualized by considering several layout and reordering properties in combination as well as different edge properties for the same set of nodes.As an additional feature set,an automatic identification of groups,clusters,and outliers is provided over time,and based on the visual outcome of the node-link and matrix visualizations,the repertoire of the supported layout and matrix reordering techniques is extended,and more interaction techniques are provided when considering the dynamics of the graph data.Finally,a small user experiment was conducted to investigate the usability of the proposed approach.The usefulness of the proposed tool is illustrated by applying it to a graph dataset,such as e co-authorships,co-citations,and a Comprehensible Perl Archive Network distribution.展开更多
节能,是无线传感器网络最关注的问题之一。让节点进行周期性的侦听与睡眠是一种常用的节能机制。但是,这种节能机制在数据报的传输过程中会引入较长的端到端延时,而且延时与整个周期的长度成正比。提出了一种应用于无线传感器网络的低...节能,是无线传感器网络最关注的问题之一。让节点进行周期性的侦听与睡眠是一种常用的节能机制。但是,这种节能机制在数据报的传输过程中会引入较长的端到端延时,而且延时与整个周期的长度成正比。提出了一种应用于无线传感器网络的低延时的节能MAC协议:RLL-MAC。该协议在S-MAC/AL(S-MAC with Adaptive Listening)的基础上,增加了数据报重排序机制,即能够根据各个节点工作周期的时序关系,动态的调整数据报的发送次序,从而减少数据报的端到端延迟。通过理论分析和仿真实验表明,与S-MAC/AL相比,RLL-MAC在保持低能耗的同时,极大的降低了端到端延迟。展开更多
With the maturation and advancement of blockchain technology,a novel execute-order-validate(EOV)architecture has been proposed,allowing transactions to be executed in parallel during the execution phase.However,parall...With the maturation and advancement of blockchain technology,a novel execute-order-validate(EOV)architecture has been proposed,allowing transactions to be executed in parallel during the execution phase.However,parallel execution may lead to multi-version concurrency control(MVCC)conflicts during the validation phase,resulting in transaction invalidation.Based on different causes,we categorize conflicts in the EOV blockchain into two types:within-block conflicts and cross-block conflicts,and propose an optimization solution called FabricMan based on Fabric v2.4.For within-block conflicts,a reordering algorithm is designed to improve the transaction success rate and parallel validation is implemented based on the transaction conflict graph.We also merge transfer transactions to prevent triggering multiple version checks.For cross-block conflicts,a cache-based version validation mechanism is implemented to detect and terminate invalid transactions in advance.Experimental comparisons are conducted between FabricMan and two other systems,Fabric and Fabric++.The results show that FabricMan outperforms the other two systems in terms of throughput,transaction abort rate,algorithm execution time,and other experimental metrics.展开更多
Unstructured and irregular graph data causes strong randomness and poor locality of data accesses in graph processing.This paper optimizes the depth-branch-resorting algorithm(DBR),and proposes a branch-alternation-re...Unstructured and irregular graph data causes strong randomness and poor locality of data accesses in graph processing.This paper optimizes the depth-branch-resorting algorithm(DBR),and proposes a branch-alternation-resorting algorithm(BAR).In order to make the algorithm run in parallel and improve the efficiency of algorithm operation,the BAR algorithm is mapped onto the reconfigurable array processor(APR-16)to achieve vertex reordering,effectively improving the locality of graph data.This paper validates the BAR algorithm on the GraphBIG framework,by utilizing the reordered dataset with BAR on breadth-first search(BFS),single source shortest paht(SSSP)and betweenness centrality(BC)algorithms for traversal.The results show that compared with DBR and Corder algorithms,BAR can reduce execution time by up to 33.00%,and 51.00%seperatively.In terms of data movement,the BAR algorithm has a maximum reduction of 39.00%compared with the DBR algorithm and 29.66%compared with Corder algorithm.In terms of computational complexity,the BAR algorithm has a maximum reduction of 32.56%compared with DBR algorithm and53.05%compared with Corder algorithm.展开更多
文摘The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property.For sparse and small graphs,the most efficient approach to such visualization is node-link diagrams,whereas for dense graphs with attached data,adjacency matrices might be the better choice.Because graphs can contain both properties,being globally sparse and locally dense,a combination of several visual metaphors as well as static and dynamic visualizations is beneficial.In this paper,a visually and algorithmically scalable approach that provides views and perspectives on graphs as interactively linked node-link and adjacency matrix visualizations is described.As the novelty of this technique,insights such as clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the other views.Moreover,the importance of nodes and node groups can be detected,computed,and visualized by considering several layout and reordering properties in combination as well as different edge properties for the same set of nodes.As an additional feature set,an automatic identification of groups,clusters,and outliers is provided over time,and based on the visual outcome of the node-link and matrix visualizations,the repertoire of the supported layout and matrix reordering techniques is extended,and more interaction techniques are provided when considering the dynamics of the graph data.Finally,a small user experiment was conducted to investigate the usability of the proposed approach.The usefulness of the proposed tool is illustrated by applying it to a graph dataset,such as e co-authorships,co-citations,and a Comprehensible Perl Archive Network distribution.
文摘节能,是无线传感器网络最关注的问题之一。让节点进行周期性的侦听与睡眠是一种常用的节能机制。但是,这种节能机制在数据报的传输过程中会引入较长的端到端延时,而且延时与整个周期的长度成正比。提出了一种应用于无线传感器网络的低延时的节能MAC协议:RLL-MAC。该协议在S-MAC/AL(S-MAC with Adaptive Listening)的基础上,增加了数据报重排序机制,即能够根据各个节点工作周期的时序关系,动态的调整数据报的发送次序,从而减少数据报的端到端延迟。通过理论分析和仿真实验表明,与S-MAC/AL相比,RLL-MAC在保持低能耗的同时,极大的降低了端到端延迟。
文摘With the maturation and advancement of blockchain technology,a novel execute-order-validate(EOV)architecture has been proposed,allowing transactions to be executed in parallel during the execution phase.However,parallel execution may lead to multi-version concurrency control(MVCC)conflicts during the validation phase,resulting in transaction invalidation.Based on different causes,we categorize conflicts in the EOV blockchain into two types:within-block conflicts and cross-block conflicts,and propose an optimization solution called FabricMan based on Fabric v2.4.For within-block conflicts,a reordering algorithm is designed to improve the transaction success rate and parallel validation is implemented based on the transaction conflict graph.We also merge transfer transactions to prevent triggering multiple version checks.For cross-block conflicts,a cache-based version validation mechanism is implemented to detect and terminate invalid transactions in advance.Experimental comparisons are conducted between FabricMan and two other systems,Fabric and Fabric++.The results show that FabricMan outperforms the other two systems in terms of throughput,transaction abort rate,algorithm execution time,and other experimental metrics.
基金the National Key R&D Program of China(No.2022ZD0119001)the National Natural Science Foundation of China(No.61834005)+3 种基金the Shaanxi Province Key R&D Plan(No.2022GY-027)the Key Scientific Research Project of Shaanxi Department of Education(No.22JY060)the Education Research Project of XUPT(No.JGA202108)the Graduate Student Innovation Fund of Xi'an University of Posts and Telecommunications(No.CXJJZL2022011)。
文摘Unstructured and irregular graph data causes strong randomness and poor locality of data accesses in graph processing.This paper optimizes the depth-branch-resorting algorithm(DBR),and proposes a branch-alternation-resorting algorithm(BAR).In order to make the algorithm run in parallel and improve the efficiency of algorithm operation,the BAR algorithm is mapped onto the reconfigurable array processor(APR-16)to achieve vertex reordering,effectively improving the locality of graph data.This paper validates the BAR algorithm on the GraphBIG framework,by utilizing the reordered dataset with BAR on breadth-first search(BFS),single source shortest paht(SSSP)and betweenness centrality(BC)algorithms for traversal.The results show that compared with DBR and Corder algorithms,BAR can reduce execution time by up to 33.00%,and 51.00%seperatively.In terms of data movement,the BAR algorithm has a maximum reduction of 39.00%compared with the DBR algorithm and 29.66%compared with Corder algorithm.In terms of computational complexity,the BAR algorithm has a maximum reduction of 32.56%compared with DBR algorithm and53.05%compared with Corder algorithm.
