In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so o...In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so on. However, there are not too many methods for detecting data-flow errors. This paper defines Petri nets with data operations(PN-DO) that can model the operations on data such as read, write and delete. Based on PN-DO, we define some data-flow errors in this paper. We construct a reachability graph with data operations for each PN-DO, and then propose a method to reduce the reachability graph. Based on the reduced reachability graph, data-flow errors can be detected rapidly. A case study is given to illustrate the effectiveness of our methods.展开更多
[目的]为打通变电工程全生命周期各阶段之间的数据隔阂,实现几何模型和工程信息的流转,提出了基于图结构的变电工程数据模型构建方法。[方法]首先分析模型几何信息和工程数据信息在变电工程各阶段的流转,将数据模型分为核心模型与场景模...[目的]为打通变电工程全生命周期各阶段之间的数据隔阂,实现几何模型和工程信息的流转,提出了基于图结构的变电工程数据模型构建方法。[方法]首先分析模型几何信息和工程数据信息在变电工程各阶段的流转,将数据模型分为核心模型与场景模型,其次按照变电工程的土建部分和电气设备来组织图的拓扑结构,然后将具有树形结构的IFC模型之中的部件之间的关系转换为“边”的形式,模型部件转为“节点”的形式,构建图结构并导入图数据库,最后设计电气设备部件级别的变电工程数据模型结构,并将该模型与核心模型的部件建立关联关系,形成变电工程图结构数据模型。[结果]测试结果表明变电工程图结构数据模型导入图数据库后可以实现几何模型与工程信息模型的灵活组合和分解,并可在面对大量几何结构和属性信息的复杂关联关系时实现高效的模型组件信息的修改和查询。[结论]基于图结构的数据模型可以同时承载几何信息和工程信息场景模型。它实现了各阶段对于模型颗粒度调整和工程信息增补和移除的需求,相比于基于表单管理的COBie(Construction Operations Building information exchange)标准可以为用户提供更容易理解的数据流转路径。展开更多
Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning models.However,data privacy and security are always a challenge in every...Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning models.However,data privacy and security are always a challenge in every field where data need to be uploaded to the cloud.Federated learning(FL)is an emerging trend for distributed training of data.The primary goal of FL is to train an efficient communication model without compromising data privacy.The traffic data have a robust spatio-temporal correlation,but various approaches proposed earlier have not considered spatial correlation of the traffic data.This paper presents FL-based traffic flow prediction with spatio-temporal correlation.This work uses a differential privacy(DP)scheme for privacy preservation of participant's data.To the best of our knowledge,this is the first time that FL is used for vehicular traffic prediction while considering the spatio-temporal correlation of traffic data with DP preservation.The proposed framework trains the data locally at the client-side with DP.It then uses the model aggregation mechanism federated graph convolutional network(FedGCN)at the server-side to find the average of locally trained models.The results of the proposed work show that the FedGCN model accurately predicts the traffic.DP scheme at client-side helps clients to set a budget for privacy loss.展开更多
基金supported in part by the National Key R&D Program of China(2017YFB1001804)Shanghai Science and Technology Innovation Action Plan Project(16511100900)
文摘In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so on. However, there are not too many methods for detecting data-flow errors. This paper defines Petri nets with data operations(PN-DO) that can model the operations on data such as read, write and delete. Based on PN-DO, we define some data-flow errors in this paper. We construct a reachability graph with data operations for each PN-DO, and then propose a method to reduce the reachability graph. Based on the reduced reachability graph, data-flow errors can be detected rapidly. A case study is given to illustrate the effectiveness of our methods.
文摘[目的]为打通变电工程全生命周期各阶段之间的数据隔阂,实现几何模型和工程信息的流转,提出了基于图结构的变电工程数据模型构建方法。[方法]首先分析模型几何信息和工程数据信息在变电工程各阶段的流转,将数据模型分为核心模型与场景模型,其次按照变电工程的土建部分和电气设备来组织图的拓扑结构,然后将具有树形结构的IFC模型之中的部件之间的关系转换为“边”的形式,模型部件转为“节点”的形式,构建图结构并导入图数据库,最后设计电气设备部件级别的变电工程数据模型结构,并将该模型与核心模型的部件建立关联关系,形成变电工程图结构数据模型。[结果]测试结果表明变电工程图结构数据模型导入图数据库后可以实现几何模型与工程信息模型的灵活组合和分解,并可在面对大量几何结构和属性信息的复杂关联关系时实现高效的模型组件信息的修改和查询。[结论]基于图结构的数据模型可以同时承载几何信息和工程信息场景模型。它实现了各阶段对于模型颗粒度调整和工程信息增补和移除的需求,相比于基于表单管理的COBie(Construction Operations Building information exchange)标准可以为用户提供更容易理解的数据流转路径。
文摘Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning models.However,data privacy and security are always a challenge in every field where data need to be uploaded to the cloud.Federated learning(FL)is an emerging trend for distributed training of data.The primary goal of FL is to train an efficient communication model without compromising data privacy.The traffic data have a robust spatio-temporal correlation,but various approaches proposed earlier have not considered spatial correlation of the traffic data.This paper presents FL-based traffic flow prediction with spatio-temporal correlation.This work uses a differential privacy(DP)scheme for privacy preservation of participant's data.To the best of our knowledge,this is the first time that FL is used for vehicular traffic prediction while considering the spatio-temporal correlation of traffic data with DP preservation.The proposed framework trains the data locally at the client-side with DP.It then uses the model aggregation mechanism federated graph convolutional network(FedGCN)at the server-side to find the average of locally trained models.The results of the proposed work show that the FedGCN model accurately predicts the traffic.DP scheme at client-side helps clients to set a budget for privacy loss.
