Causal analysis is a powerful tool to unravel the data complexity and hence provide clues to achieving, say, better platform design, efficient interoperability and service management, etc. Data science will surely ben...Causal analysis is a powerful tool to unravel the data complexity and hence provide clues to achieving, say, better platform design, efficient interoperability and service management, etc. Data science will surely benefit from the advancement in this field. Here we introduce into this community a recent finding in physics on causality and the subsequent rigorous and quantitative causality analysis. The resulting formula is concise in form, involving only the common statistics namely sample covariance. A corollary is that causation implies correlation, but not vice versa, resolving the long-standing philosophical debate over correlation versus causation. The applicability to big data analysis is validated with time series purportedly generated with hidden processes. As a demonstration, a preliminary application to the gross domestic product (GDP) data of United States, China, and Japan reveals some subtle USA-China-Japan relations in certain periods. 展开更多
The rapid growth of IP traffic has contributed to wide deployment of optical devices in elastic optical network.However,the passband shape of wavelength selective switches(WSSs)that are used in reconfigurable optical ...The rapid growth of IP traffic has contributed to wide deployment of optical devices in elastic optical network.However,the passband shape of wavelength selective switches(WSSs)that are used in reconfigurable optical add-drop multiplexer(ROADM)/optical cross connect(OXC)is not ideal,causing the narrowing of spectrum.Spectral narrowing will lead to signal impairment.Therefore,guard-bands need to be inserted between adjacent paths which will cause the waste of resources.In this paper,we propose a service-based intelligent aggregation node selection and area division(ANS-AD)algorithm.For the rationality of the aggregation node selection,the ANS-AD algorithm chooses the aggregation nodes according to historical traffic information based on big data analysis.Then the ANS-AD algorithm divides the topology into areas according to the result of the aggregation node selection.Based on the ANS-AD algorithm,we propose a time-domain and spectral-domain flow aggregation(TS-FA)algorithm.For the purpose of reducing resources'waste,the TS-FA algorithm attempts to reduce the insertion of guard-bands by time-domain and spectral-domain flow aggregation.Moreover,we design a time-domain and spectral-domain flow aggregation module on software defined optical network(SDON)architecture.Finally,a simulation is designed to evaluate the performance of the proposed algorithms and the results show that our proposed algorithms can effectively reduce the resource waste.展开更多
文摘Causal analysis is a powerful tool to unravel the data complexity and hence provide clues to achieving, say, better platform design, efficient interoperability and service management, etc. Data science will surely benefit from the advancement in this field. Here we introduce into this community a recent finding in physics on causality and the subsequent rigorous and quantitative causality analysis. The resulting formula is concise in form, involving only the common statistics namely sample covariance. A corollary is that causation implies correlation, but not vice versa, resolving the long-standing philosophical debate over correlation versus causation. The applicability to big data analysis is validated with time series purportedly generated with hidden processes. As a demonstration, a preliminary application to the gross domestic product (GDP) data of United States, China, and Japan reveals some subtle USA-China-Japan relations in certain periods.
基金funded by ZTE Industry-Academia-Research Cooperation Funds under Grant No.2017110031005226
文摘The rapid growth of IP traffic has contributed to wide deployment of optical devices in elastic optical network.However,the passband shape of wavelength selective switches(WSSs)that are used in reconfigurable optical add-drop multiplexer(ROADM)/optical cross connect(OXC)is not ideal,causing the narrowing of spectrum.Spectral narrowing will lead to signal impairment.Therefore,guard-bands need to be inserted between adjacent paths which will cause the waste of resources.In this paper,we propose a service-based intelligent aggregation node selection and area division(ANS-AD)algorithm.For the rationality of the aggregation node selection,the ANS-AD algorithm chooses the aggregation nodes according to historical traffic information based on big data analysis.Then the ANS-AD algorithm divides the topology into areas according to the result of the aggregation node selection.Based on the ANS-AD algorithm,we propose a time-domain and spectral-domain flow aggregation(TS-FA)algorithm.For the purpose of reducing resources'waste,the TS-FA algorithm attempts to reduce the insertion of guard-bands by time-domain and spectral-domain flow aggregation.Moreover,we design a time-domain and spectral-domain flow aggregation module on software defined optical network(SDON)architecture.Finally,a simulation is designed to evaluate the performance of the proposed algorithms and the results show that our proposed algorithms can effectively reduce the resource waste.
