This paper is a research on the characteristics of power big data. According to the characteristics of ＂large volume＂, ＂species diversity＂, ＂sparse value density＂, ＂fast speed＂ of the power big data, a predict...This paper is a research on the characteristics of power big data. According to the characteristics of ＂large volume＂, ＂species diversity＂, ＂sparse value density＂, ＂fast speed＂ of the power big data, a prediction model of multi-source information fusion for large data is established, the fusion prediction of various parameters of the same object is realized. A combined algorithm of Map Reduce and neural network is used in this paper. Using clustering and nonlinear mapping ability of neural network, it can effectively solve the problem of nonlinear objective function approximation, and neural network is applied to the prediction of fusion. In this paper, neural network model using multi layer feed forward network--BP neural network. Simultaneously, to achieve large-scale data sets in parallel computing, the parallelism and real-time property of the algorithm should be considered, further combined with Reduce Map model, to realize the parallel processing of the algorithm, making it more suitable for the study of the fusion of large data. And finally, through simulation, it verifies the feasibility of the proposed model and algorithm.展开更多
It is very important for the development of electric power big data technology to use the electric power knowledge.A new electric power knowledge theory model is proposed here to solve the problem of normalized modele...It is very important for the development of electric power big data technology to use the electric power knowledge.A new electric power knowledge theory model is proposed here to solve the problem of normalized modeled electric power knowledge for the management and analysis of electric power big data.Current modeling techniques of electric power knowledge are viewed as inadequate because of the complexity and variety of the relationships among electric power system data.Ontology theory and semantic web technologies used in electric power systems and in many other industry domains provide a new kind of knowledge modeling method.Based on this,this paper proposes the structure,elements,basic calculations and multidimensional reasoning method of the new knowledge model.A modeling example of the regulations defined in electric power system operation standard is demonstrated.Different forms of the model and related technologies are also introduced,including electric power system standard modeling,multi-type data management,unstructured data searching,knowledge display and data analysis based on semantic expansion and reduction.Research shows that the new model developed here is powerful and can adapt to various knowledge expression requirements of electric power big data.With the development of electric power big data technology,it is expected that the knowledge model will be improved and will be used in more applications.展开更多
Big data analytics is emerging as one kind of the most important workloads in modern data centers. Hence,it is of great interest to identify the method of achieving the best performance for big data analytics workload...Big data analytics is emerging as one kind of the most important workloads in modern data centers. Hence,it is of great interest to identify the method of achieving the best performance for big data analytics workloads running on state-of-the-art SMT( simultaneous multithreading) processors,which needs comprehensive understanding to workload characteristics. This paper chooses the Spark workloads as the representative big data analytics workloads and performs comprehensive measurements on the POWER8 platform,which supports a wide range of multithreading. The research finds that the thread assignment policy and cache contention have significant impacts on application performance. In order to identify the potential optimization method from the experiment results,this study performs micro-architecture level characterizations by means of hardware performance counters and gives implications accordingly.展开更多
This paper introduces the implementation and data analysis associated with a state-wide power quality monitoring and analysis system in China. Corporation specifications on power quality monitors as well as on communi...This paper introduces the implementation and data analysis associated with a state-wide power quality monitoring and analysis system in China. Corporation specifications on power quality monitors as well as on communication protocols are formulated for data transmission. Big data platform and related technologies are utilized for data storage and computation. Compliance verification analysis and a power quality performance assessment are conducted, and a visualization tool for result presentation is finally presented.展开更多
This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of co...This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3].展开更多
文摘This paper is a research on the characteristics of power big data. According to the characteristics of ＂large volume＂, ＂species diversity＂, ＂sparse value density＂, ＂fast speed＂ of the power big data, a prediction model of multi-source information fusion for large data is established, the fusion prediction of various parameters of the same object is realized. A combined algorithm of Map Reduce and neural network is used in this paper. Using clustering and nonlinear mapping ability of neural network, it can effectively solve the problem of nonlinear objective function approximation, and neural network is applied to the prediction of fusion. In this paper, neural network model using multi layer feed forward network--BP neural network. Simultaneously, to achieve large-scale data sets in parallel computing, the parallelism and real-time property of the algorithm should be considered, further combined with Reduce Map model, to realize the parallel processing of the algorithm, making it more suitable for the study of the fusion of large data. And finally, through simulation, it verifies the feasibility of the proposed model and algorithm.
基金supported by Science and Technology Foundation of the State Grid Corporation of China(XT71-14-043).
文摘It is very important for the development of electric power big data technology to use the electric power knowledge.A new electric power knowledge theory model is proposed here to solve the problem of normalized modeled electric power knowledge for the management and analysis of electric power big data.Current modeling techniques of electric power knowledge are viewed as inadequate because of the complexity and variety of the relationships among electric power system data.Ontology theory and semantic web technologies used in electric power systems and in many other industry domains provide a new kind of knowledge modeling method.Based on this,this paper proposes the structure,elements,basic calculations and multidimensional reasoning method of the new knowledge model.A modeling example of the regulations defined in electric power system operation standard is demonstrated.Different forms of the model and related technologies are also introduced,including electric power system standard modeling,multi-type data management,unstructured data searching,knowledge display and data analysis based on semantic expansion and reduction.Research shows that the new model developed here is powerful and can adapt to various knowledge expression requirements of electric power big data.With the development of electric power big data technology,it is expected that the knowledge model will be improved and will be used in more applications.
基金Supported by the National High Technology Research and Development Program of China(No.2015AA015308)the State Key Development Program for Basic Research of China(No.2014CB340402)
文摘Big data analytics is emerging as one kind of the most important workloads in modern data centers. Hence,it is of great interest to identify the method of achieving the best performance for big data analytics workloads running on state-of-the-art SMT( simultaneous multithreading) processors,which needs comprehensive understanding to workload characteristics. This paper chooses the Spark workloads as the representative big data analytics workloads and performs comprehensive measurements on the POWER8 platform,which supports a wide range of multithreading. The research finds that the thread assignment policy and cache contention have significant impacts on application performance. In order to identify the potential optimization method from the experiment results,this study performs micro-architecture level characterizations by means of hardware performance counters and gives implications accordingly.
基金supported by the State Grid Science and Technology Project (GEIRI-DL-71-17-002)
文摘This paper introduces the implementation and data analysis associated with a state-wide power quality monitoring and analysis system in China. Corporation specifications on power quality monitors as well as on communication protocols are formulated for data transmission. Big data platform and related technologies are utilized for data storage and computation. Compliance verification analysis and a power quality performance assessment are conducted, and a visualization tool for result presentation is finally presented.
文摘This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3].
