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检索条件"任意字段=2019 International Conference on Modeling, Simulation and Big Data Analysis（MSBDA 2019）"

共 1293 条记录，以下是71-80 订阅

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Empowering Agroecosystem modeling with HTC Scientific Workflows: The Cycles Model Use Case

Empowering Agroecosystem Modeling with HTC Scientific Workfl...

引用

2019 IEEE international conference on big data, big data 2019

作者： Silva, Rafael Ferreira Da Mayani, Rajiv Shi, Yuning Kemanian, Armen R. Rynge, Mats Deelman, Ewa Information Sciences Institute University of Southern California Marina Del ReyCA United States Pennsylvania State University University Park Department of Plant Science PA United States

ISBN: (纸本)9781728108582

Scientific workflows have enabled large-scale scientific computations and data analysis, and lowered the entry barrier for performing computations in distributed heterogeneous platforms (e.g., HTC and HPC). In spite of impressive achievements to date, large-scale modeling, simulation, and data analytics in the long-tail still face several challenges such as efficient scheduling and execution of large-scale workflows (\mathrm{O}(10^modeling)) with very short-running tasks (few seconds). While the current trend to support next-generation workflows on leadership class machines have gained much attention in the past years, at the other end of the spectrum scientific workflows from the long-tail science have become larger and require processing massive volumes of data. In this paper, we report on our experience in designing and implementing an HTC workflow for agroecosystem modeling. We leverage well-known (task clustering and co-scheduling) and emerging (hierarchical workflows and containers) workflow optimization techniques to make the workflow planning problem tractable, and maximize resource utilization and the degree of task parallelism. Experimental results, via the implementation of a use case, show that by strategically combining the above strategies and defining an appropriate set of optimization parameters, the overall workflow makespan can be improved by 3.5 orders of magnitude when compared to a regular (non-optimized) execution of the workflow. © 2019 IEEE.

关键词： data Analytics

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E-Commerce data analysis Based on big data and Artificial Intelligence 2

E-Commerce Data Analysis Based on Big Data and Artificial In...

引用

2nd international conference on Computer Network, Electronic and Automation, ICCNEA 2019

作者： Li, Lin Dalian University of Science and Technology Digital Technology College China

ISBN: (纸本)0769566847

With the advent of the Internet age, the sales industry has also launched a new networked sales channel through the information network platform. In the network sales platform where all kinds of e-commerce are gathered, the mastery and analysis of various information data can provide accurate data decision analysis for e-commerce. At present, information data analysis systems based on big data and artificial intelligence technology provide data collection, analysis and decision-making to help e-commerce provide more efficient and accurate information services. Based on the e-commerce platform's big data intelligent data processing decision system, the product recommendation and effect of e-commerce concern in the paper are studied, and the collaborative filtering algorithm is adopted to recommend the product for the e-commerce user interest preference. After optimizing the algorithm, the simulation algorithm is compared with the rule algorithm to prove that the effect of this study is more accurate and effective for e-commerce data. © 2019 IEEE.

关键词： big data

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Study on teaching mode selection model based on big data

Study on teaching mode selection model based on big data

引用

2019 international conference on Intelligent Computing, Automation and Systems, ICICAS 2019

作者： Zhao, Zhichao Wu, Tiefeng College of Information and Electronic Technology Jiamusi University Jiamusi China

ISBN: (纸本)9781728161068

the selection of teaching mode is related to the improvement of teaching quality. In order to improve the quality of teaching and promote the transformation of teaching results, combined with big data statistical analysis method, a choosing teaching method is proposed based on big data's analysis, which constructs the statistical analysis model of teaching mode selection by descriptive statistical analysis method, and extracts the quantity of characteristic information that reflects the optimization of teaching method selection. The big data analysis model based on fuzzy C-means clustering is used to evaluate the performance of teaching methods, and the method of segmental sample detection is used to carry out regression analysis to realize the sample fitting of teaching method selection. The mathematical model of teaching mode selection is designed through the sample fitting result, and the statistical mathematical model based on big data's teaching mode selection is constructed. The simulation results show that the method is used to select the teaching method;it can effectively extract the regular characteristic quantity, which reflects the teaching performance, realizes the association rule mining of the choice of teaching method. The teaching mode is selected according to the result of big data mining and statistical analysis, improving the teaching quality, and promoting the reform of teaching methods. © 2019 IEEE.

关键词： Regression analysis

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Effects of Network Structure on Subjective Preference Diversity

Effects of Network Structure on Subjective Preference Divers...

引用

IEEE international conference on big data (big data)

作者： Lin, Anne Abeliuk, Andres Ferrara, Emilio Univ Michigan Dept Psychol Ann Arbor MI 48109 USA Univ Southern Calif Inst Informat Sci Marina Del Rey CA USA

ISBN: (纸本)9781728108582

Different social media environments enable different levels of community connectedness, which in turn affects the information that a user is exposed to. In this study, we create an agent-based model to investigate how the different levels of connectedness as well as network structure affects a group's diversity of opinions. In the model, agents are tasked with "liking" or "disliking" a set of objects. At each turn, each agent sees the most popular object amongst the agents that they are connected to. There are two main findings: 1) low network connectivity leads to more diversity amongst agents, 2) a complete network leads information popularity distribution to be more skewed, 3) the more random a network is, the more skewed information popularity distribution is. These findings suggest that online platforms that either create well-connected communities or present aggregated information of users (such as Billboard rankings) may lead to homogeneity in the subjective preference of the users over time.

关键词： social network analysis collective search social influence information diffusion agent-based modeling

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analysis of data Warehouse Architectures: modeling and Classification 21

Analysis of Data Warehouse Architectures: Modeling and Class...

引用

21st international conference on Enterprise Information Systems (ICEIS)

作者： Yang, Qishan Ge, Mouzhi Helfert, Markus Dublin City Univ Insight Ctr Data Analyt Dublin Ireland Masaryk Univ Fac Informat Brno Czech Republic

ISBN: (纸本)9789897583728

With decades of development and innovation, data warehouses and their architectures have been extended to a variety of derivatives in various environments to achieve different organisations' requirements. Although there are some ad-hoc studies on data warehouse architecture (DWHA) investigations and classifications, limited research is relevant to systematically model and classify DWHAs. Especially in the big data era, data is generated explosively. More emerging architectures and technologies are leveraged to manipulate and manage big data in this domain. It is therefore valuable to revisit and investigate DWHAs with new innovations. In this paper, we collect 116 publications and model 73 disparate DWHAs using Archimate, then 9 representative DWHAs are identified and summarised into a "big picture". Furthermore, it proposes a new classification model sticking to state-of-the-art DWHAs. This model can guide researchers and practitioners to identify, analyse and compare differences and trends of DWHAs from componental and architectural perspectives.

关键词： data Warehouse Architecture Classification modeling big data Archimate

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A Distributed Event-triggered-based Control Strategy and its Stability analysis

A Distributed Event-triggered-based Control Strategy and its...

引用

international conference on Intelligent Transportation, big data & Smart City (ICITBS)

作者： Wang, Xiangxu Zhang, Xing Mu, Qing Wu, Chang Zhang, Yan Liu, Min China Elect Power Res Inst Beijing 100085 Peoples R China

ISBN: (纸本)9781728113074

This paper investigates the problem of stable control strategy for microgrids with reactive power sharing. Firstly, the idea of multi-agent is introduced to construct the model of microgrid. Then, based on the droop-voltage control, the event-triggered control method is employed to design a control strategy. And an event-triggered-based controller for the reactive power sharing is proposed. Based on those work, the Lyapunov method is introduced to analyze the stability of constructed microgrid system. Finally, a new stability criterion is obtained.

关键词： Microgrids Event-triggered Droop-control Reactive power flow Control strategy

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引用

5th IEEE international conference on big data Computing Service and Applications (IEEE bigdataService) / Workshop on big data in Water Resources, Environment, and Hydraulic Engineering / Workshop on Medical, Healthcare, Using big data Technologies

作者： Xu, Yan Shamrooz, Summera Li, Qianmu Hou, Jun Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Wuyi Univ Intelligent Mfg Dept Wuyi Peoples R China

ISBN: (纸本)9781728100593

Network traffic prediction is not only an academic problem, but also a concern of industry and network performance department. Efficient prediction of network traffic is helpful for protocol design, traffic scheduling, detection of network attacks, etc. In this paper, we analyze real network traffic at a different time scale of three provinces, which is provided by ZTE Corporation. By means of the calculation of Hurst exponent of each traffic time series, it is proved that the network traffic data are self-similar, which indicates that we can predict network traffic utilizing nonlinear time series models. In this paper, the Echo State Network is applied. Meanwhile, rigid regression method is applied to avoid the weak-conditioned problem and the gird search algorithm is used to optimize the reservoir parameters and coefficients. The result shows that our approach can predict network traffic efficiently, which is also a verification of the self-similarity analysis.

关键词： network traffic self-similarity echo state network

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Impact characteristics simulation analysis of high pressure valve based on workbench/ls-dyna

Impact characteristics simulation analysis of high pressure ...

引用

international conference on Intelligent Transportation, big data & Smart City (ICITBS)

作者： Liu Zhong-wei Yu Yuzhe Zhai Qi Hunan Univ Technol Sch Mech Engn Zhuzhou 412007 Peoples R China

ISBN: (纸本)9781728113074

In order to analysis stress and strain changes with time which the cone valve rod impact seat when the valve is closed, to prevent excessive stress due to the impact of fatigue damage, two calculation models(Elastic-rigidimpact;Elastic-elastic impact) are researched which the cone valve rod and valve seat impact respectively when the valve is closed by using the nonlinear dynamics software workbench/ls-dyna, and the effects of closing speed and contact uniformity on the calculation results are analyzed and studied, the results show that: uneven impact stress is greater than the positive impact of stress, easy to make the valve failure;the stress of elastic-rigid impact is greater than that of the elastic-elastic impact each other;The stress between valve core and valve increases as the velocity increases when the elastic-elastic impact each other. To provide a reference and basis for the structural modification and optimization design of the high pressure valve.

关键词： high temperature and high pressure valve impact strength finite element analysis

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Relative performance evaluation of Ontario's sawmills with bootstrap DEA 8

Relative performance evaluation of Ontario's sawmills with b...

引用

8th international conference on modeling simulation and Applied Optimization, ICMSAO 2019

作者： Dia, Mohamed Shahi, Shashi K. Research Centre for Operations and Decision Sciences Department of Finance and Operations Faculty of Management Laurentian University SudburyONP3E 2C6 Canada

ISBN: (纸本)9781538676844

This study uses a non-parametric technique, Bootstrap data Envelopment analysis (DEA), in analyzing the relative technical efficiency of 125 Ontario's sawmills (with 1402 sample data observations) collected over a period of 17 years (1999 to 2015). The results indicate low levels of overall technical and managerial efficiencies in the Ontario's sawmills over the entire study period. The main source of inefficiency of the sawmills was the management of operations, especially when these sawmills were not able to adjust their inputs with changing and uncertain market demand conditions. These results provide policy makers and sawmill managers with comprehensive details of relative technical efficiencies in Ontario's sawmills, so that future resources can be reallocated to improve the performance of forest products industry in Ontario. © 2019 IEEE.

关键词： data envelopment analysis

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Bayesian Non-linear Support Vector Machine for High-Dimensional data with Incorporation of Graph Information on Features

Bayesian Non-linear Support Vector Machine for High-Dimensio...

引用

IEEE international conference on big data (big data)

作者： Sun, Wenli Chang, Changgee Long, Qi Univ Penn Dept Biostat Epidemiol & Informat Philadelphia PA 19104 USA

ISBN: (纸本)9781728108582

Support vector machine (SVM) is a popular classification method for analysis of high dimensional data such as genomics data. Recently a number of linear SVM methods have been developed to achieve feature selection through either frequentist regularization or Bayesian shrinkage, but the linear assumption may not be plausible for many real applications. In addition, recent work has demonstrated that incorporating known biological knowledge, such as those from functional genomics, into the statistical analysis of genomic data offers great promise of improved predictive accuracy and feature selection. Such biological knowledge can often be represented by graphs. In this article, we propose a novel knowledge-guided non-linear Bayesian SVM approach for analysis of high-dimensional data. Our model uses graph information that represents the relationship among the features to guide feature selection. To achieve knowledge-guided feature selection, we assign an Ising prior to the indicators representing inclusion/exclusion of the features in the model. An efficient MCMC algorithm is developed for posterior inference. The performance of our method is evaluated and compared with several penalized linear SVM and the standard kernel SVM method in terms of prediction and feature selection in extensive simulation studies. Also, analyses of genomic data from a cancer study show that our method yields a more accurate prediction model for patient survival and reveals biologically more meaningful results than the existing methods.

关键词： Bayesian support vector machine Gaussian process Knowledge-guided Pathway information Ising prior

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