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DEEPEYE: An Automatic big Data Visualization Framework

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big Data Mining and Analytics 2018年第1期1卷 75-82页

作者： Xuedi Qin Yuyu Luo Nan Tang Guoliang Li Department of Computer Science Tsinghua University Qatar Computing Research Institute HBKU

Data visualization transforms data into images to aid the understanding of data; therefore, it is an invaluable tool for explaining the significance of data to visually inclined people. Given a(big) dataset, the essential task of visualization is to visualize the data to tell compelling stories by selecting, filtering, and transforming the data, and picking the right visualization type such as bar charts or line charts. Our ultimate goal is to automate this task that currently requires heavy user intervention in the existing visualization systems. An evolutionized system in the field faces the following three main challenges:(1) Visualization verification: to determine whether a visualization for a given dataset is interesting, from the viewpoint of human understanding;(2) Visualization search space: a "boring" dataset may become interesting after an arbitrary combination of operations such as selections,joins, and aggregations, among others;(3) On-time responses: do not deplete the user's patience. In this paper,we present the DEEPEYE system to address these challenges. This system solves the first challenge by training a binary classifier to decide whether a particular visualization is good for a given dataset, and by using a supervised learning to rank model to rank the above good visualizations. It also considers popular visualization operations, such as grouping and binning, which can manipulate the data, and this will determine the search space. Our proposed system tackles the third challenge by incorporating database optimization techniques for sharing computations and pruning.

关键词： big data automatic data visualization visualization verification visualization ranking visualization search space

Online Internet Traffic Monitoring System Using Spark Streaming

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big Data Mining and Analytics 2018年第1期1卷 47-56页

作者： Baojun Zhou Jie Li Xiaoyan Wang Yu Gu Li Xu Yongqiang Hu Lihua Zhu the Department of Computer Science University of Tsukuba the College of Engineering Ibaraki University the School of Computer and Information Hefei University of Technology the College of Mathematics and Computer Science Fujian Normal University the Institute of Scientific and Technical Information of Qinghai

Owing to the explosive growth of Internet traffic, network operators must be able to monitor the entire network situation and efficiently manage their network resources. Traditional network analysis methods that usually work on a single machine are no longer suitable for huge traffic data owing to their poor processing ability. big data frameworks, such as Hadoop and Spark, can handle such analysis jobs even for a large amount of network traffic.However, Hadoop and Spark are inherently designed for offline data analysis. To cope with streaming data, various stream-processing-based frameworks have been proposed, such as Storm, Flink, and Spark Streaming. In this study, we propose an online Internet traffic monitoring system based on Spark Streaming. The system comprises three parts, namely, the collector, messaging system, and stream processor. We considered the TCP performance monitoring as a special use case of showing how network monitoring can be performed with our proposed system.We conducted typical experiments with a cluster in standalone mode, which showed that our system performs well for large Internet traffic measurement and monitoring.

关键词： spark streaming network monitoring big data TCP performance monitoring

Effective Variational Data Assimilation in Air-Pollution Prediction

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big Data Mining and Analytics 2018年第4期1卷 297-307页

作者： Rossella Arcucci Christopher Pain Yi-Ke Guo Data Science Institute Department of ComputingImperial College LondonLondonSW72AZUnited Kingdom Department of Earth Science&Engineering Imperial College LondonLondonSW72AZUnited Kingdom

Numerical simulations are widely used as a predictive tool to better understand complex air flows and pollution transport on the scale of individual buildings,city blocks,and entire cities.To improve prediction for air flows and pollution transport,we propose a Variational Data Assimilation(VarDA)model which assimilates data from sensors into the open-source,finite-element,fluid dynamics model Fluidity.VarDA is based on the minimization of a function which estimates the discrepancy between numerical results and observations assuming that the two sources of information,forecast and observations,have errors that are adequately described by error covariance matrices.The conditioning of the numerical problem is dominated by the condition number of the background error covariance matrix which is ill-conditioned.In this paper,a preconditioned VarDA model is presented,it is based on a reduced background error covariance matrix.The Empirical Orthogonal Functions(EOFs)method is used to alleviate the computational cost and reduce the space dimension.Experimental results are provided assuming observed values provided by sensors from positions mainly located on roofs of buildings.

关键词： data assimilation reduced order space big data preconditioning

Semi-supervised multi-layered clustering model for intrusion detection

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Digital Communications and Networks 2018年第4期4卷 277-286页

作者： Omar Y.Al-Jarrah Yousof A1-Hammdi Patti D.Yoo Sami Muhaidat Mahmoud Al-Qutayri Department of Electrical and Computer Enooineering Khalifa Universityof Science and TechnologyAbu DhabiUnited Arab Emirates Centre for Electronic Warfare Information and Cyber (EWIC)Cranfield UniversityDefence Academy of the United KingdomShrivenhamSwindonSN68LAUnited Kingdom

A Machine Learning (ML)-based Intrusion Detection and Prevention System (IDPS)requires a large amount of labeled up-to-date training data to effectively detect intrusions and generalize well to novel attacks.However,the labeling of data is costly and becomes infeasible when dealing with big data,such as those generated by Intemet of Things applications.To this effect,building an ML model that learns from non-labeled or partially labeled data is of critical importance.This paper proposes a Semi-supervised Mniti-Layered Clustering ((SMLC))model for the detection and prevention of network intrusion.SMLC has the capability to learn from partially labeled data while achieving a detection performance comparable to that of supervised ML-based IDPS.The performance of SMLC is compared with that of a well-known semi-supervised model (tri-training)and of supervised ensemble ML models, namely Random.Forest,Bagging,and AdaboostM1on two benchmark network-intrusion datasets,NSL and Kyoto 2006+.Experimental resnits show that SMLC is superior to tri-training,providing a comparable detection accuracy with 20%less labeled instances of training data.Furthermore,our results demonstrate that our scheme has a detection accuracy comparable to that of the supervised ensemble models.

关键词： Semi-supervised intrusion detection Machine learning Classification Ensembles big data

Asymptotic and Partial Asymptotic Hankel Operators on H^2(D^n)

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Acta Mathematica Sinica,English Series 2019年第11期35卷 1729-1740页

作者： Anuradha GUPTA Bhawna GUPTA Department of Mathematics Delhi College of Arts and CommerceNetaji NagarUniversity of DelhiDelhiIndia Department of Mathematics University of DelhiDelhiIndia

In this paper,we generalize the concept of asymptotic Hankel operators on H2(D)to the Hardy space H2(Dn)(over polydisk)in terms of asymptotic Hankel and partial asymptotic Hankel operators and investigate some properties in case of its weak and strong convergence.Meanwhile,we introduce ith-partial Hankel operators on H2(Dn)and obtain a characterization of its compactness for n>1.Our main results include the containment of Toeplitz algebra in the collection of all strong partial asymptotic Hankel operators on H2(Dn).It is also shown that a Toeplitz operator with symbolφis asymptotic Hankel if and only if φ is holomorphic function in L∞(Tn).

关键词： Hankel operator Asymptotic Hankel operator partial asymptotic Hankel operator i(th)-partial Hankel operator i(th)-partial big Hankel operator

Conception and Exploration of Using Data as a Service in Tunnel Construction with the NATM

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Engineering 2018年第1期4卷 123-130页

作者： Bowen Du Yanliang Du Fei Xu Peng He State Key Laboratory of Software Development Environment Beihang University Beijing 100083 China Structural Health Monitoring and Control Institute Shijiazhuang Tiedao University Shijiazhuang 050043 China Research Center of Geotechnical and Structural Engineering Shandong University Jinan 250061 China

The New Austrian Tunneling Method (NATM) has been widely used in the construction of mountain tunnels, urban metro lines, underground storage tanks, underground power houses, mining roadways, and so on. The variation patterns of advance geological prediction data, stress–strain data of supporting structures, and deformation data of the surrounding rock are vitally important in assessing the rationality and reliability of construction schemes, and provide essential information to ensure the safety and scheduling of tunnel construction. However, as the quantity of these data increases significantly, the uncertainty and discreteness of the mass data make it extremely difficult to produce a reasonable construction scheme; they also reduce the forecast accuracy of accidents and dangerous situations, creating huge challenges in tunnel construction safety. In order to solve this problem, a novel data service system is proposed that uses data-association technology and the NATM, with the support of a big data environment. This system can integrate data resources from distributed monitoring sensors during the construction process, and then identify associations and build relations among data resources under the same construction conditions. These data associations and relations are then stored in a data pool. With the development and supplementation of the data pool, similar relations can then be used under similar conditions, in order to provide data references for construction schematic designs and resource allocation. The proposed data service system also provides valuable guidance for the construction of similar projects

关键词： New Austrian Tunneling Method big data environments Data as a service Tunnel construction

PECS: Towards Personalized Edge Caching for Future Service-Centric Networks

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China Communications 2019年第8期16卷 93-106页

作者： Ming Yan Wenwen Li Chien Aun Chan Sen Bian Chih-Lin I André F. Gygax School of Information and Telecommunications Engineering Communication University of ChinaBeijing 100024China Green Communication Research Center China Mobile Research InstituteBeijing 100053China Department of Electrical and Electronic Engineering University of MelbourneVictoria 3010Australia Department of Finance Faculty of Business and EconomicsUniversity of MelbourneVictoria 3010Australia

Mobile operators face the challenge of how to best design a service-centric network that can effectively process the rapidly increasing number of bandwidth-intensive user requests while providing a higher quality of experience(QoE). Existing content distribution networks(CDN) and mobile content distribution networks(mCDN) have both latency and throughput limitations due to being multiple network hops away from end-users. Here, we first propose a new Personalized Edge Caching System(PECS) architecture that employs big data analytics and mobile edge caching to provide personalized service access at the edge of the mobile network. Based on the proposed system architecture, the edge caching strategy based on user behavior and trajectory is analyzed. Employing our proposed PECS strategies, we use data mining algorithms to analyze the personalized trajectory and service usage patterns. Our findings provide guidance on how key technologies of PECS can be employed for current and future networks. Finally, we highlight the challenges associated with realizing such a system in 5G and beyond.

关键词： big data data mining edge caching content network personalized edge caching

Calibration of CR-39 solid-state track detectors for study of laser-driven nuclear reactions

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Nuclear Science and Techniques 2020年第4期31卷 118-122页

作者： Yang-Fan He Xiao-Feng Xi Shi-Lun Guo Bing Guo Chuang-Ye He Fu-Long Liu Di Wu Ji-Hong Wei Wan-Sha Yang Luo-Huan Wang Dong-Hai Zhang Meng-Lin Qiu Guang-Fu Wang Chao-Yang Li Xiao-Fei Lan School of Physics and Space Sciences China West Normal UniversityNanchong 637002China China Institute of Atomic Energy Beijing 102413China College of Nuclear Science and Technology Beijing Normal UniversityBeijing 100875China School of Physics and Information Engineering Shanxi Normal UniversityLinfen 041004China

It is of particular interest to investigate nuclear fusion reactions generated by high-intensity lasers in plasma environments that are similar to real astrophysical conditions.We have experimentally investigated2H(d,p)3H,one of the most crucial reactions in big bang nucleosynthesis models,at the Shenguang-Ⅱlaser facility.In this work,we present a new calibration of CR-39 solidstate track detectors,which are widely employed as the main diagnostics in this type of fusion reaction experiment.We measure the dependence of the track diameter on the proton energy.It is found that the track diameters of protons with different energies are likely to be identical.We propose that in this case,the energy of the reaction products can be obtained by considering both the diameters and gray levels of these tracks.The present results would be very helpful for analyzing the2 H(d,p)3H reaction products recorded with the same batch of CR-39 solid-state track detectors.

关键词： big bang nucleosynthesis Laser-driven nuclear reactions CR-39 detectors Gray levels

Feature Selection with Graph Mining Technology

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big Data Mining and Analytics 2019年第2期2卷 73-82页

作者： Thosini Bamunu Mudiyanselage Yanqing Zhang Department of Computer Science Georgia State University

Many real world applications have problems with high dimensionality, which existing algorithms cannot overcome. A critical data preprocessing problem is feature selection, whereby its non-scalability negatively influences both the efficiency and performance of big data applications. In this research, we developed a new algorithm to reduce the dimensionality of a problem using graph-based analysis, which retains the physical meaning of the original high-dimensional feature space. Most existing feature-selection methods are based on a strong assumption that features are independent of each other. However, if the feature-selection algorithm does not take into consideration the interdependencies of the feature space, the selected data fail to correctly represent the original data. We developed a new feature-selection method to address this challenge. Our aim in this research was to examine the dependencies between features and select the optimal feature set with respect to the original data structure. Another important factor in our proposed method is that it can perform even in the absence of class labels.This is a more difficult problem that many feature-selection algorithms fail to address. In this case, they only use wrapper techniques that require a learning algorithm to select features. It is important to note that our experimental results indicates, this proposed simple ranking method performs better than other methods, independent of any particular learning algorithm used.

关键词： graph mining network embedding big data analysis feature selection high-dimensional data