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| 1 | Visualization Research and Application of Water Quality Monitoring Data Based on ECharts显示文摘Water resources are one of the basic resources for human survival,and water protection has been becoming a major problem for countries around the world.However,most of the traditional water quality monitoring research work is still concerned with the collection of water quality indicators,and ignored the analysis of water quality monitoring data and its value.In this paper,by adopting Laravel and AdminTE framework,we introduced how to design and implement a water quality data visualization platform based on Baidu ECharts.Through the deployed water quality sensor,the collected water quality indicator data is transmitted to the big data processing platform that deployed on Tencent Cloud in real time through the 4G network.The collected monitoring data is analyzed,and the processing result is visualized by Baidu ECharts.The test results showed that the designed system could run well and will provide decision support for water resource protection. | Yifu Sheng Weida Chen Huan Wen Haijun Lin Jianjun Zhang | 2020 | Journal on Big Data2020，2，1： | 3 |
| 2 | Research on Copyright Protection Method of Material Genome Engineering Data Based on Zero-Watermarking显示文摘In order to effectively solve the problem of copyright protection of materials genome engineering data,this paper proposes a method for copyright protection of materials genome engineering data based on zero-watermarking technology.First,the important attribute values are selected from the materials genome engineering database;then,use the method of remainder to group the selected attribute values and extract eigenvalues;then,the eigenvalues sequence is obtained by the majority election method;finally,XOR the sequence with the actual copyright information to obtain the watermarking information and store it in the third-party authentication center.When a copyright dispute requires copyright authentication for the database to be detected.First,the zero-watermarking construction algorithm is used to obtain an eigenvalues sequence;then,this sequence is XORed with the watermarking information stored in the third-party authentication center to obtain copyright information to-be-detected.Finally,the ownership is determined by calculating the similarity between copyright information to-be-detected and copyright information that has practical significance.The experimental result shows that the zero-watermarking method proposed in this paper can effectively resist various common attacks,and can well achieve the copyright protection of material genome engineering database. | Lulu Cui Yabin Xu | 2020 | Journal on Big Data2020，2，2： | 1 |
| 3 | Multi-Modality Video Representation for Action Recognition显示文摘Nowadays,action recognition is widely applied in many fields.However,action is hard to define by single modality information.The difference between image recognition and action recognition is that action recognition needs more modality information to depict one action,such as the appearance,the motion and the dynamic information.Due to the state of action evolves with the change of time,motion information must be considered when representing an action.Most of current methods define an action by spatial information and motion information.There are two key elements of current action recognition methods:spatial information achieved by sampling sparsely on video frames’sequence and the motion content mostly represented by the optical flow which is calculated on consecutive video frames.However,the relevance between them in current methods is weak.Therefore,to strengthen the associativity,this paper presents a new architecture consisted of three streams to obtain multi-modality information.The advantages of our network are:(a)We propose a new sampling approach to sample evenly on the video sequence for acquiring the appearance information;(b)We utilize ResNet101 for gaining high-level and distinguished features;(c)We advance a three-stream architecture to capture temporal,spatial and dynamic information.Experimental results on UCF101 dataset illustrate that our method outperforms other previous methods. | Chao Zhu Yike Wang Dongbing Pu Miao Qi Hui Sun Lei Tan | 2020 | Journal on Big Data2020，2，3： | 1 |
| 4 | The Analysis of China’s Integrity Situation Based on Big Data显示文摘In order to study deeply the prominent problems faced by China’s clean government work,and put forward effective coping strategies,this article analyzes the network information of anti-corruption related news events,which is based on big data technology.In this study,we take the news report from the website of the Communist Party of China(CPC)Central Commission for Discipline Inspection(CCDI)as the source of data.Firstly,the obtained text data is converted to word segmentation and stop words under preprocessing,and then the pre-processed data is improved by vectorization and text clustering,finally,after text clustering,the key words of clean government work is derived from visualization analysis.According to the results of this study,it shows that China’s clean government work should focus on‘the four forms of decadence’issue,and related departments must strictly crack down five categories of phenomena,such as“illegal payment of subsidies or benefits,illegal delivery of gifts and cash gift,illegal use of official vehicles,banquets using public funds,extravagant wedding ceremonies and funeral”.The results of this study are consistent with the official data released by the CCDI’s website,which also suggests that the method is feasible and effective. | Wangdong Jiang Taian Yang Guang Sun Yucai Li Yixuan Tang Hongzhang Lv Wenqian Xiang | 2019 | Journal on Big Data2019，1，3： | 1 |
| 5 | A Meaningful Image Encryption Algorithm Based on Prediction Error and Wavelet Transform显示文摘Image encryption(IE)is a very useful and popular technology to protect the privacy of users.Most algorithms usually encrypt the original image into an image similar to texture or noise,but texture and noise are an obvious visual indication that the image has been encrypted,which is more likely to cause the attacks of enemy.To overcome this shortcoming,many image encryption systems,which convert the original image into a carrier image with visual significance have been proposed.However,the generated cryptographic image still has texture features.In line with the idea of improving the visual quality of the final password images,we proposed a meaningful image hiding algorithm based on prediction error and discrete wavelet transform.Lots of experimental results and safety analysis show that the proposed algorithm can achieve high visual quality and ensure the security at the same time. | Mengling Zou Zhengxuan Liu Xianyi Chen | 2019 | Journal on Big Data2019，1，3： | 1 |
| 6 | A New Population Initialization of Particle Swarm Optimization Method Based on PCA for Feature Selection显示文摘In many fields such as signal processing,machine learning,pattern recognition and data mining,it is common practice to process datasets containing huge numbers of features.In such cases,Feature Selection(FS)is often involved.Meanwhile,owing to their excellent global search ability,evolutionary computation techniques have been widely employed to the FS.So,as a powerful global search method and calculation fast than other EC algorithms,PSO can solve features selection problems well.However,when facing a large number of feature selection,the efficiency of PSO drops significantly.Therefore,plenty of works have been done to improve this situation.Besides,many studies have shown that an appropriate population initialization can effectively help to improve this problem.So,basing on PSO,this paper introduces a new feature selection method with filter-based population.The proposed algorithm uses Principal Component Analysis(PCA)to measure the importance of features first,then based on the sorted feature information,a population initialization method using the threshold selection and the mixed initialization is proposed.The experiments were performed on several datasets and compared to several other related algorithms.Experimental results show that the accuracy of PSO to solve feature selection problems is significantly improved after using proposed method. | Shichao Wang Yu Xue Weiwei Jia | 2021 | Journal on Big Data2021，3，1： | 1 |
| 7 | Survey on Research of RNN-Based Spatio-Temporal Sequence Prediction Algorithms显示文摘In the past few years,deep learning has developed rapidly,and many researchers try to combine their subjects with deep learning.The algorithm based on Recurrent Neural Network(RNN)has been successfully applied in the fields of weather forecasting,stock forecasting,action recognition,etc.because of its excellent performance in processing Spatio-temporal sequence data.Among them,algorithms based on LSTM and GRU have developed most rapidly because of their good design.This paper reviews the RNN-based Spatio-temporal sequence prediction algorithm,introduces the development history of RNN and the common application directions of the Spatio-temporal sequence prediction,and includes precipitation nowcasting algorithms and traffic flow forecasting algorithms.At the same time,it also compares the advantages and disadvantages,and innovations of each algorithm.The purpose of this article is to give readers a clear understanding of solutions to such problems.Finally,it prospects the future development of RNN in the Spatio-temporal sequence prediction algorithm. | Wei Fang Yupeng Chen Qiongying Xue | 2021 | Journal on Big Data2021，3，3： | 1 |
| 8 | Multi-Layer Graph Generative Model Using AutoEncoder for Recommendation Systems显示文摘Given the glut of information on the web,it is crucially important to have a system,which will parse the information appropriately and recommend users with relevant information,this class of systems is known as Recommendation Systems(RS)-it is one of the most extensively used systems on the web today.Recently,Deep Learning(DL)models are being used to generate recommendations,as it has shown state-of-the-art(SoTA)results in the field of Speech Recognition and Computer Vision in the last decade.However,the RS is a much harder problem,as the central variable in the recommendation system’s environment is the chaotic nature of the human’s purchasing/consuming behaviors and their interest.These user-item interactions cannot be fully represented in the Euclidean-Space,as it will trivialize the interaction and undermine the implicit interactions patterns.So to preserve the implicit as well as explicit interactions of user and items,we propose a new graph based recommendation framework.The fundamental idea behind this framework is not only to generate the recommendations in the unsupervised fashion but to learn the dynamics of the graph and predict the short and long term interest of the users.In this paper,we propose the first step,a heuristic multi-layer high-dimensional graph which preserves the implicit and explicit interactions between users and items using SoTA Deep Learning models such as AutoEncoders.To generate recommendation from this generated graph a new class of neural network architecture-Graph Neural Network-can be used. | Syed Falahuddin Quadri Xiaoyu Li Desheng Zheng Muhammad Umar Aftab Yiming Huang | 2019 | Journal on Big Data2019，1，1： | 1 |
| 9 | SERVQUAL Model Based Evaluation Analysis of Railway Passenger Transport Service Quality in China显示文摘Railway is the backbone of Chinese transportation system,but its poor quality of services for passengers cause complains now and then.This study first analyzed the influencing factors of service quality on railway passenger,and its quality characteristics was also explained,and finally we proposed an evaluation system of service quality on railway passenger transport.Through the statistical analysis and processing of the basic information from survey data from railway station,trains and the official website of the ticket purchase,the evaluation score of question naire was converted into the score in evaluation index system,which was based on SERVQUAL model.Finally,the evaluation index system was applied to the field test,and all levels of indicators and the overall evaluation of railway passenger transport service quality was obtained.The relevant results show that the evaluation model of this study is concise and practical,and the method has certain practicability and promotion value,which is beneficial to the department of management supervision in railway transportation. | Huiwei Niu Jiao Yao Jing Zhao Jin Wang | 2019 | Journal on Big Data2019，1，1： | 1 |
| 10 | Multi-Scale Blind Image Quality Predictor Based on Pyramidal Convolution显示文摘Traditional image quality assessment methods use the hand-crafted features to predict the image quality score,which cannot perform well in many scenes.Since deep learning promotes the development of many computer vision tasks,many IQA methods start to utilize the deep convolutional neural networks(CNN)for IQA task.In this paper,a CNN-based multi-scale blind image quality predictor is proposed to extract more effectivity multi-scale distortion features through the pyramidal convolution,which consists of two tasks:A distortion recognition task and a quality regression task.For the first task,image distortion type is obtained by the fully connected layer.For the second task,the image quality score is predicted during the distortion recognition progress.Experimental results on three famous IQA datasets show that the proposed method has better performance than the previous traditional algorithms for quality prediction and distortion recognition. | Feng Yuan Xiao Shao | 2020 | Journal on Big Data2020，2，4： | 1 |
| 11 | Brief Talk About Big Data Graph Analysis and Visualization显示文摘Graphical methods are used for construction.Data analysis and visualization are an important area of applications of big data.At the same time,visual analysis is also an important method for big data analysis.Data visualization refers to data that is presented in a visual form,such as a chart or map,to help people understand the meaning of the data.Data visualization helps people extract meaning from data quickly and easily.Visualization can be used to fully demonstrate the patterns,trends,and dependencies of your data,which can be found in other displays.Big data visualization analysis combines the advantages of computers,which can be static or interactive,interactive analysis methods and interactive technologies,which can directly help people and effectively understand the information behind big data.It is indispensable in the era of big data visualization,and it can be very intuitive if used properly.Graphical analysis also found that valuable information becomes a powerful tool in complex data relationships,and it represents a significant business opportunity.With the rise of big data,important technologies suitable for dealing with complex relationships have emerged.Graphics come in a variety of shapes and sizes for a variety of business problems.Graphic analysis is first in the visualization.The step is to get the right data and answer the goal.In short,to choose the right method,you must understand each relative strengths and weaknesses and understand the data.Key steps to get data:target;collect;clean;connect. | Guang Su Fenghua Li Wangdong Jiang | 2019 | Journal on Big Data2019，1，1： | 1 |
| 12 | Chinese News Text Classification Based on Convolutional Neural Network显示文摘With the explosive growth of Internet text information,the task of text classification is more important.As a part of text classification,Chinese news text classification also plays an important role.In public security work,public opinion news classification is an important topic.Effective and accurate classification of public opinion news is a necessary prerequisite for relevant departments to grasp the situation of public opinion and control the trend of public opinion in time.This paper introduces a combinedconvolutional neural network text classification model based on word2vec and improved TF-IDF:firstly,the word vector is trained through word2vec model,then the weight of each word is calculated by using the improved TFIDF algorithm based on class frequency variance,and the word vector and weight are combined to construct the text vector representation.Finally,the combined-convolutional neural network is used to train and test the Thucnews data set.The results show that the classification effect of this model is better than the traditional Text-RNN model,the traditional Text-CNN model and word2vec-CNN model.The test accuracy is 97.56%,the accuracy rate is 97%,the recall rate is 97%,and the F1-score is 97%. | Hanxu Wang Xin Li | 2022 | Journal on Big Data2022，4，1： | 1 |
| 13 | Research on Planar Double Compound Pendulum Based on RK-8 Algorithm显示文摘Establishing the Lagrangian equation of double complex pendulum system and obtaining the dynamic differential equation,we can analyze the motion law of double compound pendulum with application of the numerical simulation of RK-8 algorithm.When the double compound pendulum swings at a small angle,the Lagrangian equation can be simplified and the normal solution of the system can be solved.And we can walk further on the relationship between normal frequency and swing frequency of double pendulum.When the external force of normal frequency is applied to the double compound pendulum,the forced vibration of the double compound pendulum will show the characteristics of beats. | Zilu Meng Zhehan Hu Zhenzhen Ai Yanan Zhang Kunling Shan | 2021 | Journal on Big Data2021，3，1： | 0 |
| 14 | OPPR:An Outsourcing Privacy-Preserving JPEG Image Retrieval Scheme with Local Histograms in Cloud Environment显示文摘As the wide application of imaging technology,the number of big image data which may containing private information is growing fast.Due to insufficient computing power and storage space for local server device,many people hand over these images to cloud servers for management.But actually,it is unsafe to store the images to the cloud,so encryption becomes a necessary step before uploading to reduce the risk of privacy leakage.However,it is not conducive to the efficient application of image,especially in the Content-Based Image Retrieval(CBIR)scheme.This paper proposes an outsourcing privacy-preserving JPEG CBIR scheme.We design a set of JPEG format-compatible encryption method,making no file expansion to JPEG files.We firstly combine multiple adjacent 8×8 DCT coefficient blocks into big-blocks.Then,random scrambling and stream encryption are used on the binary code of DCT coefficients to protect the JPEG image privacy.The task of extracting features from encrypted images and retrieving similar images are done by the cloud server.The group index histograms of DCT coefficients are extracted from the encrypted big-blocks,then the global vector is produced to represent the JPEG image with the aid of bag-of-words(BOW)model.The security analysis and experimental results show that our proposed scheme has strong security and good retrieval performance. | Jian Tang Zhihua Xia Lan Wang Chengsheng Yuan Xueli Zhao | 2021 | Journal on Big Data2021，3，1： | 0 |
| 15 | RETRACTED:Recent Approaches for Text Summarization Using Machine Learning&LSTM0显示文摘Nowadays,data is very rapidly increasing in every domain such as social media,news,education,banking,etc.Most of the data and information is in the form of text.Most of the text contains little invaluable information and knowledge with lots of unwanted contents.To fetch this valuable information out of the huge text document,we need summarizer which is capable to extract data automatically and at the same time capable to summarize the document,particularly textual text in novel document,without losing its any vital information.The summarization could be in the form of extractive and abstractive summarization.The extractive summarization includes picking sentences of high rank from the text constructed by using sentence and word features and then putting them together to produced summary.An abstractive summarization is based on understanding the key ideas in the given text and then expressing those ideas in pure natural language.The abstractive summarization is the latest problem area for NLP(natural language processing),ML(Machine Learning)and NN(Neural Network)In this paper,the foremost techniques for automatic text summarization processes are defined.The different existing methods have been reviewed.Their effectiveness and limitations are described.Further the novel approach based on Neural Network and LSTM has been discussed.In Machine Learning approach the architecture of the underlying concept is called Encoder-Decoder. | Neeraj Kumar Sirohi Mamta Bansal S.N.Rajan | 2021 | Journal on Big Data2021，3，1： | 0 |
| 16 | l_(1)-norm Based GWLP for Robust Frequency Estimation显示文摘In this work,we address the frequency estimation problem of a complex single-tone embedded in the heavy-tailed noise.With the use of the linear prediction(LP)property and l_(1)-norm minimization,a robust frequency estimator is developed.Since the proposed method employs the weighted l_(1)-norm on the LP errors,it can be regarded as an extension of the l_(1)-generalized weighted linear predictor.Computer simulations are conducted in the environment of α-stable noise,indicating the superiority of the proposed algorithm,in terms of its robust to outliers and nearly optimal estimation performance. | Yuan Chen Liangtao Duan Weize Sun Jingxin Xu | 2019 | Journal on Big Data2019，1，3： | 0 |
| 17 | On Visualization Analysis of Stock Data显示文摘Big data technology is changing with each passing day,generating massive amounts of data every day.These data have large capacity,many types,fast growth,and valuable features.The same is true for the stock investment market.The growth of the amount of stock data generated every day is difficult to predict.The price trend in the stock market is uncertain,and the valuable information hidden in the stock data is difficult to detect.For example,the price trend of stocks,profit trends,how to make a reasonable speculation on the price trend of stocks and profit trends is a major problem that needs to be solved at this stage.This article uses the Python language to visually analyze,calculate,and predict each stock.Realize the integration and calculation of stock data to help people find out the valuable information hidden in stocks.The method proposed in this paper has been tested and proved to be feasible.It can reasonably extract,analyze and calculate the stock data,and predict the stock price trend to a certain extent. | Yue Cai Zeying Song Guang Sun Jing Wang Ziyi Guo Yi Zuo Xiaoping Fan Jianjun Zhang Lin Lang | 2019 | Journal on Big Data2019，1，3： | 0 |
| 18 | A Privacy Preserving Deep Linear Regression Scheme Based on Homomorphic Encryption显示文摘This paper proposes a strategy for machine learning in the ciphertext domain.The data to be trained in the linear regression equation is encrypted by SHE homomorphic encryption,and then trained in the ciphertext domain.At the same time,it is guaranteed that the error of the training results between the ciphertext domain and the plaintext domain is in a controllable range.After the training,the ciphertext can be decrypted and restored to the original plaintext training data. | Danping Dong Yue Wu Lizhi Xiong Zhihua Xia | 2019 | Journal on Big Data2019，1，3： | 0 |
| 19 | Social Opinion Network Analytics in Community Based Customer Churn Prediction显示文摘Community based churn prediction,or the assignment of recognising the influence of a customer’s community in churn prediction has become an important concern for firms in many different industries.While churn prediction until recent times have focused only on transactional dataset(targeted approach),the untargeted approach through product advisement,digital marketing and expressions in customer’s opinion on the social media like Twitter,have not been fully harnessed.Although this data source has become an important influencing factor with lasting impact on churn management.Since Social Network Analysis(SNA)has become a blended approach for churn prediction and management in modern era,customers residing online predominantly and collectively decide and determines the momentum of churn prediction,retention and decision support.In existing SNA approaches,customers are classified as churner or non-churner(1 or 0).Oftentimes,the customer’s opinion is also neglected and the network structure of community members are not exploited.Consequently,the pattern and influential abilities of customers’opinion on relative members of the community are not analysed.Thus,the research developed a Churn Service Information Graph(CSIG)to define a quadruple churn category(churner,potential churner,inertia customer,premium customer)for non-opinionated customers via the power of relative affinity around opinionated customers on a direct node to node SNA.The essence is to use data mining technique to investigate the patterns of opinion between people in a network or group.Consequently,every member of the online social network community is dynamically classified into a churn category for an improved targeted customer acquisition,retention and/or decision supports in churn management. | Ayodeji O.J Ibitoye Olufade F.W Onifade | 2022 | Journal on Big Data2022，4，2： | 0 |
| 20 | A Survey of Machine Learning for Big Data Processing显示文摘Today’s world is a data-driven one,with data being produced in vast amounts as a result of the rapid growth of technology that permeates every aspect of our lives.New data processing techniques must be developed and refined over time to gain meaningful insights from this vast continuous volume of produced data in various forms.Machine learning technologies provide promising solutions and potential methods for processing large quantities of data and gaining value from it.This study conducts a literature review on the application of machine learning techniques in big data processing.It provides a general overview of machine learning algorithms and techniques,a brief introduction to big data,and a discussion of related works that have used machine learning techniques in a variety of sectors to process big amounts of data.The study also discusses the challenges and issues associated with the usage of machine learning for big data. | Reem Almutiri Sarah Alhabeeb Sarah Alhumud Rehan Ullah Khan | 2022 | Journal on Big Data2022，4，2： | 0 |
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