Journal on Big Data

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Journal on Big Data

ISSN: 2579-0048 (print)
ISSN: 2579-0056 (online) Publication Frequency: Quarterly

About the Journal

Journal on Big Data is launched in a new area when the engineering features of big data are setting off upsurges of explorations in algorithms, raising challenges on big data, and industrial development integration; and novel paradigms in this cross–disciplinary field need to be constructed by translating complex innovative ideas from various fields.

Current Issue - Vol.2, No.4, 2020 Most Viewed

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ARTICLE
An Importance Assessment Model of Open-Source Community Java Projects Based on Domain Knowledge Graph
Chengrong Yang¹, Rongjing Bu², Yan Kang², Yachuan Zhang², Hao Li^2,*, Tao Li², Junfeng Li²
Journal on Big Data, Vol.2, No.4, pp. 135-144, 2020, DOI:10.32604/jbd.2020.010000
Abstract With the rise of open-source software, the social development paradigm occupies an indispensable position in the current software development process. This paper puts forward a variant of the PageRank algorithm to build the importance assessment model, which provides quantifiable importance assessment metrics for new Java projects based on Java open-source projects or components. The critical point of the model is to use crawlers to obtain relevant information about Java open-source projects in the GitHub open-source community to build a domain knowledge graph. According to the three dimensions of the Java opensource project’s project influence, project activity and project popularity, the… More
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ARTICLE
A Novel Framework for Biomedical Text Mining
Janyl Jumadinova¹, Oliver Bonham-Carter¹, Hanzhong Zheng^1,2,*, Michael Camara¹, Dejie Shi³
Journal on Big Data, Vol.2, No.4, pp. 145-155, 2020, DOI:10.32604/jbd.2020.010090
Abstract Text mining has emerged as an effective method of handling and extracting useful information from the exponentially growing biomedical literature and biomedical databases. We developed a novel biomedical text mining model implemented by a multi-agent system and distributed computing mechanism. Our distributed system, TextMed, comprises of several software agents, where each agent uses a reinforcement learning method to update the sentiment of relevant text from a particular set of research articles related to specific keywords. TextMed can also operate on different physical machines to expedite its knowledge extraction by utilizing a clustering technique. We collected the biomedical textual data from… More
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ARTICLE
An Improved Distributed Query for Large-Scale RDF Data
Aoran Li¹, Xinmeng Wang¹, Xueliang Wang⁴, Bohan Li^1,2,3,*
Journal on Big Data, Vol.2, No.4, pp. 157-166, 2020, DOI:10.32604/jbd.2020.010358
Abstract The rigid structure of the traditional relational database leads to data redundancy, which seriously affects the efficiency of the data query and cannot effectively manage massive data. To solve this problem, we use distributed storage and parallel computing technology to query RDF data. In order to achieve efficient storage and retrieval of large-scale RDF data, we combine the respective advantage of the storage model of the relational database and the distributed query. To overcome the disadvantages of storing and querying RDF data, we design and implement a breadth-first path search algorithm based on the keyword query on a distributed platform.… More
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ARTICLE
Multi-Scale Blind Image Quality Predictor Based on Pyramidal Convolution
Feng Yuan, Xiao Shao^*
Journal on Big Data, Vol.2, No.4, pp. 167-176, 2020, DOI:10.32604/jbd.2020.015357
Abstract 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… More
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ARTICLE
l₁-norm Based GWLP for Robust Frequency Estimation
Yuan Chen¹, Liangtao Duan¹, Weize Sun^{2, *}, Jingxin Xu³
Journal on Big Data, Vol.1, No.3, pp. 107-116, 2019, DOI:10.32604/jbd.2019.07294
Abstract In this work, we address the frequency estimation problem of a complex singletone embedded in the heavy-tailed noise. With the use of the linear prediction (LP) property and l₁-norm minimization, a robust frequency estimator is developed. Since the proposed method employs the weighted l₁-norm on the LP errors, it can be regarded as an extension of the l_p-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. More
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ARTICLE
A Privacy Preserving Deep Linear Regression Scheme Based on Homomorphic Encryption
Danping Dong^{1, *}, Yue Wu¹, Lizhi Xiong¹, Zhihua Xia¹
Journal on Big Data, Vol.1, No.3, pp. 145-150, 2019, DOI:10.32604/jbd.2019.08706
Abstract 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. More
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ARTICLE
The Analysis of China’s Integrity Situation Based on Big Data
Wangdong Jiang¹, Taian Yang^{1, *}, Guang Sun^{1, 3}, Yucai Li¹, Yixuan Tang², Hongzhang Lv¹, Wenqian Xiang¹
Journal on Big Data, Vol.1, No.3, pp. 117-134, 2019, DOI:10.32604/jbd.2019.08454
Abstract 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… More
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Multi-Layer Graph Generative Model Using AutoEncoder for Recommendation Systems
Syed Falahuddin Quadri¹, Xiaoyu Li^1,*, Desheng Zheng², Muhammad Umar Aftab¹, Yiming Huang³
Journal on Big Data, Vol.1, No.1, pp. 1-7, 2019, DOI:10.32604/jbd.2019.05899
Abstract 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… More
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Code-Based Preservation Mechanism of Electronic Record in Electronic Record Center of Cloud Storage
Yan Leng^1,2, Yongjun Ren^1,2,*, Chengshan Qian³, Jinyue Xia⁴
Journal on Big Data, Vol.1, No.1, pp. 39-45, 2019, DOI:10.32604/jbd.2019.05870
Abstract With the rapid development of E-commerce and E-government, there are so many electronic records have been produced. The increasing number of electronic records brings about storage difficulties, the traditional electronic records center is difficult to cope with the current fast growth requirements of electronic records storage and management. Therefore, it is imperative to use cloud storage technology to build electronic record centers. However, electronic records also have weaknesses in the cloud storage environment, and one of them is that once electronic record owners or managers lose physical control of them, the electronic records are more likely to be tampered with… More
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ARTICLE
Network Electronic Record Management Based on Linked Data
Jian Qi¹, Yongjun Ren^1,2,*, Qirun Wang³
Journal on Big Data, Vol.1, No.1, pp. 9-15, 2019, DOI:10.32604/jbd.2019.06290
Abstract With the rapid development of e-government, e-commerce and self-media, a large number of electronic network records have been generated on the Internet. How to archive these resources has become a focus of attention in academic circles. Based on the theory of linked data, this paper analyzes the archiving of electronic records, and proposes that the corresponding network electronic records should be collected using the theory of linked data. After the resource description framework and corresponding international standards are utilized to convert the network resources, the electronic network records are stored in common databases to solve the problem of network record… More
Views:1982 Downloads:578 Download PDF