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ISSN: 2579-0048 (print)
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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.
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OPEN ACCESS ARTICLE
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An Importance Assessment Model of Open-Source Community Java Projects Based on Domain Knowledge Graph
- Chengrong Yang1, Rongjing Bu2, Yan Kang2, Yachuan Zhang2, Hao Li2,*, Tao Li2, Junfeng Li2
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Journal on Big Data, Vol.2, No.4, pp. 135-144, 2020, DOI:10.32604/jbd.2020.010000
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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…
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Downloads:65
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OPEN ACCESS ARTICLE
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A Novel Framework for Biomedical Text Mining
- Janyl Jumadinova1, Oliver Bonham-Carter1, Hanzhong Zheng1,2,*, Michael Camara1, Dejie Shi3
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Journal on Big Data, Vol.2, No.4, pp. 145-155, 2020, DOI:10.32604/jbd.2020.010090
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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…
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Downloads:51
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OPEN ACCESS ARTICLE
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An Improved Distributed Query for Large-Scale RDF Data
- Aoran Li1, Xinmeng Wang1, Xueliang Wang4, Bohan Li1,2,3,*
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Journal on Big Data, Vol.2, No.4, pp. 157-166, 2020, DOI:10.32604/jbd.2020.010358
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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.…
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Views:94
Downloads:76
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OPEN ACCESS ARTICLE
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Multi-Scale Blind Image Quality Predictor Based on Pyramidal Convolution
- Feng Yuan, Xiao Shao*
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Journal on Big Data, Vol.2, No.4, pp. 167-176, 2020, DOI:10.32604/jbd.2020.015357
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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…
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Views:79
Downloads:51
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OPEN ACCESS ARTICLE
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l1-norm Based GWLP for Robust Frequency Estimation
- Yuan Chen1, Liangtao Duan1, Weize Sun2, *, Jingxin Xu3
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Journal on Big Data, Vol.1, No.3, pp. 107-116, 2019, DOI:10.32604/jbd.2019.07294
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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 lp-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.
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Views:2988
Downloads:733
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OPEN ACCESS ARTICLE
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A Privacy Preserving Deep Linear Regression Scheme Based on Homomorphic Encryption
- Danping Dong1, *, Yue Wu1, Lizhi Xiong1, Zhihua Xia1
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Journal on Big Data, Vol.1, No.3, pp. 145-150, 2019, DOI:10.32604/jbd.2019.08706
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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.
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Views:2734
Downloads:726
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OPEN ACCESS ARTICLE
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The Analysis of China’s Integrity Situation Based on Big Data
- Wangdong Jiang1, Taian Yang1, *, Guang Sun1, 3, Yucai Li1, Yixuan Tang2, Hongzhang Lv1, Wenqian Xiang1
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Journal on Big Data, Vol.1, No.3, pp. 117-134, 2019, DOI:10.32604/jbd.2019.08454
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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…
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Views:2308
Downloads:776
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OPEN ACCESS ARTICLE
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Multi-Layer Graph Generative Model Using AutoEncoder for Recommendation Systems
- Syed Falahuddin Quadri1, Xiaoyu Li1,*, Desheng Zheng2, Muhammad Umar Aftab1, Yiming Huang3
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Journal on Big Data, Vol.1, No.1, pp. 1-7, 2019, DOI:10.32604/jbd.2019.05899
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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…
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Views:2109
Downloads:836
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OPEN ACCESS ARTICLE
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Code-Based Preservation Mechanism of Electronic Record in Electronic Record Center of Cloud Storage
- Yan Leng1,2, Yongjun Ren1,2,*, Chengshan Qian3, Jinyue Xia4
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Journal on Big Data, Vol.1, No.1, pp. 39-45, 2019, DOI:10.32604/jbd.2019.05870
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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…
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Views:2088
Downloads:663
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OPEN ACCESS ARTICLE
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Network Electronic Record Management Based on Linked Data
- Jian Qi1, Yongjun Ren1,2,*, Qirun Wang3
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Journal on Big Data, Vol.1, No.1, pp. 9-15, 2019, DOI:10.32604/jbd.2019.06290
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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…
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Views:1982
Downloads:578
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