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.3, No.2, 2021 Most Viewed

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ARTICLE
Research on Key Technologies of Electronic Shelf Labels Based on LoRa
Malak Abid Ali Khan^1,2, Xiaofeng Lian^1,*, Imran Khan Mirani^1,3, Li Tan⁴
Journal on Big Data, Vol.3, No.2, pp. 49-63, 2021, DOI:10.32604/jbd.2021.016213
Abstract The demand for Electronic Shelf Labels (ESL), according to the Internet of Things (IoT) paradigm, is expected to grow considerably in the immediate future. Various wireless communication standards are currently contending to gain an edge over the competition and provide the massive connectivity that will be required by a world in which everyday objects are expected to communicate with each other. Low-Power Wide-Area Networks (LPWANs) are continuously gaining momentum among these standards, mainly thanks to their ability to provide long-range coverage to devices, exploiting license-free frequency bands. The main theme of this work is one of the most prominent LPWAN… More
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ARTICLE
Grain Yield Predict Based on GRA-AdaBoost-SVR Model
Diantao Hu, Cong Zhang^*, Wenqi Cao, Xintao Lv, Songwu Xie
Journal on Big Data, Vol.3, No.2, pp. 65-76, 2021, DOI:10.32604/jbd.2021.016317
Abstract Grain yield security is a basic national policy of China, and changes in grain yield are influenced by a variety of factors, which often have a complex, non-linear relationship with each other. Therefore, this paper proposes a Grey Relational Analysis–Adaptive Boosting–Support Vector Regression (GRAAdaBoost-SVR) model, which can ensure the prediction accuracy of the model under small sample, improve the generalization ability, and enhance the prediction accuracy. SVR allows mapping to high-dimensional spaces using kernel functions, good for solving nonlinear problems. Grain yield datasets generally have small sample sizes and many features, making SVR a promising application for grain yield datasets.… More
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ARTICLE
Encoder-Decoder Based Multi-Feature Fusion Model for Image Caption Generation
Mingyang Duan, Jin Liu^*, Shiqi Lv
Journal on Big Data, Vol.3, No.2, pp. 77-83, 2021, DOI:10.32604/jbd.2021.016674
Abstract Image caption generation is an essential task in computer vision and image understanding. Contemporary image caption generation models usually use the encoder-decoder model as the underlying network structure. However, in the traditional Encoder-Decoder architectures, only the global features of the images are extracted, while the local information of the images is not well utilized. This paper proposed an Encoder-Decoder model based on fused features and a novel mechanism for correcting the generated caption text. We use VGG16 and Faster R-CNN to extract global and local features in the encoder first. Then, we train the bidirectional LSTM network with the fused… More
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ARTICLE
A Secure Visual Secret Sharing Scheme with Authentication Based on QR Code
Xinwei Zhong^*, Lizhi Xiong, Zhihua Xia
Journal on Big Data, Vol.3, No.2, pp. 85-95, 2021, DOI:10.32604/jbd.2021.018618
Abstract With the rise of the Internet of Things (IoT), various devices in life and industry are closely linked. Because of its high payload, stable error correction capability, and convenience in reading and writing, Quick Response (QR) code has been widely researched in IoT. However, the security of privacy data in IoT is also a very important issue. At the same time, because IoT is developing towards low-power devices in order to be applied to more fields, the technology protecting the security of private needs to have the characteristics of low computational complexity. Visual Secret Sharing (VSS), with its features of… More
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RETRACTION
Retraction Notice to: Recent Approaches for Text Summarization Using Machine Learning & LSTM0
Neeraj Kumar Sirohi, Mamta Bansal, S. N. Rajan
Journal on Big Data, Vol.3, No.2, pp. 97-97, 2021, DOI:10.32604/jbd.2021.015954
Abstract This article has no abstract. 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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ARTICLE
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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ARTICLE
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
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