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  1. Increased sensing data in the context of the Internet of Things (IoT) necessitates data analytics. It is challenging to write applications for Big Data systems due to complex, highly parallel software framewor...

    Authors: Tanmaya Mahapatra and Christian Prehofer

    Citation: Journal of Big Data 2020 7:4

    Content type: Research

    Published on:

  2. Explosive performance and memory space growth in computing machines, along with recent specialization of deep learning models have radically boosted the role of images in semantic pattern recognition. In the s...

    Authors: Mahdi Hashemi and Margeret Hall

    Citation: Journal of Big Data 2020 7:2

    Content type: Research

    Published on:

    The Retraction Note to this article has been published in Journal of Big Data 2020 7:40

  3. The analysis and processing of big data are one of the most important challenges that researchers are working on to find the best approaches to handle it with high performance, low cost and high accuracy. In t...

    Authors: Saad Ahmed Dheyab, Mohammed Najm Abdullah and Buthainah Fahran Abed

    Citation: Journal of Big Data 2019 6:112

    Content type: Methodology

    Published on:

  4. In building management, energy optimization is one of the main concern that needs to be automated. For automation, an intelligent system needs to be developed. However, an intelligent system needs to be traine...

    Authors: Bens Pardamean, Hery Harjono Muljo, Tjeng Wawan Cenggoro, Bloomest Jansen Chandra and Reza Rahutomo

    Citation: Journal of Big Data 2019 6:110

    Content type: Research

    Published on:

  5. With the rapid development of smart grids and increasing data collected in these networks, analyzing this massive data for applications such as marketing, cyber-security, and performance analysis, has gained p...

    Authors: Mohammad Hasan Ansari, Vahid Tabatab Vakili and Behnam Bahrak

    Citation: Journal of Big Data 2019 6:109

    Content type: Research

    Published on:

  6. This paper presents a lifelong learning framework which constantly adapts with changing data patterns over time through incremental learning approach. In many big data systems, iterative re-training high dimen...

    Authors: Gautam Pal, Xianbin Hong, Zhuo Wang, Hongyi Wu, Gangmin Li and Katie Atkinson

    Citation: Journal of Big Data 2019 6:108

    Content type: Case Study

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  7. Severe class imbalance between majority and minority classes in Big Data can bias the predictive performance of Machine Learning algorithms toward the majority (negative) class. Where the minority (positive) clas...

    Authors: Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy and Richard A. Bauder

    Citation: Journal of Big Data 2019 6:107

    Content type: Research

    Published on:

  8. Due to the advent of new technologies, devices, and communication tools such as social networking sites, the amount of data produced by mankind is growing rapidly every year. Big data is a collection of large ...

    Authors: Abolfazl Gandomi, Midia Reshadi, Ali Movaghar and Ahmad Khademzadeh

    Citation: Journal of Big Data 2019 6:106

    Content type: Research

    Published on:

  9. A number of technologies enabled by Internet of Thing (IoT) have been used for the prevention of various chronic diseases, continuous and real-time tracking system is a particularly important one. Wearable med...

    Authors: Abderrahmane Ed-daoudy and Khalil Maalmi

    Citation: Journal of Big Data 2019 6:104

    Content type: Research

    Published on:

  10. Feature selection is mainly used to lessen the dispensation load of data mining models. To condense the time for processing voluminous data, parallel processing is carried out with MapReduce (MR) technique. Ho...

    Authors: D. Renuka Devi and S. Sasikala

    Citation: Journal of Big Data 2019 6:103

    Content type: Research

    Published on:

  11. In the era of big data applications, the demand for more sophisticated data centers and high-performance data processing mechanisms is increasing drastically. Data are originally stored in storage systems. To ...

    Authors: Mahdi Torabzadehkashi, Siavash Rezaei, Ali HeydariGorji, Hosein Bobarshad, Vladimir Alves and Nader Bagherzadeh

    Citation: Journal of Big Data 2019 6:100

    Content type: Research

    Published on:

  12. Many systems can be represented as networks or graph collections of nodes joined by edges. The social structures in these networks can be investigated using graph theory through a process called social network...

    Authors: Nour Raeef Al-Molhem, Yasser Rahal and Mustapha Dakkak

    Citation: Journal of Big Data 2019 6:99

    Content type: Research

    Published on:

  13. In this paper we are proposing an adaptive and real-time approach to resolve real-time financial data integration latency problems and semantic heterogeneity. Due to constraints that we have faced in some proj...

    Authors: Noussair Fikri, Mohamed Rida, Noureddine Abghour, Khalid Moussaid and Amina El Omri

    Citation: Journal of Big Data 2019 6:97

    Content type: Research

    Published on:

  14. The Hadoop distributed file system (HDFS) is responsible for storing very large data-sets reliably on clusters of commodity machines. The HDFS takes advantage of replication to serve data requested by clients ...

    Authors: Hilmi Egemen Ciritoglu, John Murphy and Christina Thorpe

    Citation: Journal of Big Data 2019 6:94

    Content type: Research

    Published on:

  15. One of the most common diseases among women is breast cancer, the early diagnosis of which is of paramount importance. Given the time-consuming nature of the diagnosis process of the disease, using new methods...

    Authors: Seyed Reza Kamel, Reyhaneh YaghoubZadeh and Maryam Kheirabadi

    Citation: Journal of Big Data 2019 6:90

    Content type: Research

    Published on:

  16. With the growing use of information technology in all life domains, hacking has become more negatively effective than ever before. Also with developing technologies, attacks numbers are growing exponentially e...

    Authors: Khloud Al Jallad, Mohamad Aljnidi and Mohammad Said Desouki

    Citation: Journal of Big Data 2019 6:88

    Content type: Research

    Published on:

  17. The trend of current software inevitably leads to the big data era. There are much of large software developed from hundreds to thousands of modules. In software development projects, finding the defect pronen...

    Authors: Aris Marjuni, Teguh B. Adji and Ridi Ferdiana

    Citation: Journal of Big Data 2019 6:87

    Content type: Research

    Published on:

  18. With the popularity of e-commerce, posting online product reviews expressing customer’s sentiment or opinion towards products has grown exponentially. Sentiment analysis is a computational method that plays an...

    Authors: Bagus Setya Rintyarna, Riyanarto Sarno and Chastine Fatichah

    Citation: Journal of Big Data 2019 6:84

    Content type: Research

    Published on:

  19. As social network structures evolve constantly, it is necessary to design an efficient mechanism to track the influential nodes and accurate communities in the networks. The attributed graph represents the inf...

    Authors: Sanket Chobe and Justin Zhan

    Citation: Journal of Big Data 2019 6:83

    Content type: Research

    Published on:

  20. In the era of accelerating growth of genomic data, feature-selection techniques are believed to become a game changer that can help substantially reduce the complexity of the data, thus making it easier to ana...

    Authors: Khawla Tadist, Said Najah, Nikola S. Nikolov, Fatiha Mrabti and Azeddine Zahi

    Citation: Journal of Big Data 2019 6:79

    Content type: Survey Paper

    Published on:

  21. The DBSCAN algorithm is a prevalent method of density-based clustering algorithms, the most important feature of which is the ability to detect arbitrary shapes and varied clusters and noise data. Nevertheless...

    Authors: Safanaz Heidari, Mahmood Alborzi, Reza Radfar, Mohammad Ali Afsharkazemi and Ali Rajabzadeh Ghatari

    Citation: Journal of Big Data 2019 6:77

    Content type: Research

    Published on:

  22. The authors note a correction to the article [1]. Table 5 of the original article is incomplete. Few percentage values are missing. This article presents the corrected version of Table 5.

    Authors: Imane El Alaoui, Youssef Gahi, Rochdi Messoussi, Youness Chaabi, Alexis Todoskoff and Abdessamad Kobi

    Citation: Journal of Big Data 2019 6:76

    Content type: Correction

    Published on:

    The original article was published in Journal of Big Data 2018 5:12

  23. Data scientists spend considerable amounts of time preparing data for analysis. Data preparation is labour intensive because the data scientist typically takes fine grained control over each aspect of each ste...

    Authors: Nikolaos Konstantinou, Edward Abel, Luigi Bellomarini, Alex Bogatu, Cristina Civili, Endri Irfanie, Martin Koehler, Lacramioara Mazilu, Emanuel Sallinger, Alvaro A. A. Fernandes, Georg Gottlob, John A. Keane and Norman W. Paton

    Citation: Journal of Big Data 2019 6:74

    Content type: Research

    Published on:

  24. This study addresses the problem of traffic flow estimation based on the data from a video surveillance camera. Target problem here is formulated as counting and classifying vehicles by their driving direction...

    Authors: Aleksandr Fedorov, Kseniia Nikolskaia, Sergey Ivanov, Vladimir Shepelev and Alexey Minbaleev

    Citation: Journal of Big Data 2019 6:73

    Content type: Methodology

    Published on:

  25. In this paper, we describe the design of a machine learning-based classifier, tailored to predict whether a water meter will fail or need a replacement. Our initial attempt to train a recurrent deep neural net...

    Authors: Marco Roccetti, Giovanni Delnevo, Luca Casini and Giuseppe Cappiello

    Citation: Journal of Big Data 2019 6:70

    Content type: Research

    Published on:

  26. High class imbalance between majority and minority classes in datasets can skew the performance of Machine Learning algorithms and bias predictions in favor of the majority (negative) class. This bias, for cases ...

    Authors: Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy and Naeem Seliya

    Citation: Journal of Big Data 2019 6:69

    Content type: Research

    Published on:

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