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  1. This paper proposes a reinforcement learning based message transfer model for transferring news report messages through a selected path in a trusted provenance network with the objective of maximizing the rewa...

    Authors: Sanjoy Kumar Mukherjee and Sivaji Bandyopadhyay
    Citation: Journal of Big Data 2017 4:35
  2. Classifying short texts to one category or clustering semantically related texts is challenging, and the importance of both is growing due to the rise of microblogging platforms, digital news feeds, and the li...

    Authors: Justin Zhan and Binay Dahal
    Citation: Journal of Big Data 2017 4:34
  3. The restrictions that are related to using single distribution resampling for some specific computing devices’ memory gives developers several difficulties as a result of the increased effort and time needed ...

    Authors: Wan Mohd Yaakob Wan Bejuri, Mohd Murtadha Mohamad, Raja Zahilah Raja Mohd Radzi, Mazleena Salleh and Ahmad Fadhil Yusof
    Citation: Journal of Big Data 2017 4:33
  4. The recent technology development in the concern of microarray experiments has provided many new potentialities in terms of simultaneous measurement. But new challenges have arisen from these massive quantitie...

    Authors: Fadoua Badaoui, Amine Amar, Laila Ait Hassou, Abdelhak Zoglat and Cyrille Guei Okou
    Citation: Journal of Big Data 2017 4:32
  5. Predicting stock market price is considered as a challenging task of financial time series analysis, which is of great interest to stock investors, stock traders and applied researchers. Many machine learning ...

    Authors: Meryem Ouahilal, Mohammed El Mohajir, Mohamed Chahhou and Badr Eddine El Mohajir
    Citation: Journal of Big Data 2017 4:31
  6. The explosive growing number of data from mobile devices, social media, Internet of Things and other applications has highlighted the emergence of big data. This paper aims to determine the worldwide research ...

    Authors: Ali Kalantari, Amirrudin Kamsin, Halim Shukri Kamaruddin, Nader Ale Ebrahim, Abdullah Gani, Ali Ebrahimi and Shahaboddin Shamshirband
    Citation: Journal of Big Data 2017 4:30
  7. Transfer learning has been demonstrated to be effective for many real-world applications as it exploits knowledge present in labeled training data from a source domain to enhance a model’s performance in a tar...

    Authors: Oscar Day and Taghi M. Khoshgoftaar
    Citation: Journal of Big Data 2017 4:29
  8. Big data has become popular for processing, storing and managing massive volumes of data. The clustering of datasets has become a challenging issue in the field of big data analytics. The K-means algorithm is ...

    Authors: Chowdam Sreedhar, Nagulapally Kasiviswanath and Pakanti Chenna Reddy
    Citation: Journal of Big Data 2017 4:27
  9. Predictive analytics has gained a lot of reputation in the emerging technology Big data. Predictive analytics is an advanced form of analytics. Predictive analytics goes beyond data mining. A huge amount of me...

    Authors: N. Jayanthi, B. Vijaya Babu and N. Sambasiva Rao
    Citation: Journal of Big Data 2017 4:26
  10. Text similarity measurement aims to find the commonality existing among text documents, which is fundamental to most information extraction, information retrieval, and text mining problems. Cosine similarity b...

    Authors: Sahar Sohangir and Dingding Wang
    Citation: Journal of Big Data 2017 4:25
  11. With the increasing demand for examining and extracting patterns from massive amounts of data, it is critical to be able to train large models to fulfill the needs that recent advances in the machine learning ...

    Authors: Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Flavio Villanustre and John Holt
    Citation: Journal of Big Data 2017 4:22
  12. Determining user’s perception of a brand in short periods of time has become crucial for business. Distilling brand perception directly from people’s comments in social media has promise. Current techniques fo...

    Authors: Manu Shukla, Raimundo Dos Santos, Andrew Fong and Chang-Tien Lu
    Citation: Journal of Big Data 2017 4:17
  13. Detecting botnets in a network is crucial because bots impact numerous areas such as cyber security, finance, health care, law enforcement, and more. Botnets are becoming more sophisticated and dangerous day-b...

    Authors: Sudipta Chowdhury, Mojtaba Khanzadeh, Ravi Akula, Fangyan Zhang, Song Zhang, Hugh Medal, Mohammad Marufuzzaman and Linkan Bian
    Citation: Journal of Big Data 2017 4:14
  14. The ability to process large volumes of data on the fly, as soon as they become available, is a fundamental requirement in today’s information systems. Modern distributed stream processing engines (SPEs) addre...

    Authors: Lorenzo Affetti, Riccardo Tommasini, Alessandro Margara, Gianpaolo Cugola and Emanuele Della Valle
    Citation: Journal of Big Data 2017 4:12
  15. Cloud data stores that can handle very large amounts of data, such as Apache HBase, have accelerated the use of non-relational databases (coined as NoSQL databases) as a way of addressing RDB database limitati...

    Authors: R. Ouanouki, A. April, A. Abran, A. Gomez and J. M. Desharnais
    Citation: Journal of Big Data 2017 4:10
  16. Proliferation of structural, semi-structural and no-structural data, has challenged the scalability, flexibility and processability of the traditional relational database management systems (RDBMS). The next g...

    Authors: Nadeem Qaisar Mehmood, Rosario Culmone and Leonardo Mostarda
    Citation: Journal of Big Data 2017 4:8
  17. Social media data has provided various insights into the behaviour of consumers and businesses. However, extracted data may be erroneous, or could have originated from a malicious source. Thus, quality of soci...

    Authors: Pekka Pääkkönen and Juha Jokitulppo
    Citation: Journal of Big Data 2017 4:6
  18. A fully self-managed DBMS which does not require administrator intervention is the ultimate goal of database developers. This system should automate deploying, configuration, administration, monitoring, and tu...

    Authors: George Chernishev
    Citation: Journal of Big Data 2017 4:5
  19. The popularity of social media and computer-mediated communication has resulted in high-volume and highly semantic data about digital social interactions. This constantly accumulating data has been termed as B...

    Authors: Ekaterina Olshannikova, Thomas Olsson, Jukka Huhtamäki and Hannu Kärkkäinen
    Citation: Journal of Big Data 2017 4:3
  20. Network infections that are already in progress cause challenges to those officers trying to preserve those nodes not yet infected. Static solutions can take advantage of global knowledge of the network to pro...

    Authors: Justin Zhan, Timothy Rafalski, Gennady Stashkevich and Edward Verenich
    Citation: Journal of Big Data 2017 4:2
  21. In order to forecast prices of arbitrary agricultural commodity in different wholesale markets in one city, this paper proposes a mixed model, which combines ARIMA model and PLS regression method based on time...

    Authors: Haoyang Wu, Huaili Wu, Minfeng Zhu, Weifeng Chen and Wei Chen
    Citation: Journal of Big Data 2017 4:1
  22. Graph clustering is an important technique to understand the relationships between the vertices in a big graph. In this paper, we propose a novel random-walk-based graph clustering method. The proposed method ...

    Authors: Honglei Zhang, Jenni Raitoharju, Serkan Kiranyaz and Moncef Gabbouj
    Citation: Journal of Big Data 2016 3:26
  23. Big data is a term used for very large data sets that have more varied and complex structure. These characteristics usually correlate with additional difficulties in storing, analyzing and applying further pro...

    Authors: Priyank Jain, Manasi Gyanchandani and Nilay Khare
    Citation: Journal of Big Data 2016 3:25
  24. Human behavior is essentially social and humans start their daily routines by interacting with others. There are many forms of social interactions and we have used mobile phone based social interaction feature...

    Authors: B. Padmaja, V. V. Rama Prasad and K. V. N. Sunitha
    Citation: Journal of Big Data 2016 3:24
  25. Nowadays, big data is a key component in (bio)medical research. However, the meaning of the term is subject to a wide array of opinions, without a formal definition. This hampers communication and leads to mis...

    Authors: Allard J. van Altena, Perry D. Moerland, Aeilko H. Zwinderman and Sílvia D. Olabarriaga
    Citation: Journal of Big Data 2016 3:23
  26. Community structures and relation patterns, and ranking them for social networks provide us with great knowledge about network. Such knowledge can be utilized for target marketing or grouping similar, yet dist...

    Authors: Matin Pirouz, Justin Zhan and Shahab Tayeb
    Citation: Journal of Big Data 2016 3:22
  27. In recent years, there has been an increasing amount of data being produced and stored, which is known as Big Data. The social networks, internet of things, scientific experiments and commercial services play ...

    Authors: Uthayanath Suthakar, Luca Magnoni, David Ryan Smith, Akram Khan and Julia Andreeva
    Citation: Journal of Big Data 2016 3:21
  28. Life Sciences have been established and widely accepted as a foremost Big Data discipline; as such they are a constant source of the most computationally challenging problems. In order to provide efficient sol...

    Authors: Athanassios M. Kintsakis, Fotis E. Psomopoulos and Pericles A. Mitkas
    Citation: Journal of Big Data 2016 3:20
  29. A very important area of financial risk management is systemic risk modelling, which concerns the estimation of the interrelationships between financial institutions, with the aim of establishing which of them...

    Authors: Paola Cerchiello and Paolo Giudici
    Citation: Journal of Big Data 2016 3:18
  30. Because communities are the fundamental component of big data/large data network graphs, community detection in large-scale graphs is an important area to study. Communities are a collection of a set of nodes ...

    Authors: Justin Zhan, Vivek Guidibande and Sai Phani Krishna Parsa
    Citation: Journal of Big Data 2016 3:16
  31. Patient wellness and preventative care are increasingly becoming a concern for many patients, employers, and healthcare professionals. The federal government has increased spending for wellness alongside new l...

    Authors: Ankur Agarwal, Christopher Baechle, Ravi S. Behara and Vinaya Rao
    Citation: Journal of Big Data 2016 3:15
  32. We collect big data use cases for a representative sample of telecom companies worldwide and observe a wide and skewed distribution of big data returns, with a few companies reporting large impact for a long t...

    Authors: Jacques Bughin
    Citation: Journal of Big Data 2016 3:14

Annual Journal Metrics

  • Citation Impact 2023
    Journal Impact Factor: 8.6
    5-year Journal Impact Factor: 12.4
    Source Normalized Impact per Paper (SNIP): 3.853
    SCImago Journal Rank (SJR): 2.068

    Speed 2023
    Submission to first editorial decision (median days): 56
    Submission to acceptance (median days): 205

    Usage 2023
    Downloads: 2,559,548
    Altmetric mentions: 280