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  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. The big data phenomenon is becoming a fact. Continuous increase of digitization and connecting devices to Internet are making current solutions and services smarter, richer and more personalized. The emergence...

    Authors: Mohamed Ben Brahim, Wassim Drira, Fethi Filali and Noureddine Hamdi
    Citation: Journal of Big Data 2016 3:11
  21. The use of Big Data in today’s world has become a necessity due to the massive number of technologies developed recently that keeps on providing us with data such as sensors, surveillance system and even smart...

    Authors: Haysam Selim and Justin Zhan
    Citation: Journal of Big Data 2016 3:10
  22. Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from ...

    Authors: Karl Weiss, Taghi M. Khoshgoftaar and DingDing Wang
    Citation: Journal of Big Data 2016 3:9
  23. Road accident data analysis plays an important role in identifying key factors associated with road accidents. These associated factors help in taking preventive measures to overcome the road accidents. Variou...

    Authors: Sachin Kumar and Durga Toshniwal
    Citation: Journal of Big Data 2016 3:8
  24. Finding topics from a collection of documents, such as research publications, patents, and technical reports, is helpful for summarizing large scale text collections and the world wide web. It can also help fo...

    Authors: Jose L. Hurtado, Ankur Agarwal and Xingquan Zhu
    Citation: Journal of Big Data 2016 3:7
  25. This paper describes the vision behind and the mission of the Maxeler Application Gallery ( project. First, it concentrates on the essence and performance advantages of the Maxeler dataf...

    Authors: Nemanja Trifunovic, Veljko Milutinovic, Nenad Korolija and Georgi Gaydadjiev
    Citation: Journal of Big Data 2016 3:4
  26. Using a random sample consisting of hundreds of companies worldwide, we are testing the impact on company performance of investing in big data projects targeted on three major business domains (namely, custome...

    Authors: Jacques Bughin
    Citation: Journal of Big Data 2016 3:2
  27. One of the key objectives in accident data analysis to identify the main factors associated with a road and traffic accident. However, heterogeneous nature of road accident data makes the analysis task difficu...

    Authors: Sachin Kumar and Durga Toshniwal
    Citation: Journal of Big Data 2015 2:26
  28. The term smart manufacturing refers to a future-state of manufacturing, where the real-time transmission and analysis of data from across the factory creates manufacturing intelligence, which can be used to ha...

    Authors: P. O’Donovan, K. Leahy, K. Bruton and D. T. J. O’Sullivan
    Citation: Journal of Big Data 2015 2:25
  29. With an ever-increasing amount of options, the task of selecting machine learning tools for big data can be difficult. The available tools have advantages and drawbacks, and many have overlapping uses. The wor...

    Authors: Sara Landset, Taghi M. Khoshgoftaar, Aaron N. Richter and Tawfiq Hasanin
    Citation: Journal of Big Data 2015 2:24
  30. Online reviews are often the primary factor in a customer’s decision to purchase a product or service, and are a valuable source of information that can be used to determine public opinion on these products or...

    Authors: Michael Crawford, Taghi M. Khoshgoftaar, Joseph D. Prusa, Aaron N. Richter and Hamzah Al Najada
    Citation: Journal of Big Data 2015 2:23
  31. This paper provides a multi-disciplinary overview of the research issues and achievements in the field of Big Data and its visualization techniques and tools. The main aim is to summarize challenges in visuali...

    Authors: Ekaterina Olshannikova, Aleksandr Ometov, Yevgeni Koucheryavy and Thomas Olsson
    Citation: Journal of Big Data 2015 2:22
  32. The age of big data is now coming. But the traditional data analytics may not be able to handle such large quantities of data. The question that arises now is, how to develop a high performance platform to effic...

    Authors: Chun-Wei Tsai, Chin-Feng Lai, Han-Chieh Chao and Athanasios V. Vasilakos
    Citation: Journal of Big Data 2015 2:21
  33. The manufacturing industry is currently in the midst of a data-driven revolution, which promises to transform traditional manufacturing facilities in to highly optimised smart manufacturing facilities. These s...

    Authors: Peter O’Donovan, Kevin Leahy, Ken Bruton and Dominic T. J. O’Sullivan
    Citation: Journal of Big Data 2015 2:20
  34. For over forty years, relational databases have been the leading model for data storage, retrieval and management. However, due to increasing needs for scalability and performance, alternative systems have eme...

    Authors: João Ricardo Lourenço, Bruno Cabral, Paulo Carreiro, Marco Vieira and Jorge Bernardino
    Citation: Journal of Big Data 2015 2:18

Annual Journal Metrics

  • 2022 Citation Impact
    8.1 - 2-year Impact Factor
    5.095 - SNIP (Source Normalized Impact per Paper)
    2.714 - SJR (SCImago Journal Rank)

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