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Call for papers

Visualization, Interpretation and Descriptive Big Data Science

​​​​​​​Visualization, Interpretation and Descriptive Big Data Science © Grandjean, Martin (2014). "La connaissance est un réseau". Les Cahiers du Numérique 10 (3): 37-54. DOI:10.3166/LCN.10.3.37-54.
Guest Editors

  • Alberto Fernández, Universidad de Granada
  • Isaac Triguero, University of Nottingham
  • Mikel Galar, Universidad Pública de Navarra
  • Francisco Herrera, Universidad de Granada

Description

In the era of data science, the problem of handling big data sets is becoming a main focus of attention in a wide variety of disciplines such as business, engineering, industry, etc. Data and the ability to process and extract knowledge from it are the “new gold” in the digital economy we live in. Having a larger amount of data provides new opportunities for researchers and corporations to extract better and more useful knowledge from the applications they are working with. Extracting valuable knowledge from big data, however, becomes a very interesting and challenging task where we must consider new paradigms to develop scalable algorithms.

Fortunately, the leverage of recent advances achieved in distributed technologies (e.g. Hadoop and Spark) enables data science techniques to discover unknown patterns or hidden relations from voluminous data in a faster way. The challenge lies in realizing a proper use of such technologies with data mining and machine learning techniques.

A great number problems arise when dealing with big data from the data mining and machine learning perspective. While some areas such as classification and data preprocessing are starting to get a decent number of proposals, there is a number of relevant areas with great potential that have been neglected so far. This special issue will be looking at visualization, interpretation and descriptive techniques as three promising areas of research in big data for which the number of existing works is quite limited nowadays. They link to each other with a clear focus on providing a better understanding of the data.

Data visualization is key in many real-world applications to help understand the significance of data by placing it in a visual context. In the big data setting, we need a way to quickly and easily get an overview of this massive amount of data. Likewise, obtaining a machine learning model that is interpretable by humans is required in many real-life problems. However, with the increase of data, standard machine learning and data mining will typically produce models that are too big to be easily interpreted by experts. Descriptive analytics help summarize raw data and make it interpretable, but their capabilities to deal with massive amounts of data fall short.The overall aim of this special issue is to collect research findings on the latest development, up-to-date issues, and challenges in the field of Big Data Analytics with a clear focus on visualization, interpretation and description of big data.

Proposed submissions should be original, unpublished, and novel in-depth research that makes significant methodological or application contributions.

Topics of Interest

  • Visualization for big data analytics
  • Visualization for feature extraction and selection in big data
  • Visualization for big data preparation
  • Visualization for model building and big data
  • Interpretable machine learning models for big data
  • Fuzzy methods for big data
  • Supervised descriptive rule discovery for big data
  • Descriptive big data science
  • Real-world applications of big data analytics, such as bioinformatics, social mining, cyber-crime, etc.

Submission

All submissions should be made online via the journal's Editorial Manager system, with manuscript cover letters indicating that it is to be considered for this series. For any questions regarding the scope or content, or for pre-submission enquiries, please contact the lead Guest Editor Francisco Herrera. For any queries regarding the journal or the publication process, please contact the Publishing Editor, Samuel Winthrop.

1. Summary and Scope

In this era of big data, we have witnessed a dramatic growth in the volume, variety and complexity of data, including textual, imaging, video and time sequence datasets. These large scale and heterogeneous data arise from multiple sources and applications.  Significant challenges therefore arise for the design of effective, scalable algorithms and generalized frameworks to meet the multiple requirements of real-world tasks, e.g. understanding, recognition and control. Representation learning is a key component of various intelligent data analytic algorithms, with its capacity to discover the intrinsic structure of data. Consequently, it has become critical to explore advanced representation learning techniques for large scale and heterogeneous data analytics.

This timely special issue serves as an interdisciplinary forum for researchers across the world to share their findings in this exciting area. We solicit original contributions on machine learning theories, deep learning models, surveys of recent advances, benchmark datasets and novel application scenarios. 

The topics include but are not limited to:

  • Scalable optimization algorithms for representation learning
  • Unsupervised, semi-supervised, and supervised learning (either shallow or deep) with representation learning for big data analytics
  • Representation learning in neural architecture search, deep network compression
  • Representation learning in zero-shot learning, few-shot learning
  • Representation learning in retrieval and recommendation systems for big data analytics
  • Representation learning in large scale object detection, recognition, and tracking, semantic segmentation
  • Representation learning in transfer learning and multi-modal learning
  • Representation learning in graph embedding, graph neural networks, manifold learning
  • Representation learning in natural language processing and question answering
  • Novel datasets and benchmarks for big data analytics
  • Implementation issues, parallelization, software platforms, hardware for representation learning
  • Other related topics of representation learning

2. Submission Guidelines
Authors should prepare their manuscripts according to the submission guidelines of “Big Data Analytics” outlined at the journal website https://bdataanalytics.biomedcentral.com/submission-guidelines. All papers will be peer-reviewed following a regular reviewing procedure. Each submission should clearly demonstrate evidence of benefits to society or large communities. Originality and impact, in combination with an innovative technical aspect of the proposed algorithms/datasets/applications will be the major evaluation criteria.

3. Important Dates
Submissions are open until the end of 2020. However potential authors are highly encouraged to submit papers to the special issue as early as possible. Papers will be reviewed and processed as soon as they are submitted.

4. Guest Editors

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