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International Conference on Big Data Analytics

BDA 2017: Big Data Analytics pp 59–68Cite as

Holistic Analysis of Multi-source, Multi-feature Data: Modeling and Computation Challenges

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10721))

Abstract

As a result of our increased ability to collect data from different sources, many real-world datasets are increasingly becoming multi-featured and these features can also be of different types. Examples of such multi-feature data include different modes of interactions among people (Facebook, Twitter, LinkedIn, ...) or traffic accidents associated with diverse factors (speed, light conditions, weather, ...).

Efficiently modeling and analyzing these complex datasets to obtain actionable knowledge presents several challenges. Traditional approaches, such as using single layer networks (or monoplexes) may not be sufficient or appropriate for modeling and computation scalability. Recently, multiplexes have been proposed for the elegant handling of such data.

In this position paper, we elaborate on different types of multiplexes (homogeneous, heterogeneous and hybrid) for modeling different types of data. The benefits of this modeling in terms of ease, understanding, and usage are highlighted. However, this model brings with it a new set of challenges for its analysis. The bulk of the paper discusses these challenges and the advantages of using this approach. With the right tools, both computation and storage can be reduced in addition to accommodating scalability.

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Acknowledgment

We would like to extend our gratitude towards Dr. Sharma Chakravarthy, University of Texas whose insight and expertise greatly helped in shaping up of this position paper.

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Authors and Affiliations

  1. Information Technology Laboratory, CSE Department, University of Texas at Arlington, Arlington, TX, USA

    Abhishek Santra

  2. Department of Computer Science, University of Nebraska at Omaha, Omaha, NE, USA

    Sanjukta Bhowmick

Authors
  1. Abhishek Santra
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  2. Sanjukta Bhowmick
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Corresponding author

Correspondence to Abhishek Santra .

Editor information

Editors and Affiliations

  1. International Institute of Information Technology, Hyderabad, India

    P. Krishna Reddy

  2. Rajiv Gandhi Education City, Sonepat, India

    Ashish Sureka

  3. University of Texas at Arlington, Arlington, Texas, USA

    Sharma Chakravarthy

  4. University of Aizu, Aizu-Wakamatsu, Japan

    Subhash Bhalla

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Santra, A., Bhowmick, S. (2017). Holistic Analysis of Multi-source, Multi-feature Data: Modeling and Computation Challenges. In: Reddy, P., Sureka, A., Chakravarthy, S., Bhalla, S. (eds) Big Data Analytics. BDA 2017. Lecture Notes in Computer Science(), vol 10721. Springer, Cham. https://doi.org/10.1007/978-3-319-72413-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-72413-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72412-6

  • Online ISBN: 978-3-319-72413-3

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