Multimedia big data analytics: A survey

Samira Pouyanfar, Yimin Yang, Shu Ching Chen, Mei-Ling Shyu, S. S. Iyengar

Research output: Contribution to journalReview article

37 Scopus citations

Abstract

With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. A vast amount of research work has been done in the multimedia area, targeting different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, very few research work provides a complete survey of the whole pine-line of the multimedia big data analytics, including the management and analysis of the large amount of data, the challenges and opportunities, and the promising research directions. To serve this purpose, we present this survey, which conducts a comprehensive overview of the state-of-The-Art research work on multimedia big data analytics. It also aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimedia big data analytics. To the best of our knowledge, this is the first survey that targets the most recent multimedia management techniques for very large-scale data and also provides the research studies and technologies advancing the multimedia analyses in this big data era.

Original languageEnglish (US)
Article number10
JournalACM Computing Surveys
Volume51
Issue number1
DOIs
StatePublished - Jan 1 2018

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Keywords

  • 5v challenges
  • Big data analytics
  • Data mining
  • Indexing
  • Machine learning
  • Mobile multimedia
  • Multimedia analysis
  • Multimedia databases
  • Retrieval
  • Survey

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Pouyanfar, S., Yang, Y., Chen, S. C., Shyu, M-L., & Iyengar, S. S. (2018). Multimedia big data analytics: A survey. ACM Computing Surveys, 51(1), [10]. https://doi.org/10.1145/3150226