What is it about?

Based on the references from SCI-EXPANDED(SCIE), SSCI, CPCI-S, CPCI-SSHSI and arXiv databases in 2008-2017, the hotspots and emerging trends of Multimedia Big Data were identified for the first time by visualizing the co-cited references network, co-occurrence keywords network, burst references, burst keywords, Dual-Map Overlays network and Timeline networks with the information visualization software CiteSpaceV, Google Fusion Tables and Carrot2. The results show that: (1)Multimedia Big Data research has spread across the globe, especially in the United States, China and some European countries; (2)"big data", "web application", "data mining”, “virtual screening", "cloud service”, “structure-activity relationship", "similarity search problems", "concept modeling", etc. are the research hotspots; (3) the research focus evolved mainly from "basic security problems" and "algorithm problems" in the early, to technical problems, then to the applications and social impacts, and to "mobile internet", "cloud", "data screening", "payment security", etc. till now.(4) The emerging trends mainly include "social influence modeling", "mobile media cloud", "video surveillance system", "semantic relations", "privacy”, “internet of thing", "precision medicine", "parallel massive clustering", etc.; (5) Multimedia Big Data research is developing toward interdisciplinary, of which "mathematics and systems" is a hot discipline and "medicine and clinical" is an emerging discipline; (6) the fusion development of multimedia big data with smart city, automotive industry, clothing industry and medical industry will be the trends of the times. The paper aims to promote the development of related theories on Multimedia Big Data and provide reference for researchers to identify relevant research directions.

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Why is it important?

Multimedia Big Data, known as the biggest big data, is becoming the forefront of big data research. However, a visualization research on the hotspots and trends of Multimedia Big Data through scientometric is still lacking.


The study can provide a reference for the theoretical research and practical development of Multimedia Big Data. What’s more, it may also provide some directions for scholars to identify future research fields about Multimedia Big Data.

Dr. Yuran Jin
University of Science and Technology Liaoning

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This page is a summary of: Visualizing the Hotspots and Emerging Trends of Multimedia Big Data through Scientometrics, Multimedia Tools and Applications, June 2018, Springer Science + Business Media, DOI: 10.1007/s11042-018-6172-5.
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