MultiMedia (MM) information, such as text, audio, images, video, animation, and interactive data, allows fast and effective communication about people’ lives, their behaviors, works, interests; additionally, they are also the digital testimony of facts, objects, and locations. The widespread availability of cheap media technologies (e.g., digital cameras and video cameras, smart phones, etc.) dramatically increased the availability of MM data, their production and utilization at cheap costs. In addition, the use of Online Social Networks (OSNs) has caused at the same time MM data to be disseminated and shared to a wide audience of people.
For all intents and purposes, this enormous amount of MM content generated at unpredictable rates constitutes an instance of Big Data. Thus, modern Big Data Analytics techniques can be used to support advanced analytics by solving the volume, variety and velocity issues typical of such data, at the same time, leveraging the related multimedia features.
The new challenges in the area of multimedia and big data have created opportunities to explore the valuable information using big data analytics. In order to drive the next horizon of multimedia research, big data solutions are used to optimize processes, reduce decision-making time, and so on. Given the complex nature of the multimedia, big data analytics for multimedia is demanded to address the interaction and flexibility issues among the new intelligent objects.
The number of possible applications that can benefit from the analysis of huge amounts of multimedia data and the techniques (e.g., Computer Vision, Machine/Deep Learning, etc.) already available for processing them (e.g., speech recognition, text understanding, image analysis, video processing, etc.) is frightening: Social Network Analysis, Computer Assisted Diagnosis, Video Surveillance, Cyber Security, Virtual Assistants, Smart Cities, just to cite some of the most diffused.
As a result, the purpose of this Research Topic is to gather original research articles from both academia and industry on Big Multimedia Data Analytics applications. We welcome articles particularly focusing on the design and application of data-driven analysis techniques that use large amounts of data with the related multimedia features, eventually to solve a specific task within the above listed domains, both for real-time and batch contexts. Review articles discussing the current state of the art are also welcome.
Potential topics include but are not limited to the following:
• Large Scale Multimedia Search
• Automatic Tagging of Big Multimedia Data
• Big Multimedia Data Fusion
• Interactive Interfaces for Big Multimedia Data
• Big Multimedia Data Representation Learning
• Concept and event-based Multimedia Search in Large Collections
• Big Multimedia Data mining
• Multimedia Social Networks Analysis
• Misinformation Mining in Social Networks using Big Multimedia Data
• Medical Decision Support Applications using Large Multimodal Data
• Big Multimedia Data Analytics for Cyber Security Applications
• Big Multimedia Data Analytics for Virtual Assistants
• Big Multimedia Data Analytics for Smart Cities
• IoT and Multimedia Data Analytics
• Multimedia Analytics for Social Networks
• Machine Learning and Deep Learning for Multimedia Data Analytics
MultiMedia (MM) information, such as text, audio, images, video, animation, and interactive data, allows fast and effective communication about people’ lives, their behaviors, works, interests; additionally, they are also the digital testimony of facts, objects, and locations. The widespread availability of cheap media technologies (e.g., digital cameras and video cameras, smart phones, etc.) dramatically increased the availability of MM data, their production and utilization at cheap costs. In addition, the use of Online Social Networks (OSNs) has caused at the same time MM data to be disseminated and shared to a wide audience of people.
For all intents and purposes, this enormous amount of MM content generated at unpredictable rates constitutes an instance of Big Data. Thus, modern Big Data Analytics techniques can be used to support advanced analytics by solving the volume, variety and velocity issues typical of such data, at the same time, leveraging the related multimedia features.
The new challenges in the area of multimedia and big data have created opportunities to explore the valuable information using big data analytics. In order to drive the next horizon of multimedia research, big data solutions are used to optimize processes, reduce decision-making time, and so on. Given the complex nature of the multimedia, big data analytics for multimedia is demanded to address the interaction and flexibility issues among the new intelligent objects.
The number of possible applications that can benefit from the analysis of huge amounts of multimedia data and the techniques (e.g., Computer Vision, Machine/Deep Learning, etc.) already available for processing them (e.g., speech recognition, text understanding, image analysis, video processing, etc.) is frightening: Social Network Analysis, Computer Assisted Diagnosis, Video Surveillance, Cyber Security, Virtual Assistants, Smart Cities, just to cite some of the most diffused.
As a result, the purpose of this Research Topic is to gather original research articles from both academia and industry on Big Multimedia Data Analytics applications. We welcome articles particularly focusing on the design and application of data-driven analysis techniques that use large amounts of data with the related multimedia features, eventually to solve a specific task within the above listed domains, both for real-time and batch contexts. Review articles discussing the current state of the art are also welcome.
Potential topics include but are not limited to the following:
• Large Scale Multimedia Search
• Automatic Tagging of Big Multimedia Data
• Big Multimedia Data Fusion
• Interactive Interfaces for Big Multimedia Data
• Big Multimedia Data Representation Learning
• Concept and event-based Multimedia Search in Large Collections
• Big Multimedia Data mining
• Multimedia Social Networks Analysis
• Misinformation Mining in Social Networks using Big Multimedia Data
• Medical Decision Support Applications using Large Multimodal Data
• Big Multimedia Data Analytics for Cyber Security Applications
• Big Multimedia Data Analytics for Virtual Assistants
• Big Multimedia Data Analytics for Smart Cities
• IoT and Multimedia Data Analytics
• Multimedia Analytics for Social Networks
• Machine Learning and Deep Learning for Multimedia Data Analytics
