Analytical Aspects of Multimedia Big Data Computing and Future Scope

Abstract

The multimedia data are also known as interactive data. The multimedia is progressively turning into the “greatest big data” which are the most imperative and important hotspot for bits of knowledge and data. The multimedia data also provide incredible open door for the multimedia computing in the big data centric as a functioning disciplinary research field. As per current technological usage in terms of Internet or smart devices, the data manipulate in the form of digital. Massive multimedia data have been produced in the different forms like text, image, video, and audio which is shared among vast number of people. The multimedia data are real-time unstructured, heterogeneous, and multimodal. It has vast scope to mine model, learn, and analyze the service provided by multimedia. Of course, some primarily level challenges need to be addressed like analysis, storage, retrieval, and data processing. The most complicated thing in multimedia big data (MMBD) analytics is that the computer cannot understand higher level of semantics. The quality of experience (QoE) is the most evolving part of MMBD which are directly intended with storage and performance. MMBD are highly resource intensive. They often require dedicated processing capabilities in terms of graphical processing unit (GPU). An advance-level storage-related mechanism is also needed for efficient parallel processing, transmission, and presentation. Generally, non-multimedia data are always forming in text which is normally understood by machine. The multimedia data always in the form of videos are easily understood by human compared to textual data, but it is more complex task to make it understandable to machines. The MMBD performs the task by converting the human language to computer language in an efficient manner. This chapter is also introducing salient features of MMBD. The main aim of this chapter is to cover the fundamentals for MMBD computing and feasibility study. The chapter explores the technical problems and challenges to be addressed. It also focuses on methodologies and approaches that are available from the perspectives of MMBD computing life cycle. The chapter may be beneficial for the readers to understand the features, importance and application of MMBD.