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The major component of agriculture production includes the type of seed, soil, climatic conditions, irrigation pattern, fertilizer, weed control, and technology used. Soil is one of the prime elements in modern times for agriculture. Soil is also one of the primary and important factors for crop production. The available soil nutrient status and external applications of fertilizers decide the growth of crop productivity (Annoymous, 2017). The upcoming research question that needs to be addressed is What is the application of soil data on soil health management for sustaining agriculture? Driven by the need, the aim of the present study is (a) to explore the soil parameters of a district, (b) compare the values with the standards, and (c) pave a way for mapping the crops with suitability of soil health. This study will not only be beneficial for the district to take appropriate steps to improve the soil health but also would help in understanding the causal relationship among soil health parameters, cropping pattern, and crop productivity.

A major chunk of rural people live on agriculture and other allied activities viz animal husbandry, dairying and fisheries, etc. Rural development constitutes of lot of big data related to rural employment which has driven this study to address a research question that what is the application of big data in rural development with special reference to the world’s largest public works and wage employment generating poverty alleviation program – Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA)? The concepts of MGNREGA are novel and innovative though the program continues to suffer from various rigidities depicted from the data. This drives us to the objectives of our research. The objective of the study is to explore literature and big data on rural development with special reference to MGNREGA, explore the upcoming challenges in rural employment with special reference to MGNREGA, identify gaps in existing literature and pave out future research direction. The present study paves various ways for future research directions for academicians, researchers and policy maker.

This chapter has been seminal work of Dr K.S.S. Iyer, which has taken time to develop, for over the last 56 years to be presented here. The method in advance predictive analytics has developed, from his several other applications, in predictive modeling by using the stochastic point process technique. In the chapter on advance predictive analytics, Dr Iyer is collecting his approaches and generalizing it in this chapter. In this chapter, two of the techniques of stochastic point process known as Product Density and Random point process used in modelling problems in High energy particles and cancer, are redefined to suit problems currently in demand in IoT and customer equity in marketing (Iyer, Patil, & Chetlapalli, 2014b). This formulation arises from these techniques being used in different fields like energy requirement in Internet of Things (IoT) devices, growth of cancer cells, cosmic rays’ study, to customer equity and many more approaches.

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.