In today's world where everything is recorded digitally, right from our web surfing patterns to our medical records, we are generating and processing petabytes of data every day. Big data will be transformative in every sphere of life. But just to process and analyze those data is not enough, human brain tends to find pattern more efficiently when data is represented visually. Data Visualization and Analytics plays important role in decision making in various sectors. It also leads to new opportunities in the visualization domain representing the innovative ideation for solving the big-data problem via visual means. It is quite a challenge to visualize such a mammoth amount of data in real time or in static form. In this paper, we discuss why big data visualization is of utmost importance, what are the challenges related to it and review some big data visualization tools.

Present research work on the Internet of Things and Fog computing has taken hype due to the low cost of building them by optimized sensors. Wireless sensor devices play a vital role in developing IoT sensing infrastructure. These devices are connected to form a network known as the WSN assisted IoT network. The number of things is verdant in IoT creates an immense energy need. So, efficient energy utilization is required to alter the green IoT environment. These sensor devices are having limited power and computational capabilities. We can use energy-efficient data routing protocols as data transferring with these low powered sensors is very challenging. One may use the sensors for more time in sensing the environment and sending the information. This proposed protocol deals with a reliable routing protocol for IoT sensing Infrastructure. Initially, a rendezvous region was created in the middle of the network area. Clustering and multipath technique are used because it reduces energy consumption and increases reliability. The proposed protocol is simulated within Castalia simulator for attaining the performance beneath different characteristics like packet delivery ratio, the average energy consumption, end to end delay, and network lifetime. It is observed that the suggested technique is useful in energy consumption and helps in boosting the IoT infrastructure network lifetime.

Cloud computing provides variety of services with the growth of their offerings. Due to efficient services, it faces numerous challenges. It is based on virtualization, which provides users a plethora computing resources by internet without managing any infrastructure of Virtual Machine (VM). With network virtualization, Virtual Machine Manager (VMM) gives isolation among different VMs. But, sometimes the levels of abstraction involved in virtualization have been reducing the workload performance which is also a concern when implementing virtualization to the Cloud computing domain. In this paper, it has been explored how the vendors in cloud environment are using Containers for hosting their applications and also the performance of VM deployments. It also compares VM and Linux Containers with respect to the quality of service, network performance and security evaluation.

In the big data world, recommendation system is becoming growingly popular. In this work Apache Spark is used to demonstrate an efficient parallel implementation of a new hybrid algorithm for User Oriented Collaborative Filtering method. Dimensionality reduction techniques like Alternating Least Square and Clustering techniques like K-Means are used in order to overcome the limitations of Collaborative Filtering such as data Sparsity and Scalability. We also tried to alleviate the cold start problem of Collaborative Filtering by correlating the users to products through features (tags).

Wireless Sensors are an important component to develop the Internet of Things (IoT) Sensing infrastructure. There are enormous numbers of sensors connected with each other to form a network (well known as wireless sensor networks) to complete IoT Infrastructure. These deployed wireless sensors are with limited energy and processing capabilities. IoT infrastructure becomes a key factor to building cyber-physical-social networking infrastructure, where all these sensing devices transmit data towards the cloud data center. Data routing towards cloud data center using such low power sensor is still a challenging task. In order to prolong the lifetime of the IoT sensing infrastructure and building scalable cyber infrastructure, there is the requirement of sensing optimization and energy efficient data routing. Towards addressing these issues of IoT sensing, this paper proposes a novel rendezvous data routing protocol for low power sensors. The proposed method divides the sensing area into a number of clusters to lessen the energy consumption with data accumulation and aggregation. As a result, there will be less amount of data stream to the network. Another major reason to select cluster-based data routing is to reduce the control overhead. Finally, the simulation of the proposed method is done in the Castalia simulator to observe the performance. It has been concluded that the proposed method is energy efficient and it prolongs the networks lifetime for scalable IoT infrastructure.