Big Data Analytics

Advances in recent hardware technology have permitted to document transactions and other pieces of information of everyday life at an express pace. In addition of speed up and storage capacity, real-life perceptions tend to transform over time. However, there are so much prospective and highly functional values unseen in the vast volume of data. For this kind of applications conventional data mining is not suitable, so they should be tuned and changed or designed with new algorithms. Big data computing is inflowing to the category of most hopeful technologies that shows the way to new ways of thinking and decision making. This epoch of big data helps users to take benefit out of all available data to gain more precise systematic results or determine latent information, and then make best possible decisions. Depiction from a broad set of workloads, the author establishes a set of classifying measures based on the storage architecture, processing types, processing techniques and the tools and technologies used.

Cryptography in Big Data Security

Information security is a prime goal for every individual and organization. The travelling from client to cloud server can be prone to security issues. The big data storages are available through cloud computing system to facilitate mobile client. The information security can be provided to mobile client and cloud technology with the help of integrated parallel and distributed encryption and decryption mechanism. The traditional technologies include the plaintext stored across cloud and can be prone to security issues. The solution provided by applying the encrypted data upload and encrypted search. The clouds can work in collaboration; therefore, the encryption can also be done in collaboration. Some part of encryption handle by client and other part handled by cloud system. This chapter presents the security scenario of different security algorithms and the concept of mobile and cloud computing. This chapter precisely defines the security features of existing cloud and big data system and provides the new framework that helps to improve the data security over cloud computing and big data security system.

Bio-Inspired Techniques in Human-Computer Interface for Control of Assistive Devices

Most of the assistive devices are of user contact based control like body-powered prosthetic hand, joystick control of wheelchair, sip-and-puff, etc. and have a limited number of control movements. The performance of these assistive devices improves using bio-signals/gesture based control embedded in the processor. Gesture based control is widely used in wheelchair navigation control, communication with external world for neuromuscular impaired subjects. On the other hand, bio-signals are used widely in prosthetic devices, wheelchair control, orthotic devices, etc. with pattern recognition based control strategy. The choice and number of features used in pattern recognition for accurate control of assistive device is crucial. Further, these features performance also varies with the classifier. The appropriate selection of combination of pattern recognition will enhance the accuracy. This chapter focuses on bio-inspired techniques in selection of features and classification for the pattern recognition based assistive device control.

Case Studies in Amalgamation of Deep Learning and Big Data

Deep learning is now more popular research domain in machine learning and pattern recognition in the world. It is widely success in the far-reaching area of applications such as Speech recognition, Computer vision, Natural language processing and Reinforcement learning. With the absolute amount of data accessible nowadays, big data brings chances and transformative possible for several sectors, on the other hand, it also performs on the unpredicted defies to connecting data and information. The size of the data is getting larger, and deep learning is imminent to play a vital role in big data predictive analytics solutions. In this paper, we make available a brief outline of deep learning and focus recent research efforts and the challenges in the fields of science, medical and water resource system.

Prediction of Cancer Disease Using Classification Techniques in Map Reduce Programming Model

As the volume of data is increasing with time the primary issue is how to store and process such data and get useful information out of it. Analysis of classification algorithms and MapReduce programming model has led to the conclusion that the distributed file system and parallel computing attributes of MapReduce are good for designing classifier model. The major reason for it is parallel processing of data in which data is divided and processed in parallel and the output from each is reduced further for a single output. In this paper, we are going to study how to use MapReduce model to build classifier model. We are using cancer dataset to predict if a person has cancer or not by using Naive Bayes and KNN classification algorithms. We have compared them on the basis on computational time and the factors like sensitivity, specificity, and accuracy. In the end, we would be able to compare these two algorithms and tell which one works better on MapReduce programming model

A Survey of Big Data Analytics Using Machine Learning Algorithms

Big data is information management system through the integration of various traditional data techniques. Big data usually contains high volume of personal and authenticated information which makes privacy as a major concern. To provide security and effective processing of collected data various techniques are evolved. Machine Learning (ML) is considered as one of the data technology which handles one of the central and hidden parts of collected data. Same like ML algorithm Deep Learning (DL) algorithm learn program automatically from the data it is considered to enhance the performance and security of the collected massive data. This paper reviewed security issues in big data and evaluated the performance of ML and DL in a critical environment. At first, this paper reviewed about the ML and DL algorithm. Next, the study focuses towards issues and challenges of ML and their remedies. Following, the study continues to investigate DL concepts in big data. At last, the study figures out methods adopted in recent research trends and conclude with a future scope.

Privacy Preserving Big Data Publishing

Data privacy plays a noteworthy part in today's digital world where information is gathered at exceptional rates from different sources. Privacy preserving data publishing refers to the process of publishing personal data without questioning the privacy of individuals in any manner. A variety of approaches have been devised to forfend consumer privacy by applying traditional anonymization mechanisms. But these mechanisms are not well suited for Big Data, as the data which is generated nowadays is not just structured in manner. The data which is generated at very high velocities from various sources includes unstructured and semi-structured information, and thus becomes very difficult to process using traditional mechanisms. This chapter focuses on the various challenges with Big Data, PPDM and PPDP techniques for Big Data and how well it can be scaled for processing both historical and real-time data together using Lambda architecture. A distributed framework for privacy preservation in Big Data by combining Natural language processing techniques is also proposed in this chapter.

Human-Computer Interaction With Big Data Analytics

Big Data has been playing a vital role in almost all environments such as healthcare, education, business organizations and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Big data consists of huge unstructured data that require advance real-time analysis. Thus, nowadays many of the researchers are interested in developing advance technologies and algorithms to solve the issues when dealing with big data. Big Data has gained much attention from many private organizations, public sector and research institutes. This chapter provides an overview of the state-of-the-art algorithms for processing big data, as well as the characteristics, applications, opportunities and challenges of big data systems. This chapter also presents the challenges and issues in human computer interaction with big data analytics.