Recommender System in the Context of Big Data

The increasing usage of e-commerce website has led to the emergence of Recommender System (RS) with the aim of personalizing the web content for each user. One of the successful techniques of RSs is Collaborative Filtering (CF) which makes recommendations for users based on what other like-mind users had preferred. However, as the world enter Big Data era, CF has faced some challenges such as: scalability, sparsity and cold start. Thus, new approaches that overcome the existing problems have been studied such as Singular Value Decomposition (SVD). This chapter surveys the literature of RSs, reviews the current state of RSs with the main concerns surrounding them due to Big Data, investigates thoroughly SVD and provides an implementation to it using Apache Hadoop and Spark. This is intended to validate the applicability of, existing contributions to the field of, SVD-based RSs as well as validated the effectiveness of Hadoop and spark in developing large-scale systems. The results proved the scalability of SVD-based RS and its applicability to Big Data.

Vehicle to Cloud

The population of humanity has become more than seven billion. Daily used devices, machines, and equipment, are also increasing quicker than the human population. The number of mobile devices in use like phones, tablets and IoT devices already passed the two billion barrier and even more than one billion as vehicles are also on the roads. Combining these two will make the one of the biggest Big Data Environment about the daily life of human beings after the use of internet and social applications. For the newly manufactured vehicles, internet operated entertainment and information Systems are becoming a standard equipment delivering such an information to the manufacturers but most of the current vehicles do not have a system like that. This chapter explains the combined version of IoT and vehicles to create a V2C vehicle to cloud system that will create the big data for environmental sustainability, energy and traffic management by different technical and political views and aspects.

Using Big Data to Improve the Educational Infrastructure and Learning Paradigm

In higher education, the demand for improved information in relation to educational and learning outcomes is greater than ever before. Leveraging technology, new models of education have emerged that are not only improving modes of lecture delivery and information retention, but also generating huge amounts of data. This data is potentially a gold mine that needs to be explored to uncover patterns associated with student behavior and how information is processed, retained and used by the students. This chapter proposes a generic model that uses the techniques of educational data mining to explore and analyze Big Data being generated by the education sector. This chapter also examines the various questions that can be answered using educational data mining methods and how the discovered patterns can be used to enrich the learning experience of a student as well as help teachers make pedagogical decisions.

Big Data Management in Financial Services

Many organizations are beginning to feel frustrated by the limited progress of their companies with the application of new technologies to date. At the same time is convenient to remember that this is something that always happens when new technologies are introduced, companies must accept the challenge of self-assessment and measure the barriers that threaten to prevent them from reaching to get the maximum potential derived from big data and analytics In financial services, there are significant opportunities to obtain benefits by applying technologies and methodologies of big data and analytics. Regulatory pressure has forced many businesses, particularly in banking, to invest in areas such as risk management, compliance and operations. This has accelerated the trend toward enterprise data management.

Point Cloud Manager

Recent advances in acquisition technologies, such as LIDAR and photogrammetry, have brought back to popularity 3D point clouds in a lot of fields of application of Computer Graphics: Civil Engineering, Architecture, Topography, etc. These acquisition systems are producing an unprecedented amount of geometric data with additional attached information, resulting in huge datasets whose processing and storage requirements exceed usual approaches, presenting new challenges that can be addressed from a Big Data perspective by applying High Performance Computing and Computer Graphics techniques. This chapter presents a series of applications built on top of Point Cloud Manager (PCM), a middleware that provides an abstraction for point clouds with arbitrary attached data and makes it easy to perform out-of-core operations on them on commodity CPUs and GPUs. Hence, different kinds of real world applications are tackled, showing both real-time and offline examples, and render-oriented and computation-related operations as well.

Possibilities, Impediments, and Challenges for Network Security in Big Data

With the time, Big Data became the core competitive factor for enterprises to develop and grow. Some enterprises such as, information industrial enterprises will put more focus on the technology or product innovation for solving the challenges of big data, i.e., capture, storage, analysis and application. Enterprises like, manufacturing, banking and other enterprises will also benefit from analysis and manage big data, and be provided more opportunities for management innovation, strategy innovation or marketing innovation. High performance network capacity provides the backbone for high end computing systems. These high end computing systems plays vital role in Big Data. Persistent and Sophisticated targeted network attacks have challenged today's enterprise security teams. By exploring each aspect of high performance network capacity, the major objective of this book chapter is to present fundamental theoretical aspects in analytical way with deep focus on possibilities, impediments and challenges for network security in Big Data.

The Challenges of Data Cleansing with Data Warehouses

Data cleansing is a long standing problem which every organisation that incorporates a form of data processing or data mining must undertake. It is essential in improving the quality and reliability of data. This paper presents the necessary methods needed to process data at a high quality. It also classifies common problems which organisations face when cleansing data from a source or multiple sources while evaluating methods which aid in this process. The different challenges faced at schema-level and instance-level are also outlined and how they can be overcome. Currently there are tools which provide data cleansing, but are limited due to the uniqueness of every data source and data warehouse. Outlined are the limitations of these tools and how human interaction (self-programming) may be needed to ensure vital data is not lost. We also discuss the importance of maintaining and removing data which has been stored for several years and may no longer have any value.

Big Data Mining

Big data is a term used to describe very large amount of structured, semi-structured and unstructured data that is difficult to process using the traditional processing techniques. It is now expanding in all science and engineering domains. The key attributes of big data are volume, velocity, variety, validity, veracity, value, and visibility. In today's world, everyone is using social networking applications like Facebook, Twitter, YouTube, etc. These applications allow the users to create the contents for free of cost and it becomes huge volume of web data. These data are important in the competitive business world for making decisions. In this context, big data mining plays a major role which is different from the traditional data mining. The process of extracting useful information from large datasets or streams of data, due to its volume, velocity, variety, validity, veracity, value and visibility is termed as Big Data Mining.

Big Data Analysis

The data management industry has matured over the last three decades, primarily based on Relational Data Base Management Systems (RDBMS) technology. The amount of data collected and analyzed in enterprises has increased several folds in volume, variety and velocity of generation and consumption, organizations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. The data-driven world has the potential to improve the efficiencies of enterprises and improve the quality of our lives. There are a number of challenges that must be addressed to allow us to exploit the full potential of Big Data. This article highlights the key technical challenges of Big Data.

Big Data

With the advent of Internet of Things (IoT) and Web 2.0 technologies, there has been a tremendous growth in the amount of data generated. This chapter emphasizes on the need for big data, technological advancements, tools and techniques being used to process big data. Technological improvements and limitations of existing storage techniques are also presented. Since the traditional technologies like Relational Database Management System (RDBMS) have their own limitations to handle big data, new technologies have been developed to handle them and to derive useful insights. This chapter presents an overview of big data analytics, its application, advantages, and limitations. Few research issues and future directions are presented in this chapter.