Learning Management System 2.0

The recent adoption of cloud computing, Web 2.0 (web as a platform), and Big Data technologies have become the main driver of the paradigm shift. For higher education, choosing the right platform for a next generation of Learning Management System (LMS) namely LMS 2.0 is becoming more important than choosing a tool in the new paradigm. This chapter discusses factors for higher institution in determining a future direction for its LMS to take advantage of pervasive knowledge management, efficiency and effectiveness of operations. Literature studies have deployed for this study to portray the state of future LMS initiative. We found that the trends of cloud computing and big data will be predominant factor in viewing future LMS adoption and implementation. LMS 2.0 can be a solution to make learning systems in a higher education is flexible in terms of resources adoption, quality of learning, knowledge management, and implementation.

Computational Systems Biology Perspective on Tuberculosis in Big Data Era

The major concern in this chapter is to understand the need of system biology in prediction models in studying tuberculosis infection in the big data era. The overall complexity of biological phenomenon, such as biochemical, biophysical, and other molecular processes, within pathogen as well as their interaction with host is studied through system biology approaches. First, consideration is given to the necessity of prediction models integrating system biology approaches and later on for their replacement and refinement using high throughput data. Various ongoing projects, consortium, databases, and research groups involved in tuberculosis eradication are also discussed. This chapter provides a brief account of TB predictive models and their importance in system biology to study tuberculosis and host-pathogen interactions. This chapter also addresses big data resources and applications, data management, limitations, challenges, solutions, and future directions.

Big Data and Data Analytics Research

The Big Data and Data Analytics is a brand new paradigm, for the integration of Internet Technology in the human and machine context. For the first time in the history of the human mankind we are able to transforming raw data that are massively produced by humans and machines in to knowledge and wisdom capable of supporting smart decision making, innovative services, new business models, innovation, and entrepreneurship. For the Web Science research, this is a new methodological and technological spectrum of advanced methods, frameworks and functionalities never experienced in the past. At the same moment communities out of web science need to realize the potential of this new paradigm with the support of new sound business models and a critical shift in the perception of decision making. In this short visioning article, the authors are analyzing the main aspects of Big Data and Data Analytics Research and they provide their own metaphor for the next years. A number of research directions are outlined as well as a new roadmap towards the evolution of Big Data to Smart Decisions and Cognitive Computing. The authors do hope that the readers would like to react and to propose their own value propositions for the domain initiating a scientific dialogue beyond self-fulfilled expectations.

Impact of EU Data Protection Laws on Cloud Computing

The global nature of cloud computing has resulted in emerging challenges, such as clashes between legal systems, cultural differences, and business practice norms: cloud-computing is at the forefront of recognising, and “smoothing over,” emergent differences between nation states as we move towards a more globally connected world. This chapter uses the emergent differences over regulation governing data protection; as the world becomes more interconnected, we are likely to see more examples of technology practices and models sweeping around the globe, and raising further areas for clashes between nations and regions, much like the fault lines between tectonic plates. This chapter provides contribution by capturing some emergent “fault lines” in an in-depth case study comparing the evolving EU directives covering data protection and how they relate to non-EU data protection legal systems. This provides the foundations to consider cloud-computing challenges, inform policymakers in measures to resolve “clashes,” and in informing researchers investigating other global technology phenomena.

Virtualization Evolution

This chapter discusses the evolution of the cloud computing paradigm and its applicability in various sections of the computing and networking/telecommunications industry, such as the cloud networking, the cloud offloading, and the network function virtualization. The new heterogeneous virtualized ecosystem that is formulated creates new needs and challenges for management and administration at the network part. For this purpose, the approach of Software-Defined Networking is discussed and its future perspectives are further analyzed.

A Hybrid Cloud Model for Cloud Adoption by Multinational Enterprises

As a new IT paradigm for users, cloud computing has the potential to transform the way that IT resources are utilized and consumed. Many multinational enterprises (MNEs) are interested in cloud computing but do not know how to adopt and implement cloud computing in their enterprise settings. In an effort to help MNEs understand cloud computing and develop successful enterprise adoption strategies for cloud computing, the authors propose a hybrid cloud model for MNEs and illustrate the utility of this model by using two case studies. Insights for adopting and implementing this model in international settings are provided as well.

Semantic Technologies and Big Data Analytics for Cyber Defence

The Governments, military forces and other organisations responsible for cybersecurity deal with vast amounts of data that has to be understood in order to lead to intelligent decision making. Due to the vast amounts of information pertinent to cybersecurity, automation is required for processing and decision making, specifically to present advance warning of possible threats. The ability to detect patterns in vast data sets, and being able to understanding the significance of detected patterns are essential in the cyber defence domain. Big data technologies supported by semantic technologies can improve cybersecurity, and thus cyber defence by providing support for the processing and understanding of the huge amounts of information in the cyber environment. The term big data analytics refers to advanced analytic techniques such as machine learning, predictive analysis, and other intelligent processing techniques applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends and other useful information. Semantic technologies is a knowledge representation paradigm where the meaning of data is encoded separately from the data itself. The use of semantic technologies such as logic-based systems to support decision making is becoming increasingly popular. However, most automated systems are currently based on syntactic rules. These rules are generally not sophisticated enough to deal with the complexity of decisions required to be made. The incorporation of semantic information allows for increased understanding and sophistication in cyber defence systems. This paper argues that both big data analytics and semantic technologies are necessary to provide counter measures against cyber threats. An overview of the use of semantic technologies and big data technologies in cyber defence is provided, and important areas for future research in the combined domains are discussed.

A New Approach in Cloud Computing User Authentication

Cloud computing provides the full scalability, reliability, high performance and relatively low cost feasible solution as compared to dedicated infrastructure. These features make cloud computing more attractive to users and intruders. It needs more and complex security measures to protect user privacy and data centers. The main concern in this chapter is security, privacy and trust. This chapter will give a discussion and a suggestion for using cloud computing to preserve security and privacy. The malicious hacker and other threats are considering the major cause of leaking security of the personal cloud due to centralized location and remote accesses to the cloud. According to attacks, a centralized location can be easier target rather than several goals and remote access is insecure technologies which offer a boundary of options for attackers to infiltrate enterprises. The biggest concern is attackers that will use the remote connection as a jumping point to get deeper into an organization.

Data Science and Big Data Analytics in Financial Services

The chapter discusses how Financial Services organizations can take advantage of Big Data analysis for disruptive innovation through examination of a case study in the financial services industry. Popular tools for Big Data Analysis are discussed and the challenges of big data are explored as well as how these challenges can be met. The work of Hayes-Roth in Valued Information at the Right Time (VIRT) and how it applies to the case study is examined. Boyd's model of Observe, Orient, Decide, and Act (OODA) is explained in relation to disruptive innovation in financial services. Future trends in big data analysis in the financial services domain are explored.

Analytics-as-a-Service (AaaS)

Big Data Analytics is a major branch of data science where the huge amount raw data is processed to get insight for relevant business processes. Integration of big data, its analytics along with Service Oriented Architecture (SOA) is need of the hour, such integration shall render reusability and scalability to various business processes. This chapter explains the concept of Big Data and Big Data Analytics at its implementation level. The Chapter further describes Hadoop and its technologies which are one of the popular frameworks for Big Data Analytics and envisage integrating SOA with relevant case studies. The chapter demonstrates the SOA integration with Big Data through, two case studies of two different scenarios are incorporated that integrates real world implementation with theory and enables better understanding of the industrial level processes and practices.