Big Data Paradigm for Healthcare Sector

The digitization of world in various areas including health care domain has brought up remarkable changes. Electronic Health Records (EHRs) have emerged for maintaining and analyzing health care real data online unlike traditional paper based system to accelerate clinical environment for providing better healthcare. These digitized health care records are form of Big Data, not because of the fact they are voluminous but also they are real time, dynamic, sporadic and heterogeneous in nature. It is desirable to extract relevant information from EHRs to facilitate various stakeholders of the clinical environment. The role, scope and impact of Big Data paradigm on health care is discussed in this chapter.

Big Data from Management Perspective

Competency matters. Social media, customer transactions, mobile sensors, and feedback contents are all piled up with data. This might be unstructured and complex data in voluminous quantity, often called Big Data. However, if this Big Data is managed, it might bring competency for organizations. This chapter introduces the must-know concepts and materials for organizational managers who face Big Data. Through the chapter, Big Data is defined and its emergence over the time is reviewed. The four Vs model in Big Data literature and its link to a banking system is analyzed. The chapter concludes by making a managerial awareness concerning ethical issues in Big Data. This is of high priority in public sectors as data relies for every individual in the society.

Opportunities and Challenges of Big Data in Public Sector

Data has always been the backbone of modern society. It is generated by individuals, businesses and governments. It is used in many citizen-centric applications, including weather forecasts, controlling diseases, monitoring undesirables etc. What is changing is the source of data. Advances in technology are allowing data to be generated from any devise at any place in any form. The challenge is to “understand”, “manage” and make use of this data. It is well known that government generates unprecedented amount of data (ex: US census), the question remains: can this data be combined with technology generated data to make it useful for societal benefit. Governments and non-profits, however, work across borders making data access and integration challenging. Rules, customs and politics must be followed while sharing data across borders. Despite these challenges, big data application in public sector are beginning to emerge. This chapter discusses areas of government applications and also discusses challenges of developing such systems.

On Efficient Acquisition and Recovery Methods for Certain Types of Big Data

Big data is characterized in many circles in terms of the three V's – volume, velocity and variety. Although most of us can sense palpable opportunities presented by big data there are overwhelming challenges, at many levels, turning such data into actionable information or building entities that efficiently work together based on it. This chapter discusses ways to potentially reduce the volume and velocity aspects of certain kinds of data (with sparsity and structure), while acquiring itself. Such reduction can alleviate the challenges to some extent at all levels, especially during the storage, retrieval, communication, and analysis phases. In this chapter we will conduct a non-technical survey, bringing together ideas from some recent and current developments. We focus primarily on Compressive Sensing and sparse Fast Fourier Transform or Sparse Fourier Transform. Almost all natural signals or data streams are known to have some level of sparsity and structure that are key for these efficiencies to take place.

Blending Technology, Human Potential, and Organizational Reality

To make the deeply rooted layers of catalyzing technology and optimized modelling gain their true value for education, healthcare or other public services, it is necessary to prepare well the Big Data environment in which the Big Data will be developed, and integrate elements of it into the project approach. It is by integrating and managing these non-technical aspects of project reality that analytics will be accepted. This will enable data power to infuse the organizational processes and offer ultimately real added value. This chapter will shed light on complementary actions required on different levels. It will be analyzed how this layered effort starts by a good understanding of the different elements that contribute to the definition of an organization's Big Data ecosystem. It will be explained how this interacts with the management of expectations, needs, goals and change. Lastly, a closer look will be given at the importance of portfolio based big picture thinking.

Emerging Role of Big Data in Public Sector

Technological advancements have made it easier to collect and store data. We are generating and storing data on a nearly pervasive basis and across multiple environments including work and home. Big data, a general term for the massive amount of digital data being collected from all sorts of sources, is too large, raw, or unstructured for analysis through conventional relational database techniques. For public managers, big data represents an opportunity to infuse information and technology into the design and management of organizations, personnel, and resources. Although the business sector is leading big-data-application development, the public sector has begun to derive insight to help support decision making in real time from fast-growing in-motion data from multiple sources. This chapter explores the big-data applications associated with the public sector and provide suggestions for follower governments.

Big Data Models and the Public Sector

This chapter aims to give an overview of the wide range of Big Data approaches and technologies today. The data features of Volume, Velocity, and Variety are examined against new database technologies. It explores the complexity of data types, methodologies of storage, access and computation, current and emerging trends of data analysis, and methods of extracting value from data. It aims to address the need for clarity regarding the future of RDBMS and the newer systems. And it highlights the methods in which Actionable Insights can be built into public sector domains, such as Machine Learning, Data Mining, Predictive Analytics and others.

Big Data and National Cyber Security Intelligence

With the availability of large volumes of data and with the introduction of new tools and techniques for analysis, the security analytics landscape has changed drastically. To face the challenges posed by cyber-terrorism, espionage, cyber frauds etc. Government and law enforcing agencies need to enhance the security and intelligence analysis systems with big data technologies. Intelligence and security insight can be improved considerably by analyzing the under-leveraged data like the data from social media, emails, web logs etc. This Chapter provides an overview of the opportunities presented by Big Data to provide timely and reliable intelligence in properly addressing terrorism, crime and other threats to public security. This chapter also discusses the threats posed by Big Data to public safety and the challenges faced in implementing Big Data security solutions. Finally some of the existing initiatives by national governments using Big Data technologies to address major national challenges has been discussed.

Information Processing for Disaster Communications

This chapter presents the issues on disaster communications. The Great East Japan Earthquake on March 11th, 2011 caused severe damage to the northern coast of the main island in Japan. We report our support activities in Iwate prefecture as well as our findings and experiences. We call disaster communications in this chapter. disaster communications. Following the requests from many organizations and groups of people, we started our support for the disaster area with a few of us in the department of Software and Information Science, Iwate Prefectural University ten days after the disaster. Through our support activities we came across an interesting issue concerning collaboration with people from heterogeneous backgrounds. Disagreements and distrust happened quite easily. We found that trust plays an important role in such communications. In our chapter, we introduce disaster communications as an area for research and practice as well as our trials on the recovery phase after the emergency response.

Application of Big Data in Healthcare

This chapter investigates the application, opportunities, challenges and techniques of Big Data in healthcare. The healthcare industry is one of the most important, largest, and fastest growing industries in the world. It has historically generated large amounts of data, “Big Data”, related to patient healthcare and well-being. Big Data can transform the healthcare industry by improving operational efficiencies, improve the quality of clinical trials, and optimize healthcare spending from patients to hospital systems. However, the health care sector lags far behind compared to other industries in leveraging their data assets to improve efficiencies and make more informed decisions. Big Data entails many new challenges regarding security, privacy, legal concerns, authenticity, complexity, accuracy, and consistency. While these challenges are complex, they are also addressable. The predominant ‘Big Data' Management technologies such as MapReduce, Hadoop, STORM, and others with similar combinations or extensions should be used for effective data management in healthcare industry.