Climate change and disease dynamics - A big data perspective

Increase in aflatoxin exposure in two populations residing in East and West Texas, United States

Although aflatoxin (AF) exposure has not been recognized as a major problem in the United States and other developed nations, recent global climate change may have a profound impact on distribution of toxigenic fungi growth and production of AFs in grain and groundnuts. Alterations in the contamination pattern can increase human dietary exposure, and further invoke public health concerns and associated disease risks. In this study, two populations from East and West Texas, known for their high risk of liver cancer, were examined for their AF exposure at three different time periods from 2004 to 2014. Serum samples (n = 1124) were collected from participants recruited for various studies from Bexar County and Lubbock County, TX, over the span of 2004 through 2014. The exposure biomarker, serum AFB₁-lysine adduct, was analyzed by HPLC-FLD and confirmed by LC-MS. Both populations showed a significant increase in detection rate, as well as median levels of serum AFB₁-lysine adduct over time, from 2.35 to 4.34 pg/mg albumin in East Texas (2007-2014), and 0.63-3.98 pg/mg albumin in West Texas (2004-2010). This observed shift in exposure likely represents a shift in the AF contamination pattern in the State of Texas, and may warrant further studies on risk assessment for the potential etiological effects of such increased exposures.

K. P. Centralized Fog Computing Security Platform for IoT and Cloud in Healthcare System

This chapter proposes an efficient centralized secure architecture for end to end integration of IoT based healthcare system deployed in Cloud environment. The proposed platform uses Fog Computing environment to run the framework. In this chapter, health data is collected from sensors and collected sensor data are securely sent to the near edge devices. Finally, devices transfer the data to the cloud for seamless access by healthcare professionals. Security and privacy for patients' medical data are crucial for the acceptance and ubiquitous use of IoT in healthcare. The main focus of this work is to secure Authentication and Authorization of all the devices, Identifying and Tracking the devices deployed in the system, Locating and tracking of mobile devices, new things deployment and connection to existing system, Communication among the devices and data transfer between remote healthcare systems. The proposed system uses asynchronous communication between the applications and data servers deployed in the cloud environment.

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.

Architecture for Big Data Storage in Different Cloud Deployment Models

Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

Big Data Security Framework for Distributed Cloud Data Centers

The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment.