Visualization of Big Data Sets Using Computer Graphics

This chapter discusses some possible ways of how professionals, researchers and users representing various knowledge domains are collecting and visualizing big data sets. First it describes communication through senses as a basis for visualization techniques, computational solutions for enhancing senses and ways of enhancing senses by technology. The next part discusses ideas behind visualization of data sets and ponders what is and what not visualization is. Further discussion relates to data visualization through art as visual solutions of science and mathematics related problems, documentation objects and events, and a testimony to thoughts, knowledge and meaning. Learning and teaching through data visualization is the concluding theme of the chapter. Edoardo L'Astorina provides visual analysis of best practices in visualization: An overlay of Google Maps that showed all the arrival times - in real time - of all the buses in your area based on your location and visual representation of all the Tweets in the world about TfL (Transport for London) tube lines to predict disruptions.

The Image as Big Data Toolkit

This chapter is a brief introduction to the Image As Big Data Toolkit (IABDT), a Java-based open source framework for performing a variety of distributed image processing and analysis tasks. IABDT has been developed over the last two years in response to the rapid evolution of Big Data architectures and technologies, distributed and image processing systems. This chapter presents an architecture for image analytics that uses Big Data storage and compression methods. A sample implementation of our image analytic architecture called the Image as Big Data Toolkit (IABDT) addresses some of the most frequent challenges experienced by the image analytics developer. Baseline applications developed with IABDT, status of the toolkit and directions for future extension with emphasis on image display, presentation, and reporting case studies are discussed to motivate our design and technology stack choices. Sample applications built using IABDT, as well as future development plans for IABDT are discussed.

Big Data Storage for the Modeling of Historical Time Series Solar Irradiations

This chapter proposes Big Data Analytics for the sizing and locating of solar photovoltaic farms to reduce the total energy loss in distribution networks. The Big Data Analytics, which uses the advance statistical and computational tools for the handling of large data sets, has been adopted for modeling the 15 years of solar weather data. Total Power Loss Index (TPLI) is formulated as the main objective function for the optimization problem and meanwhile bus voltage deviations and penetrations of the PV farms are calculated. To solve the optimization problem, this study adopts the Mixed Integer Optimization using Genetic Algorithm (MIOGA) technique. By considering different time varying voltage dependent load models, the proposed algorithm is applied on IEEE 33 bus and IEEE 69 bus test distribution networks and optimum results are acquired. From the results, it is revealed that compared to single PV farm, the integration of two PV farms reduced more energy loss and reduced the total size of PV farms. Big Data Analytics is found very effective for the storing, handling, processing and the visualizing of the weather Big Data.

High Performance Storage for Big Data Analytics and Visualization

Scientific Big Data being gathered at exascale needs to be stored, retrieved and manipulated. The storage stack for scientific Big Data includes a file system at the system level for physical organization of the data, and a file format and input/output (I/O) system at the application level for logical organization of the data; both of them of high-performance variety for exascale. The high-performance file system is designed with concurrent access, high-speed transmission and fault tolerance characteristics. High-performance file formats and I/O are designed to allow parallel and distributed applications with easy and fast access to Big Data. These specialized file formats make it easier to store and access Big Data for scientific visualization and predictive analytics. This chapter provides a brief review of the characteristics of high-performance file systems such as Lustre and GPFS, and high-performance file formats such as HDF5, NetCDF, MPI-IO, and HDFS.

Big Data Tools for Computing on Clouds and Grids

The number of tools available for Big Data processing have grown exponentially as cloud providers have introduced solutions for businesses that have little or no money for capital expenditures. The chapter starts by discussing historic data tools and the evolution to those of today. With Cloud Computing, the need for upfront costs has been removed, costs are continuing to fall and costs can be negotiated. This chapter reviews the current types of Big Data tools, and how they evolved. To give readers an idea of costs, the chapter shows example costs (in today's market) for a sampling of the tools and relative cost comparisons of the other tools like the Grid tools used by the government, scientific communities and academic communities. Readers will take away from this chapter an understanding of what tools work best for several scenarios and how to select cost effective tools (even tools that are unknown today).

Big Data Analytics and Visualization of Performance of Stock Exchange Companies Based on Balanced Scorecard Indicators

One of the major concerns of managers at stock exchange companies is the maximum and efficient use of limited resources to meet the unlimited users' demands and in particular, investors and company owners. Achieving this goal gets more complex everyday due to the changing environment and multidimensional economic pressures. It is necessary that managers know the process of effective data oriented measurement in every single aspect of a successful business. One of the most accredited and useful methods for evaluating performance is the Balanced Scorecard (BSC). In this chapter, researchers have focused on providing a model that evaluates the performance of companies based on a combination of BSC indicators and big data analytics and algorithms. The chapter's purpose is to indicate which analytics algorithms are most appropriate for each BSC indicator based on a deep review of broad literature as a measurement guideline for future researchers and practitioners.

The Artist's Move

Dance and technology have been partners from an early age. In 1892 Loie Fuller recognized the potential in the latest theater lighting technologies that would enable her to creatively explore her dance and dance performance. Like Fuller, as technologies emerged to the world at large, dance artists began to explore the effect such technologies would have on their art. Eventually, explorations of dance and technology focused on how computers contributed to the performance of dance. This chapter will review the history of dance and technology culminating into a discussion of the next evolution of technology in the discipline of dance, the potential computational thinking and Big Data bring to the visualization of the creative process. Particular emphasis will focus on how the work, Synchronous Objects One Flat Thing reproduced, exemplifies the convergence of dance, technology, Big Data and visualization.

Big Data and Its Role in Facilitating the Visualization of Financial Analytics

The business use of data analytics is growing rapidly in the accounting environment. Similar to many new systems that involve accounting information, data analytics has fundamentally changed task based processes particularly those tasks that provide inference, prediction and assurance to decision makers. Big Data analytics is the process of inspecting, cleaning, transforming, and modeling Big Data to discover and communicate useful information and patterns, suggest conclusions, and support decision making. Big Data now pervades every sector and function of the global economy. These essays focus on the uses and challenges of Big Data in accounting (measurement) and auditing (assurance). The objective of this chapter is to examine how Big Data analytics will impact the accounting and auditing environment. This is important to practitioners as well as academics because they will be using data analytics in accounting and auditing tasks and will need to have an in-depth familiarity with financial analytics to effectively accomplish these tasks and make effective and efficient decisions.

Mining Multimodal Big Data

The last decade has witnessed exponential growth of data particularly in the fields of biomedicine, unstructured text processing and signal processing. There exist instances of data depicting simultaneous interactions amongst more than two types of entities. Such data are not readily amenable to matrix representation as matrices can show interactions between only two types of entities at a time. Tensors are multimodal extensions of matrices (a matrix can be thought of as 2-mode tensor), and tensor factorizations (decompositions) are multiway generalizations of matrix factorizations. This chapter provides an overview of tensor factorization methods as well as a literature review of selected applications in areas that are currently experiencing exponential data growth and likely of interest to a broad audience.

Introduction to Smart City and Agricultural Revolution

In this chapter, a discussion is presented about what Big Data and Internet of Things (IoT) really is and what intricacies are used while building big data and internet of things. Further Big Data and Internet of Things have been used for building an application used for Smart City & Agriculture. A smart city is an urban development vision to integrate multiple information and communication technology (ICT) solutions. Smart city's goal is to improve quality of life with technology to improve the efficiency of services and meet residents' needs. Smart agriculture approach is to develop, transform and reorient agricultural development under new realities of climate change. It increases productivity enhances resilience (adaptation), reduces mitigation with achievement of national food security and development goals. This chapter includes detailed discussion on Smart City and Smart Agriculture along with planning, designing as well as various approaches used to build and implement them effectively.