Big Data Visualization Tools and Techniques

In this chapter, a discussion is presented on the latest tools and techniques available for Big Data Visualization. These tools, techniques and methods need to be understood appropriately to analyze Big Data. Big Data is a whole new paradigm where huge sets of data are generated and analyzed based on volume, velocity and variety. Conventional data analysis methods are incapable of processing data of this dimension; hence, it is fundamentally important to be familiar with new tools and techniques capable of processing these datasets. This chapter will illustrate tools available for analysts to process and present Big Data sets in ways that can be used to make appropriate decisions. Some of these tools (e.g., Tableau, RapidMiner, R Studio, etc.) have phenomenal capabilities to visualize processed data in ways traditional tools cannot. The chapter will also aim to explain the differences between these tools and their utilities based on scenarios.

Comprehensive Analysis Methodology for Business Planning

This chapter uses the normative research methodology to review the literature for business research and analysis to search for a set of tools that can provide a comprehensive analysis methodology. The investigation of competitive advantage follows the dynamic capability of the subjective approach to the resource-based view (RBV). This view needs to establish the current position of the company's resources in the context of the surrounding environment to develop a clear understanding of the current competitive status of the organisation. The best practice of suggestions from the literature will be gathered into a proposal for a systematic research and analysis methodology governed by an information policy. A formal information project is suggested to collect and store the required information and to use a formalized set of visualization tools to provide an evolving holistic picture of the information leading to the architectural blueprint.

HFTViz: Visualization for the exploration of high frequency trading data

High Frequency Trading (HFT), mainly based on high speed infrastructure, is a significant element of the trading industry. However, trading machines generate enormous quantities of trading messages that are difficult to explore for financial researchers and traders. Visualization tools of financial data usually focus on portfolio management and the analysis of the relationships between risk and return. Beside risk-return relationship, there are other aspects that attract financial researchers like liquidity and moments of flash crashes in the market. HFT researchers can extract these aspects from HFT data since it shows every detail of the market movement. In this paper, we present HFTViz, a visualization tool designed to help financial researchers explore the HFT dataset provided by NASDAQ exchange. HFTViz provides a comprehensive dashboard aimed at facilitate HFT data exploration. HFTViz contains two sections. It first proposes an overview of the market on a specific date. After selecting desired stocks from overview visualization to investigate in detail, HFTViz also provides a detailed view of the trading messages, the trading volumes and the liquidity measures. In a case study gathering five domain experts, we illustrate the usefulness of HFTViz.

Visualisation of Perception of Experiential Activities in Business and Administration and Economy

This paper explores how to incorporate information visualization tools into qualitative studies to represent the underlying structure of knowledge. Information visualization plays a key role in many areas such as decision-making, data mining, market studies, or knowledge management. A case of experiential learning was developed for Quantitative Techniques in Business and Administration and Economy Degrees at the University of Granada, Spain. The goal is to analyze the opinion of students (n = 227) on the development of the activity through information visualization techniques. The gathered information was subjected to a categorization process to unify and homogenize the responses. After a term-clumping process, a co-word analysis using the VosViewer software is used to analyze the relationships among terms and provide the network maps. Results display the main associations and clusters of terms used when assessing the experiential activity, using qualitative techniques. In conclusion, the strengths of data visualization enabling a better understanding of data for qualitative studies are established. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Data Analysis and Visualization Tools for GeoHumanities

This review paper's aim is to assess some of the digital tools available to the social science and GeoHumanities scholarsand evaluate to what degree these digital tools can support investigation of varioussocial science and humanities questions and issues via visualizationtechniques. To this end, after an extensive analysis of the existing software packages, three software were identified and evaluated. 

COVID-19 geoviz for spatio-temporal structures detection

The spread of COVID-19 has motivated a wide interest in visualization tools to represent the pandemic’s spatio-temporal evolution. This tools usually rely on dashboard environments which depict COVID-19 data as temporal series related to different indicators (number of cases, deaths) calculated for several spatial entities at different scales (countries or regions). In these tools, diagrams (line charts or histograms) display the temporal component of data, and 2D cartographic representations display the spatial distribution of data at one moment in time. In this paper, we aim at proposing novel visualization designs in order to help medical experts to detect spatio-temporal structures such as clusters of cases and spatial axes of propagation of the epidemic, through a visual analysis of detailed COVID-19 event data. In this context, we investigate and revisit two visualizations, one based on the Growth Ring Map technique and the other based on the space-time cube applied on a spatial hexagonal grid. We assess the potential of these visualizations for the visual analysis of COVID-19 event data, through two proofs of concept using synthetic cases data and web-based prototypes. The Grow Ring Map visualization appears to facilitate the identification of clusters and propagation axes in the cases distribution, while the space-time cube appears to be suited for the identification of local temporal trends.