n the past decade, the analysis of data has faced the challenge of dealing with very large and complex datasets and the real-time generation of data. Technologies to store and access these complex and large datasets are in place. However, robust and scalable analysis technologies are needed to extract meaningful information from these datasets. The research field of Information Visualization and Visual Data Analytics addresses this need. Information visualization and data mining are often used complementary to each other. Their common goal is the extraction of meaningful information from complex and possibly large data. However, though data mining focuses on the usage of silicon hardware, visualization techniques also aim to access the powerful image-processing capabilities of the human brain. This article highlights the research on data visualization and visual analytics techniques. Furthermore, we highlight existing visual analytics techniques, systems, and applications including a perspective on the field from the chemical process industry

In the era of big data, the ability to derive meaningful insights from complex datasets is critical across industries. Data visualization and analytics serve as fundamental tools for transforming raw data into actionable intelligence. This chapter explores the principles, techniques, and tools that drive effective data analysis and communication. It begins with an overview of different data types and the foundational principles of clear and accurate visualization. Various methods of data representation—ranging from basic charts to interactive dashboards—are examined alongside popular tools such as Tableau, Power BI, Python libraries, and R. The chapter also introduces core analytics concepts including descriptive, diagnostic, predictive, and prescriptive approaches, emphasizing the importance of data preparation, statistical techniques, and machine learning in driving decisions. Ethical considerations, such as avoiding misleading visuals and maintaining data privacy, are also discussed. Through real-world applications and case studies, readers will gain practical knowledge on how to integrate data visualization with analytics to uncover patterns, forecast trends, and support data-driven decision-making in a visually compelling and ethically sound manner.

© 2014 by IGI Global. All rights reserved. Highlighting has been known as a basic viewing control mechanism in computer graphics and visualization for guiding users' attention in reading diagrams, images, graphs, and digital texts. Due to the rapid development of theory and practice in information visualization and visual analytics, the role of 'highlighting' in computer graphics has been extended from just acting as a viewing control to being part of an interaction control and a visual recommendation mechanism that is important in modern information visualization and visual analytics. In this chapter, the authors present a brief literature review. They try to assign the word 'highlighting' a contemporary definition and attempt to give a formal summarization and classification of the existing and potential 'highlighting' methods that are to be applied in Information Visualization, Visual Analytics, and Knowledge Visualization. We also propose a new three-layer model of 'highlighting' and discuss the responsibilities of each layer accordingly

A large amount of land-use, environment, socio-economic, energy and transport data is generated in cities. An integrated perspective of managing and analysing such big data can answer a number of science, policy, planning, governance and business questions and support decision making in enabling a smarter environment. This paper presents a theoretical and experimental perspective on the smart cities focused big data management and analysis by proposing a cloud-based analytics service. A prototype has been designed and developed to demonstrate the effectiveness of the analytics service for big data analysis. The prototype has been implemented using Hadoop and Spark and the results are compared. The service analyses the Bristol Open data by identifying correlations between selected urban environment indicators. Experiments are performed using Hadoop and Spark and results are presented in this paper. The data pertaining to quality of life mainly crime and safety & economy and employment was analysed from the data catalogue to measure the indicators spread over years to assess positive and negative trends

The need to understand and track files (and inherently, data) in cloud computing systems is in high demand. Over the past years, the use of logs and data representation using graphs have become the main method for tracking and relating information to the cloud users. While it is still in use, tracking and relating information with ‘Data Provenance’ (i.e. series of chronicles and the derivation history of data on meta-data) is the new trend for cloud users. However, there is still much room for improving representation of data activities in cloud systems for end-users. In this thesis, we propose “UVisP (User-centric Visualization of Data Provenance with Gestalt)”, a novel user-centric visualization technique for data provenance. This technique aims to facilitate the missing link between data movements in cloud computing environments and the end-users’ uncertain queries over their files’ security and life cycle within cloud systems. The proof of concept for the UVisP technique integrates D3 (an open-source visualization API) with Gestalts’ theory of perception to provide a range of user-centric visualizations. UVisP allows users to transform and visualize provenance (logs) with implicit prior knowledge of ‘Gestalts’ theory of perception.’ We presented the initial development of the UVisP technique and our results show that the integration of Gestalt and the existence of ‘perceptual key(s)’ in provenance visualization allows end-users to enhance their visualizing capabilities, extract useful knowledge and understand the visualizations better. This technique also enables end-users to develop certain methods and preferences when sighting different visualizations. For example, having the prior knowledge of Gestalt’s theory of perception and integrated with the types of visualizations offers the user-centric experience when using different visualizations. We also present significant future work that will help profile new user-centric visualizations for cloud users

In today's data-driven world, organizations across industries are grappling with vast amounts of data, and the realm of market intelligence is no exception. The explosive growth of big data has created both opportunities and challenges for businesses seeking to gain valuable insights from their data assets. This chapter proposes to explore the significance of big data visualization with key performance indicator (KPI) dashboards as a powerful technique to visualize and extract actionable intelligence from complex datasets in the context of market intelligence. This chapter provides a comprehensive overview of KPI dashboards as these are essential tools for monitoring and analyzing business performance and to make data-driven decisions. The chapter explores the importance of KPIs, the design and implementation of KPI dashboards in big data. Additionally, it discusses the benefits and challenges associated with these tools, along with real-world examples of their successful implementation.

Data Visualization and Visual Analytics (DVVA) techniques help human to extract essential information and knowledge from large and complex data sets, and then represent them clearly and comprehensively in the graphic format for better understanding. These techniques have been successfully applied in many areas, including the application in computer forensic science. This paper discusses the background, approaches to the visual analysis of typical computer forensic data. Primarily we will look at how visualization techniques can enhance the computer forensics task, and proposes a new model with a set of data attributes for visual analytics of computer forensics. © 2013 IEEE

Most advanced text analytics and text mining tasks include text classification, text clustering, building ontology, concept/entity extraction, summarization, deriving patterns within the structured data, production of granular taxonomies, sentiment and emotion analysis, document summarization, entity relation modelling, interpretation of the output. Already existing text analytics and text mining cannot develop text material alternatives (perform a multivariant design), perform multiple criteria analysis, automatically select the most effective variant according to different aspects (citation index of papers (Scopus, ScienceDirect, Google Scholar) and authors (Scopus, ScienceDirect, Google Scholar), Top 25 papers, impact factor of journals, supporting phrases, document name and contents, density of keywords), calculate utility degree and market value. However, the Text Analytics for Android Project can perform the aforementioned functions. To the best of the knowledge herein, these functions have not been previously implemented; thus this is the first attempt to do so. The Text Analytics for Android Project is briefly described in this article

We introduce a framework and class library (GAV Flash) implemented in Adobe’s ActionScript, designed with the intention to significantly shorten the time and effort needed to develop customized web-enabled applications for visual analytics or geovisual analytics tasks. Through an atomic layered component architecture, GAV Flash provides a collection of common geo- and information visualization representations extended with motion behavior including scatter matrix, extended parallel coordinates, table lens, choropleth map and treemap, integrated in a multiple, time-linked layout. Versatile interaction methods are drawn from many data visualization research areas and optimized for dynamic web visualization of spatio-temporal and multivariate data. Based on layered component thinking and the use of programming interface mechanism the GAV Flash architecture is open and facilitates the creation of new or improved versions of existing components so that ideas can be tried out or optimized rapidly in a fully functional environment. Following the Visual Analytics mantra, a mechanism “snapshot” for saving the explorative results of a reasoning process is developed that aids collaboration and publication of gained insight and knowledge embedded as dynamic visualizations in blogs or web pages with associative metadata or “storytelling”

In recent times, big data analysis is gaining immense credence in the fields of academics and business. Businesses similar as operation and marketing have demonstrated a strong inclination and interest in data analytics. Still, numerous businesses are unfit to use data indeed if they have access to it. The main reason for that's the lack of familiarity with data analytics procedures. Hence, a system needs to be developed that can perform data analytics and demonstrate its benefits. In this study, we use point of deals data attained from a supermarket chain to dissect and show the relationship between purchase goods at the same time. A supermarket is one of the ideal places to demonstrate data analysis because retail stores have numerous purchase records and are always conducting colorful marketing conditioning. We propose an easy- to- handle visualization system to show the goods that are inter-related. By using our system, a store director can gain information about the item deals fluently and interactively.

International audienceIn the context of data visualization and analytics, this report outlines some of the challenges and emerging applications that arise in the Big Data era. In particularly, fourteen distinguished scientists from academia and industry, and diverse related communities, i.e., Information Visualization, Human-Computer Interaction, Machine Learning, Data management & Mining, and Computer Graphics have been invited to express their opinions

This chapter explores the role of advanced data visualization techniques in predictive analytics and business intelligence. It highlights the challenges businesses face when using data for strategic insights and predictive modeling, such as scalability constraints, integration issues with machine learning algorithms, and the need for user-friendly interfaces. The chapter also addresses the complexity of multidimensional data, limitations of traditional visualization tools, and the challenge of translating predictive models into actionable insights. The methodology combines interactive visualization techniques with machine learning algorithms to create dynamic data representations. The chapter concludes by discussing the importance of these techniques in data decision-making.

In this dissertation, we discuss analysis and visualization of performance anxiety in tennis matches along with confidence and momentum. We also discuss the micro-level analysis and visualization of tennis shot patterns with fractal tables and tactical rings, followed by discussion about mapping a tennis player's style of play with a visual analysis technique called tennis fingerprinting. According to sports psychology, anxiety, confidence and momentum has a big impact on an athlete's performance in a sport event. Although much work has been done in sports data analysis and visualization, analysis of anxiety, confidence and momentum has rarely been included in recent literature. We propose a method to analyze a tennis player's anxiety level, confidence and momentum levels during a tennis match. This method is based on the psychological theories of anxiety and a database of over 4,000 professional tennis matches. Since sports data analysis and visualization can be a useful tool for gaining insights into the games, we present new techniques to analyze and visualize the shot patterns in tennis matches via our Fractal Tables and Tactical Rings. Tennis is a complicated game that involves a rich set of tactics and strategies. The current tennis analysis are usually conducted at a high level, which often fail to show the useful patterns and nuances embedded in low level data. However, based on a very detailed database of professional tennis matches, we have developed a system to analyze the serve and shot patterns so that an user can explore questions such as "What are the favorite patterns of this player? What are the most effective patterns for this player?" This can help tennis experts and fans gain a deeper insight and appreciation of the sport that are not usually obvious just by watching the match. Further, we present a new visual analytics technique called Tennis Fingerprinting to analyze tennis players' tactical patterns and styles of play. In tennis, style is a complicated and often abstract concept that cannot be easily described or analyzed. The proposed visualization method is an attempt to provide a concrete and visual representation of a tennis player's style.Doctor of Philosophy (PhD)Computer Scienc

This paper describes our latest research in data analytics and visualization for bioinformatics and healthcare. Each year many patients have suffered cancers. Analytics and visualization can help to simulate the development of malignant tumors and help identify weak spots of tumor for treatment, inspect malignant tumors in general and inspect whether genes have cancerous cells. Related literature, technologies, simulation results with explanation, performance evaluation and comparisons with other work have been discussed in details. We can process training data with a low completion time to achieve simulations of malignant tumors and genes to inspect their status, as well as the querying the output data within seconds. Our malignant tumor and gene simulation can achieve 360 degrees for an inspection of cancerous presence. We conclude that data analytics and visualization can provide effective and efficient healthcare research and also other type of interdisciplinary research