Big Data Visualization Tools

Data visualization is the presentation of data in a pictorial or graphical format, and a data visualization tool is the software that generates this presentation. Data visualization provides users with intuitive means to interactively explore and analyze data, enabling them to effectively identify interesting patterns, infer correlations and causalities, and supports sense-making activities.Comment: This article appears in Encyclopedia of Big Data Technologies, Springer, 201

Exploring narrativity in data visualization in journalism

Many news stories are based on data visualization, and storytelling with data has become a buzzword in journalism. But what exactly does storytelling with data mean? When does a data visualization tell a story? And what are narrative constituents in data visualization? This chapter first defines the key terms in this context: story, narrative, narrativity, showing and telling. Then, it sheds light on the various forms of narrativity in data visualization and, based on a corpus analysis of 73 data visualizations, describes the basic visual elements that constitute narrativity: the instance of a narrator, sequentiality, temporal dimension, and tellability. The paper concludes that understanding how data are transformed into visual stories is key to understanding how facts are shaped and communicated in society

Dynamic 3D Network Data Visualization

Monitoring network traffic has always been an arduous and tedious task because of the complexity and sheer volume of network data that is being consistently generated. In addition, network growth and new technologies are rapidly increasing these levels of complexity and volume. An effective technique in understanding and managing a large dataset, such as network traffic, is data visualization. There are several tools that attempt to turn network traffic into visual stimuli. Many of these do so in 2D space and those that are 3D lack the ability to display network patterns effectively. Existing 3D network visualization tools lack user interaction, dynamic generation, and intuitiveness. This project proposes a user-friendly 3D network visualization application that creates both dynamic and interactive visuals. This application was built using the Bablyon.js graphics framework and uses anonymized data collected from a campus network

Data visualization within urban models

Models of urban environments have many uses for town planning, pre-visualization of new building work and utility service planning. Many of these models are three-dimensional, and increasingly there is a move towards real-time presentation of such large models. In this paper we present an algorithm for generating consistent 3D models from a combination of data sources, including Ordnance Survey ground plans, aerial photography and laser height data. Although there have been several demonstrations of automatic generation of building models from 2D vector map data, in this paper we present a very robust solution that generates models that are suitable for real-time presentation. We then demonstrate a novel pollution visualization that uses these models

ViBe (Virtual Berlin) - Immersive Interactive 3D Urban Data Visualization - Immersive interactive 3D urban data visualization

The project investigates the possibility of visualizing open source data in a 3D interactive virtual environment. We propose a new tool, 'ViBe'. We programmed 'ViBe' using Unity for its compatibility with HTC VIVE glasses for virtual reality (VR). ViBe offers an abstract visualization of open source data in a 3D interactive environment. The ViBe environment entails three main topics a) inhabitants, b) environmental factors, and c) land-use; acting as representatives of parameters for cities and urban design. Berlin serves as a case study. The data sets used are divided according to Berlin's twelve administrative districts. The user immerses into the virtual environment where they can choose, using the HTC Vive controllers, which district (or Berlin as a whole) they want information for and which topics they want to be visualized, and they can also teleport back and forth between the different districts. The goal of this project is to represent different urban parameters an abstract simulation where we correlate the corresponding data sets. By experiencing the city through visualized data, ViBe aims to provide the user with a clearer perspective onto the city and the relationship between its urban parameters. ViBe is designed for adults and kids, urban planners, politicians and real estate developers alike

Topic Grids for Homogeneous Data Visualization

We propose the topic grids to detect anomaly and analyze the behavior based on the access log content. Content-based behavioral risk is quantified in the high dimensional space where the topics are generated from the log. The topics are being projected homogeneously into a space that is perception- and interaction-friendly to the human experts

Interactive Data Visualization using Mondrian

This paper presents the Mondrian data visualization software. In addition to standard plots like histograms, barcharts, scatterplots or maps, Mondrian offers advanced plots for high dimensional categorical (mosaic plots) and continuous data (parallel coordinates). All plots are linked and offer various interaction techniques. A special focus is on the seamless integration of categorical data. Unique is Mondrian's special selection technique, which allows advanced selections in complex data sets. Besides loading data from local (ASCII) files it can connect to databases, avoiding a local copy of the data on the client machine. Mondrian is written in 100% pure JAVA.

Computational Statistics and Data Visualization

This book is the third volume of the Handbook of Computational Statistics and covers the field of Data Visualization. In line with the companion volumes, it contains a collection of chapters by experts in the field to present readers with an up-to-date and comprehensive overview of the state of the art. Data Visualization is an active area of application and research and this is a good time to gather together a summary of current knowledge. Graphic displays are often very effective at communicating information. They are also very often not effective at communicating information. Two important reasons for this state of affairs are that graphics can be produced with a few clicks of the mouse without any thought, and that the design of graphics is not taken seriously in many scientific textbooks. Some people seem to think that preparing good graphics is just a matter of common sense (in which case their common sense cannot be in good shape) and others believe that preparing graphics is a low-level task, not appropriate for scientific attention. This volume of the Handbook of Computational Statistics takes graphics for Data Visualization seriously.Data Visualization, Exploratory Graphics.

GenomeGraphs: integrated genomic data visualization with R.

BackgroundBiological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses.ResultsWe developed GenomeGraphs, as an add-on software package for the statistical programming environment R, to facilitate integrated visualization of genomic datasets. GenomeGraphs uses the biomaRt package to perform on-line annotation queries to Ensembl and translates these to gene/transcript structures in viewports of the grid graphics package. This allows genomic annotation to be plotted together with experimental data. GenomeGraphs can also be used to plot custom annotation tracks in combination with different experimental data types together in one plot using the same genomic coordinate system.ConclusionGenomeGraphs is a flexible and extensible software package which can be used to visualize a multitude of genomic datasets within the statistical programming environment R

Using data visualization to deduce faces expressions

Conferência Internacional, realizada na Turquia, de 6-8 de setembro de 2018.Collect and examine in real time multi modal sensor data of a human face, is an important problem in computer vision, with applications in medical and monitoring analysis, entertainment and security. Although its advances, there are still many open issues in terms of the identification of the facial expression. Different algorithms and approaches have been developed to find out patterns and characteristics that can help the automatic expression identification. One way to study data is through data visualizations. Data visualization turns numbers and letters into aesthetically pleasing visuals, making it easy to recognize patterns and find exceptions. In this article, we use information visualization as a tool to analyse data points and find out possible existing patterns in four different facial expressions.info:eu-repo/semantics/publishedVersio