Visualization Of Large Data Sets Research Articles

Towards Better Utilization of Haptic Interaction in Visualization: Design Space and Knob Prototype

Humans encounter a vast array of sensory stimuli in their everyday lives. However, many visualization techniques primarily utilize visual feedback, which may disregard certain intricate details. Relying on a single visual channel may overlook complex layouts. However, how haptic force feedback can be used to assist visualization remained under-explored. In this work, we initially conducted a literature review to identify potential problems in the visualization of large datasets and engaged in discussions with domain experts to explore the potential of haptic force feedback and visual collision representation. Subsequently, we designed an innovative haptic force feedback knob, which included 3 primary modules and 29 elements. To evaluate the clarity and usefulness of this design space, we conducted a workshop and devised “recommended solutions” for the identified visualization problems. Finally, we implemented a prototype of the haptic force feedback knob and assessed its performance on scatterplot and parallel coordinate plot tasks using large datasets. The results indicated that the knob prototype could reduce visual strain and enhance the efficiency of visualization tasks.

Nuevo aporte al conocimiento sobre la redacción del control δ de las ánforas dressel 20 del Monte Testaccio en la modificación antoniniana: el refrendo en δ de la cifra escrita en la posición α

Within the complex system that once formed part of the epigraphic ensemble that accompanied these amphorae, we highlight the particular study of the painted marks known as tituli picti in the delta position of the olive oil amphorae from Baetica, Dressel 20. Here we address a singular fact, only detectable through the visualization of large data sets, indicating the existence of the endorsement in delta of the amount expressed in alpha throughout the years of the Antonine reform. If until now it was believed to be something fortuitous, or a scribeʼs error, now it could be considered, due to its structure and the cases exposed, a regulated script.

Artificial Intelligence and Dimensionality Reduction: Tools for Approaching Future Communications

This article presents a novel application of the t-distributed Stochastic Neighbor Embedding (t-SNE) clustering algorithm to the telecommunication field. t-SNE is a dimensionality reduction (DR) algorithm that allows the visualization of large dataset into a 2D plot. We present the applicability of this algorithm in a communication channel dataset formed by several scenarios (anechoic, reverberation, indoor and outdoor), and by using six channel features. Applying this artificial intelligence (AI) technique, we are able to separate different environments into several clusters allowing a clear visualization of the scenarios. Throughout the article, it is proved that t-SNE has the ability to cluster into several subclasses, obtaining internal classifications within the scenarios themselves. t-SNE comparison with different dimensionality reduction techniques (PCA, Isomap) is also provided throughout the paper. Furthermore, post-processing techniques are used to modify communication scenarios, recreating a real communication scenario from measurements acquired in an anechoic chamber. The dimensionality reduction and classification by using t-SNE and Variational AutoEncoders (VAE) show good performance distinguishing between the recreation and the real communication scenario. The combination of these two techniques opens up the possibility for new scenario recreations for future mobile communications. This work shows the potential of AI as a powerful tool for clustering, classification and generation of new 5G propagation scenarios.

Data Journalism as an innovation in social communication: The case in sports industry

Data analysis has always been an integral part of journalism but combining it with technology was a novelty for newspapers. Journalism’s combination with technology was an innovation because of processing, interpretation, and visualization of large datasets in a journalistic content. In recent years, newspapers have started to adapt data journalism and integrated it to sports for better storytelling and making sports more understandable for readers. This research aims to analyse sports data journalism practices in Spain with a quantitative approach with content analysis of 1068 data journalism articles published by 6 newspapers (Marca, Mundo Deportivo, AS, El Mundo, El Periódico, El Pais) between 2017-2019. Quantitative analysis focuses on how sports data journalism is being adapted in Spain, technical features of articles, and the similarities and differences between sports and national newspapers to identify integration of sports data journalism.

ReVisE: Remote visualization environment for large numerical simulation datasets.

The paper presents a new open-source visualization system, named ReVisE, aimed to provide interactive visualization of large datasets, which are results of complex numerical simulations. These datasets are hosted on a remote server or a supercomputer. The design of the system is briefly described. Dataset representation, proposed for interactive visualization and implemented in the system, is discussed. The effectiveness of our approach is confirmed by results of performance measurements on test and real-life large datasets. A comparison with other visualization systems is presented. Future plans of system development are outlined.

DEVILS: a tool for the visualization of large datasets with a high dynamic range.

The number of grey values that can be displayed on monitors and be processed by the human eye is smaller than the dynamic range of image-based sensors. This makes the visualization of such data a challenge, especially with specimens where small dim structures are equally important as large bright ones, or whenever variations in intensity, such as non-homogeneous staining efficiencies or light depth penetration, becomes an issue. While simple intensity display mappings are easily possible, these fail to provide a one-shot observation that can display objects of varying intensities. In order to facilitate the visualization-based analysis of large volumetric datasets, we developed an easy-to-use ImageJ plugin enabling the compressed display of features within several magnitudes of intensities. The Display Enhancement for Visual Inspection of Large Stacks plugin (DEVILS) homogenizes the intensities by using a combination of local and global pixel operations to allow for high and low intensities to be visible simultaneously to the human eye. The plugin is based on a single, intuitively understandable parameter, features a preview mode, and uses parallelization to process multiple image planes. As output, the plugin is capable of producing a BigDataViewer-compatible dataset for fast visualization. We demonstrate the utility of the plugin for large volumetric image data.

Restricted Boltzmann Machine method for dimensionality reduction of large spectroscopic data

Multivariate data obtained using, for instance, Laser-Induced Breakdown Spectroscopy (LIBS), are quite bulky and complex. Advanced processing of spectroscopic data demands a multidisciplinary approach, covering not only modern machine learning tools but also a deep understanding of underlying physical mechanisms. Dimension reduction and visualization of large datasets is a task of significant interest in the spectroscopic data processing. Commonly employed linear techniques (e.g., Principal Component Analysis, PCA) cannot explain the correlations of higher-order which are present in the data. Even more, computational cost and memory limitations become way more relevant considering the size of “modern” LIBS data (millions of high-dimensional spectra). Methods based on Artificial Neural Networks (ANN) seem suitable for this task, and based on their success, they are given considerable attention within the spectroscopic community. We propose a new methodology based on Restricted Boltzmann Machine (ANN method) for dimensionality reduction of spectroscopic data and compare it to standard PCA. As an extension to successful reconstruction, we demonstrate a generation of new (unseen) spectra by the RBM model trained on a large spectroscopic dataset. This data generation is of great use not only for the extending measured datasets but also as a proper training state's confirmation of the model.

Open access resource for cellular-resolution analyses of corticocortical connectivity in the marmoset monkey

Understanding the principles of neuronal connectivity requires tools for efficient quantification and visualization of large datasets. The primate cortex is particularly challenging due to its complex mosaic of areas, which in many cases lack clear boundaries. Here, we introduce a resource that allows exploration of results of 143 retrograde tracer injections in the marmoset neocortex. Data obtained in different animals are registered to a common stereotaxic space using an algorithm guided by expert delineation of histological borders, allowing accurate assignment of connections to areas despite interindividual variability. The resource incorporates tools for analyses relative to cytoarchitectural areas, including statistical properties such as the fraction of labeled neurons and the percentage of supragranular neurons. It also provides purely spatial (parcellation-free) data, based on the stereotaxic coordinates of 2 million labeled neurons. This resource helps bridge the gap between high-density cellular connectivity studies in rodents and imaging-based analyses of human brains.

Preserving the Topology of Self-Organizing Maps for Data Analysis: A Review

In Kohonen’s Self-Organizing Maps (SOM) algorithm, preserving the map structure to represent the real input patterns appears to be a significant process. Misinterpretation of the training samples can lead to failure in identifying the important features that may affect the outcomes generated by the SOM model. This paper presents detail explanation on SOM learning algorithm and its applications. Some issues related to SOM’s architecture are also discussed, namely the formulation of training data from input samples, and the Best Matching Unit (BMU) identification for better visualization of large datasets, and improvement made to the SOM algorithm.

E-Commerce Product Classification using Lexical Based Hybrid Feature Extraction and SVM

At present, online shopping has become a growing process, in which the profit statistics are posted by familiar e-commerce corporations like Amazon, Flipkart, Snapdeal, etc. However, this kind of online shopping unkindly omits the touch and feel of the products that can be used to estimate the product quality as the main factor while buying the commodities from the shops. The estimation of product quality is more significant during the purchasing of online products. Therefore, many opinion mining and sentiment classification methods were introduced to purchase the best products through online shopping. But, these classification methods haven’t attained the effective product classification with best reviews and ratings. In this paper, we propose a hybrid feature extraction method PCA (Principle Component Analysis) and t-SNE (t-Distributed Stochastic Neighbor Embedding ) with SVM (Support Vector Machine) using lexicon-based method to classify and separate the products from the large set of different products depending on their features, best product ratings and positive reviews. In this process, the online products will be isolated and listed according to their high positive reviews. The data preprocessing is applied to the dataset to get the data accuracy before the execution of feature extraction and classification. The dimensionality reduction and best visualization of large data set are executed by applying the PCA and t-SNE method. The sentiments are also been extracted by this hybrid feature extraction method to acquire the best neighboring product ratings. The polarity of words is discovered using a lexical based approach to extract positive reviews for obtaining the best products. Finally, the SVM is exploited to the classification of products. The performance of the proposed method is estimated with precision, recall, accuracy and complexity that can provide the entire accurateness of the system.

MetaUniDec: High-Throughput Deconvolution of Native Mass Spectra.

The expansion of native mass spectrometry (MS) methods for both academic and industrial applications has created a substantial need for analysis of large native MS datasets. Existing software tools are poorly suited for high-throughput deconvolution of native electrospray mass spectra from intact proteins and protein complexes. The UniDec Bayesian deconvolution algorithm is uniquely well suited for high-throughput analysis due to its speed and robustness but was previously tailored towards individual spectra. Here, we optimized UniDec for deconvolution, analysis, and visualization of large data sets. This new module, MetaUniDec, centers around a hierarchical data format 5 (HDF5) format for storing datasets that significantly improves speed, portability, and file size. It also includes code optimizations to improve speed and a new graphical user interface for visualization, interaction, and analysis of data. To demonstrate the utility of MetaUniDec, we applied the software to analyze automated collision voltage ramps with a small bacterial heme protein and large lipoprotein nanodiscs. Upon increasing collisional activation, bacterial heme-nitric oxide/oxygen binding (H-NOX) protein shows a discrete loss of bound heme, and nanodiscs show a continuous loss of lipids and charge. By using MetaUniDec to track changes in peak area or mass as a function of collision voltage, we explore the energetic profile of collisional activation in an ultra-high mass range Orbitrap mass spectrometer. Graphical abstract ᅟ.

Interactive Visualization of Large Data Sets

Visualization provides a powerful means for data analysis. But to be practical, visual analytics tools must support smooth and flexible use of visualizations at a fast rate. This becomes increasingly onerous with the ever-increasing size of real-world datasets. First, large databases make interaction more difficult once query response time exceeds several seconds. Second, any attempt to show all data points will overload the visualization, resulting in chaos that will only confuse the user. Over the last few years, substantial effort has been put into addressing both of these issues and many innovative solutions have been proposed. Indeed, data visualization is a topic that is too large to be addressed in a single survey paper. Thus, we restrict our attention here to interactive visualization of large data sets. Our focus then is skewed in a natural way towards query processing problem—provided by an underlying database system—rather than to the actual data visualization problem.

Towards a comprehensive atlas of cortical connections in a primate brain: Mapping tracer injection studies of the common marmoset into a reference digital template.

The marmoset is an emerging animal model for large‐scale attempts to understand primate brain connectivity, but achieving this aim requires the development and validation of procedures for normalization and integration of results from many neuroanatomical experiments. Here we describe a computational pipeline for coregistration of retrograde tracing data on connections of cortical areas into a 3D marmoset brain template, generated from Nissl‐stained sections. The procedure results in a series of spatial transformations that are applied to the coordinates of labeled neurons in the different cases, bringing them into common stereotaxic space. We applied this procedure to 17 injections, placed in the frontal lobe of nine marmosets as part of earlier studies. Visualizations of cortical patterns of connections revealed by these injections are supplied as Supplementary Materials. Comparison between the results of the automated and human‐based processing of these cases reveals that the centers of injection sites can be reconstructed, on average, to within 0.6 mm of coordinates estimated by an experienced neuroanatomist. Moreover, cell counts obtained in different areas by the automated approach are highly correlated (r = 0.83) with those obtained by an expert, who examined in detail histological sections for each individual. The present procedure enables comparison and visualization of large datasets, which in turn opens the way for integration and analysis of results from many animals. Its versatility, including applicability to archival materials, may reduce the number of additional experiments required to produce the first detailed cortical connectome of a primate brain. J. Comp. Neurol. 524:2161–2181, 2016. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.

Towards a comprehensive atlas of cortical connections in a primate brain: Mapping tracer injection studies of the common marmoset into a reference digital template

The marmoset is an emerging animal model for large‐scale attempts to understand primate brain connectivity, but achieving this aim requires the development and validation of procedures for normalization and integration of results from many neuroanatomical experiments. Here we describe a computational pipeline for coregistration of retrograde tracing data on connections of cortical areas into a 3D marmoset brain template, generated from Nissl‐stained sections. The procedure results in a series of spatial transformations that are applied to the coordinates of labeled neurons in the different cases, bringing them into common stereotaxic space. We applied this procedure to 17 injections, placed in the frontal lobe of nine marmosets as part of earlier studies. Visualizations of cortical patterns of connections revealed by these injections are supplied as Supplementary Materials. Comparison between the results of the automated and human‐based processing of these cases reveals that the centers of injection sites can be reconstructed, on average, to within 0.6 mm of coordinates estimated by an experienced neuroanatomist. Moreover, cell counts obtained in different areas by the automated approach are highly correlated (r = 0.83) with those obtained by an expert, who examined in detail histological sections for each individual. The present procedure enables comparison and visualization of large datasets, which in turn opens the way for integration and analysis of results from many animals. Its versatility, including applicability to archival materials, may reduce the number of additional experiments required to produce the first detailed cortical connectome of a primate brain. J. Comp. Neurol. 524:2161–2181, 2016. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.

GPU-Based Medical Visualization for Large Datasets

GPU-Based Medical Visualization for Large Datasets

One Sinister Hurricane: Simondon and Collaborative Visualization

This article offers a theory and methodology for understanding and interpreting collaborations that involve visualization technologies. The collaboration discussed here is technically a geovisualization—an immersive, digital “fulldome” film of Hurricane Katrina developed by the Advanced Visualization Laboratory (AVL) at the University of Illinois at Urbana–Champaign, produced in collaboration with atmospheric scientists at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado. The project, which brought together AVL's programmers, visualization experts, and artists with NCAR's scientists, required the integration of diverse disciplinary perspectives. In the language of such collaborations, the term renaissance team was coined to capture the collective expertise necessary to produce modern, high-end visualizations of large data sets. In this article, we deploy Simondon's concepts of technical objects and collective individuation to analyze the development of AVL's Katrina simulation. One extended sequence of team member collaboration suggests that technical objects also be treated as “collaborators,” for they have the capacity to transform such collectives through the unique problems they present.

Heuristic Evaluation of Data Integration and Visualization Software Used for Continuous Monitoring to Support Intensive Care: A Bedside Nurses Perspective

The Intensive Care Unit (ICU) is a complex and technologically advanced healthcare setting. Technologies enable continuous monitoring through patient signals that are sensed, recorded and displayed at the bedside. Although such technologies have significantly decreased mortality rates in the ICU, the large amounts of data have contributed to clinician information overload. Critical care nurses spend more than half of their time scanning and assimilating information from disparate monitors, at the bedside to assess the patient status. Software that integrates and allows visualization of large data sets on a single screen are now available. In the present study, we evaluated software entitled T3™ (Tracking, Trajectory and Triggering). Such computationally powerful software has great potential to support nurses’ monitoring and decision-making tasks but the usability, efficiency, and effectiveness of the software are key to end-user adoption. As such, we conducted a Heuristic Evaluation, where the study’s evaluators interacted with the software interfaces and were asked to comment on it by describing the usability issues and if they were in compliance with established usability principles, or heuristics, specifically for medical device interfaces. A total of 50 usability issues associated with 194 heuristic violations were found. Identified issues included difficulty with choosing the time period of the patient data signals, distinguishing between several patient signals and appearance of patient values which were imperceptible to evaluators; both issues could lead nurses to misinterpret the timing and/or the physiological status of the patient (e.g., time of shock and exact value of vitals). Heuristic evaluation, an efficient and inexpensive method, was successfully applied to the T3™ software to identify usability problems that if left unresolved could lead to patient safety issues. These findings may have broad implications for the design of the T3™ and other continuous monitoring systems.

Reflective Healthcare Systems: micro-Cycle of Self-Reflection to empower users

Data collection and reflection are considered an integrated process in Personal Informatics to help users take action towards changing behaviour. Facilitating the collection and visualization of large data sets has been a major technical challenge to guarantee meaningful and effortless information to users. However this focus results in a passive involvement of users in these stages, creating distance between the user and their data, thus hindering proper understanding of people’s current behaviours. Designing for active participation may aid users in forming a closer bond to data. Going beyond the support of visualization of performance data, this paper introduces ESTHER 1.3 as an approach to facilitate active mini cycles of self-reflection (mCR) by means of in-situ self-reporting mechanisms. ESTHER 1.3 is presented as an implementation of this mini cycles in the context of physical activity and knowledge workers. A field study evaluation of 23 days with 5 users shows the opportunities of the mini cycles to engage people in deeper reflection and to support them to perform better-informed actions, as well as the challenges in the implementation of mCR elements for a specific context.

Visualizing in vivo brain neural structures using volume rendered feature spaces

BackgroundDendrites of cortical neurons are widely spread across several layers of the cortex. Recently developed two-photon microscopy systems are capable of visualizing the morphology of neurons within deeper layers of the brain and generate large amounts of volumetric imaging data from living tissue. MethodFor visual exploration of the three-dimensional (3D) structure of dendrites and the connectivity among neurons in the brain, we propose a visualization software and interface for 3D images based on a new transfer function design using volume rendered feature spaces. This software enables the visualization of multidimensional descriptors of shape and texture extracted from imaging data to characterize tissue. It also allows the efficient analysis and visualization of large data sets. ResultsWe apply and demonstrate the software to two-photon microscopy images of a living mouse brain. By applying the developed visualization software and algorithms to two-photon microscope images of the mouse brain, we identified a set of feature values that distinguish characteristic structures such as soma, dendrites and apical dendrites in mouse brain. Also, the visualization interface was compared to conventional 1D/2D transfer function system. ConclusionsWe have developed a visualization tool and interface that can represent 3D feature values as textures and shapes. This visualization system allows the analysis and characterization of the higher-dimensional feature values of living tissues at the micron level and will contribute to new discoveries in basic biology and clinical medicine.

A web-based protein interaction network visualizer.

BackgroundInteraction between proteins is one of the most important mechanisms in the execution of cellular functions. The study of these interactions has provided insight into the functioning of an organism’s processes. As of October 2013, Homo sapiens had over 170000 Protein-Protein interactions (PPI) registered in the Interologous Interaction Database, which is only one of the many public resources where protein interactions can be accessed. These numbers exemplify the volume of data that research on the topic has generated. Visualization of large data sets is a well known strategy to make sense of information, and protein interaction data is no exception. There are several tools that allow the exploration of this data, providing different methods to visualize protein network interactions. However, there is still no native web tool that allows this data to be explored interactively online.ResultsGiven the advances that web technologies have made recently it is time to bring these interactive views to the web to provide an easily accessible forum to visualize PPI. We have created a Web-based Protein Interaction Network Visualizer: PINV, an open source, native web application that facilitates the visualization of protein interactions (http://biosual.cbio.uct.ac.za/pinv.html). We developed PINV as a set of components that follow the protocol defined in BioJS and use the D3 library to create the graphic layouts. We demonstrate the use of PINV with multi-organism interaction networks for a predicted target from Mycobacterium tuberculosis, its interacting partners and its orthologs.ConclusionsThe resultant tool provides an attractive view of complex, fully interactive networks with components that allow the querying, filtering and manipulation of the visible subset. Moreover, as a web resource, PINV simplifies sharing and publishing, activities which are vital in today’s research collaborative environments. The source code is freely available for download at https://github.com/4ndr01d3/biosual.